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ISSN : 0256-6524 VOL. 52, No. 1 January-March 2015 JOURNAL OF AGRICULTURAL ENGINEERING INDIAN SOCIETY OF AGRICULTURAL ENGINEERS ISAE Journal of Agricultural Engineering Editorial Board Chief Editor: Dipankar De (jae.chiefeditor@gmail.com) Farm Machinery and Power Division Processing, Dairy and Food Engineering Division Soil and Water Engineering Division Energy and other Areas Division Debaraj Behera S. N. Jha S. K. Ambast A. K. Kurchania debaraj1963@rediffmail.com fmp.isae@gmail.com snjhajae@gmail.com skambast@cssri.ernet.in skambast65@gmail.com K. Kathirvel Niranjan Prasad R. Subbaiah Pravakar Mohanty G.S.Manes K. Narasaih P.K. Shrivastava N. L. Panwar P.K.Sahoo D. M. Kadam M.J. Kaledhonkar Madhuri Narra M. Mahapatra Pramod Rai Satyendra Kumar R.Mahendiran Editor Associate Editors jae.divenergyeditor@ gmail.com K.V. Rao Publication Enquiries The Journal of Agricultural Engineering, a publication of the Indian Society of Agricultural Engineers, is published in four issues per year. This journal is not responsible for statements and opinions expressed by the authors of papers published in it. Reprints may be made from this publication on the condition that full credit is given to the author(s) and the Journal of Agricultural Engineering. All communications regarding the publication or submission of manuscripts should be addressed to The Chief Editor, Journal of Agricultural Engineering at the address given below. Subscription Rate Annual subscription Rs. 2000/- or US $400 ($ 50 extra for air mail dispatch) Per copy Rs. 600/- or US $ 150 ($ 25 extra for air mail dispatch) Payment are to be made by crossed cheque/ draft including bank charges in favour of “Indian Society of Agricultural Engineers”, payable at New Delhi, India, and directly sent to the Society Headquarters at the address given below: Indian Society of Agricultural Engineers G-4, National Societies Block, National Agricultural Science Centre Complex, Dev Prakash Shastri Marg, Pusa Campus, New Delhi – 110012, India Tel / Fax: 091-11-25849003 E-mail: isae1960@gmail.com Web: www.isae.in Md Shaiq Alam, Harjot Khaira, Shivani Pathania, Sunil Kumar and Baljit Singh JAE : 52 (1) Journal of Agricultural Engineering, Vol. 52 (1): January-March, 2015 Extrusion Process Optimization for Soy-Carrot Pomace Powder Incorporated Wheat-based Snacks Md Shaiq Alam1, Harjot Khaira2, Shivani Pathania3, Sunil Kumar4 and Baljit Singh5 Manuscript received: July, 2014 Revised manuscript accepted: December, 2014 ABSTRACT Twin screw extruder was used to develop soy-carrot pomace incorporated wheat-based nutritionally rich snacks. Different experimental combinations of extrusion process variables as die temperature, screw speed, feed moisture and wheat flour in the feed formulation of wheat, defatted soy flour and carrot pomace powder, were tried using Box-Behnken design of experiment. Response surface methodology (RSM) was used to investigate the effect of die temperature (120-180°C), screw speed (300-500 rpm), feed moisture (14-20%) and wheat flour (65-85%) with soy-carrot pomace blend in equal proportion on product responses like bulk density (BD), water absorption index (WAI), water solubility index (WSI), hardness (H), colour change (CC), and overall acceptability (OA). The extrusion process was optimized for maximum expansion ratio, water absorption index, water solubility index and overall acceptability; and minimum bulk density, hardness and colour change within the experimental range. Analysis of variance (ANOVA) revealed that among the process variables, feed moisture and wheat flour percentage in feed formulation had significantly higher effect on BD, ER, WSI, CC and OA. Die temperature was observed to have significantly lower effect on the selected responses. The optimized extrusion conditions for desired product quality had desirability of 0.729. Key words: Extrusion, snacks, wheat lour, defatted soy lour, carrot pomace powder, RSM Extrusion is an important technology for processing grainbased products and adds value to raw material ranging from corn, wheat and rice to sorghum, oats and soybean. This modern high-temperature short-time process is being adopted to replace traditional food processing techniques (as drum dryer, batch cookers, mixers, formers, stirred tank reactors and ovens) working at low temperatures and pressures, and with long residence times (Lee, 1987). Extrusion is most economical in large scale production (Onyango et al., 2004). It offers numerous advantages including versatility, high productivity, low operating cost, energy efficiency, high quality of resulting products and an improvement in digestibility and biological value of proteins (Mercier and Feillet, 1975; Carrillo et al., 2002). To achieve the desirable changes in the raw material, the most important process parameters during extrusion are feed moisture, screw speed and temperature. wheat can be used as a base to manufacture healthy snacks. The extruded products content could be improved in its overall nutritional content and taste by incorporation of protein rich ingredients. As wheat is deficient in methoinine, the amino acid profile can be improved by supplementation. Incorporation of ingredients with high nutrient density into these snack products could increase their nutritional values. Legume not only holds great promise in meeting the protein needs of poor population, but also contributes in solving some health- related problems (Veronica et al., 2006). Hence, soybean an easily available rich source of protein, could also be used to further nutritionally enrich the food. Incorporation of legume flour has been shown to cause a positive impact on levels of proteins and dietary fibre of extruded snacks (Berrios, 2006). The pulses in the extruded product can pave the way for a snack which is rich in both fibre and protein contents. With changes in formulation and control of extrusion parameters, an acceptable expanded ready-to-eat snack could be produced (Stojceska et al., 2008b). Offering complete nutrition is an important asset of any food. Generally, rice and corn are used to prepare extruded products due to their bland taste, hypo-allergicity and good puffing characteristics. However, wheat is the major food crop next to rice in the Indian sub-continent. A large section of Indian population is adapted to the taste of wheat. Hence, Carrot pomace can be used to develop products that could supplement fibre in the diet of people. During carrot juice 1 Research Engineer, Department of Processing and Food Engineering, Punjab Agricultural University, Ludhiana, Punjab, India, ms_alam@ rediffmail.com, 2,3,4Senior Research Fellow, Department of Processing and Food Engineering, Punjab Agricultural University, Ludhiana, Punjab, India, 5Baking Technologist, Department of Food Science and Technology, Punjab Agricultural University, Ludhiana, Punjab, India 1 January-March, 2015 Extrusion Process Optimization for Soy-Carrot Pomace Powder incorporated Wheat-based Snacks and water solubility index, hardness, colour change and overall acceptability. extraction, up to 80% of carotene may be lost with left over carrot pomace. It has good residual amount of all the vitamins, minerals and dietary fibre. A promising way is to store the carrot pomace in dried form and utilize in development of bakery products, specifically extrudates, which are presently becoming more popular than other bakery products in ready-to-eat food category. Dried carrot pomace has carotene and ascorbic acid in the range of 9.87 to 11.57 mg and 13.53 to 22.95 mg per 100 g, respectively (Upadhyay et al., 2008). Sample Preparation The formulations of ingredients for the preparation of extruded products are given in Table 1. Carrot pomace powder and defatted soy flour were mixed in equal proportion in a food processor with mixture attachment. Wheat flour (W) was substituted with defatted soy flour (S) and carrot pomace powder (C) at levels of 15%, 25% and 35 per cent. Sugar @10 % was added in every sample to enhance the flavour of the snacks. After mixing, samples were packed in polyethylene bags at refrigerated temperature for 24 h (Stojeska et al., 2008). Incorporation of by-products from the food industry using extrusion technology in order to improve the nutritional characteristics of ready-to-eat snacks is very well documented (Stojceska et al., 2008). An attempt was made to prepare a nutritious extruded snack using wheat flour, carrot pomace powder and defatted soybean flour. Process parameters were optimized using response surface methodology techniques. Extrudates Preparation A laboratory-scale, co-rotating twin-screw extruder with intermeshing (Model BC2; Clextral, Firminy Cedex, France), was used for the extrusion study. The barrel diameter and its length-to-diameter (L/D) were 25 mm and 16:1, respectively. The extruder had 4 barrel zones. Temperature of the first, second and third zone were maintained at 40, 70 and 100°C, respectively, throughout the experiment. The temperature at the last zone (compression and die section) was varied according to the experimental design. The diameter of die opening was 6 mm. The extruder was powered by 8.5 KW motor with speed variable from 0 to 682 rpm. The screw configuration is shown in Table 2 and Fig 1. The extruder was calibrated with respect to the combination of feed rate and screw speed to be used. The feed rate was varied for optimum filling of the extruder barrel corresponding to the screw speed. The moisture content of feed was varied by injecting water into the extruder with water pump. A variable speed die face cutter with four bladed knives was used to cut the extrudates. The temperature of the twin-screw extruder was stabilized to set values. Samples were then poured into the feed hopper and the feed rate was adjusted as per experimental requirements. The product was collected at the die end and kept at 60 ± 0.5°C in hot air oven for 1 h duration to remove extra moisture from the product. The samples were packed in polythene bags for further analysis. MATERIALS AND METHODS Experimental Design Response surface methodology (RSM) was used for the design of experimental combinations (Ding et al., 2005; Altan et al., 2008; Yagci and Gogus, 2008). The main advantage of RSM is the reduced number of experimental runs needed to provide sufficient information for statistically acceptable results. A four-factor three-level central composite experimental design was employed. The parameters and their levels were chosen based on the literature available on wheat-based extrudates (Ding et al., 2006; Upadhyay et al., 2008; Yagci and Gogus, 2008; Ibanoglu et al., 2006). Carrot based ready-to-eat snack was developed with wheat flour; pulse flour (defatted soybean flour) and carrot pomace powder as ingredients. The independent variables included the proportion of defatted soy-flour and carrot pomace powder in wheat flour (15-35%), die temperature (120-180 oC), screw speed (300-500 rpm) and moisture content (14-20%). Response variables were specific mechanical energy, bulk density, expansion ratio, water absorption index Table1. Ingredient formulations of snack Feed Ingredient Feed formulation (Wheat lour: Defatted soy lour: Carrot pomace powder) 65:17.5:17.5 75:12.5:12.5 85:7.5:7.5 Wheat flour, g 195 225 255 Defatted soy flour, g 52.5 37.5 22.5 Carrot pomace powder, g 52.5 37.5 22.5 Total, g 300 300 300 2 Md Shaiq Alam, Harjot Khaira, Shivani Pathania, Sunil Kumar and Baljit Singh JAE : 52 (1) Table 2. Screw coniguration in different sections of the extruder (From hopper to die) Screw section 1 2 3 4 5 6 7 8 9 10 Screw element BAGUE C2F C2F C2F C2F C2F INO 0 C1F CF1C C1F 20 50 50 50 50 50 5 50 25 50 - 50 33.33 25 25 16.66 - 16.66 12.5 12.5 Length, mm Pitch, mm Fig. 1: Screw proile Product Quality Responses Water solubility index Bulk density The supernatant, as above, was evaporated to dryness at 105°C until constant weight was obtained. Water solubility index (WSI) was determined as (Anderson et al., 1969): Bulk density (BD, g.cm-3) of extrudates was determined by using average diameter and an average length of extrudate samples and calculated using the following expression (Stojceska et al., 2008): … (4) …(1) Hardness Where, m is mass in g; L is length in cm and d is diameter in cm of extrudates. Mechanical properties of the extrudates were determined by crushing method using a TA-XT2i (Stable Micro-Systems, Surrey, England) with p75 compression plate. The tests were conducted at pre–test speed of 1.0 mm.s-1, test speed of 5 mm.s-1, post-test speed of 5 mm.s-1, strain- 25%, trigger force of 0.4903 N and load cell of 50 kg. Expansion ratio The ratio of diameter of the extrudate and the diameter of die was used to express the expansion of extrudate (Fan et al., 1996). The diameter of extrudate was determined as the mean of 10 random measurements made with a Vernier caliper. The extrudate expansion ratio was calculated as: An extrudate was compressed with a probe SMS-P/75-75 mm diameter at a crosshead speed 5 mm.s-1 to 3 mm (50% of diameter) of the extrudate. The compression generated a curve with the force over distance. The highest first peak value was recorded, as this value indicated the first rupture of snack at one point, and taken as a measurement for hardness (Stojceska et al., 2008). … (2) Water absorption index A 2.0 g ± 0.005 g sample was placed in a tared centrifuge tube, and 20 ml distilled water was added to it. After 15 min (with intermittent shaking every 5 min), the sample was centrifuged at 4000 rpm for 15 min. The supernatant was decanted into a tared aluminum pan and weight gain in the gel was noted. Water absorption index (WAI) was calculated as the increase in weight of sediment obtained after decanting the supernatant as: Colour change Colour is one the important parameter for acceptability of a product. Colour properties of the samples were measured by using Miniscan XE plus Hunter Lab Colourimeter (USA), Model No. 45/0-L. Colour of ground samples was measured in terms of ‘L’, ‘a’ and ‘b’ value. Ground sample was completely filled in a petri-dish, and no light was allowed to pass during the measuring process. The colour change was … (3) 3 January-March, 2015 Extrusion Process Optimization for Soy-Carrot Pomace Powder incorporated Wheat-based Snacks calculated from the ‘L’, ‘a’ and ‘b’ readings (Gnanasekharan et al., 1992) as following: Colour change = √[(L-L0)2 + (a-a0) 2 + (b-b0)2] … (5) High density product is an indication of more uniform and continuous protein matrix, and therefore, extrudate was dense with parallel layers and no air pockets. High density of the product might have been due to less generation of heat with low temperature process, with avoidance of moisture flash-off from the product. This could have been due to carrot pomace powder, a good source of fibre, which led to less expansion resulting in a high density product with collapsed cells usually disintegrating on cooling. Where, L0, a0 and b0 represent the respective values for fresh sample. Sensory evaluation Sensory quality of extrudates was determined with the help of 10 semi-trained consumer panellist, using a 9-point Hedonic scale (9-liked extremely to 1-disliked extremely) method described by Amerine et al. (1965). The aspects considered were colour, appearance, taste, flavour and overall acceptability (OA). The decrease in density with screw speed could be due to gelatinization of starch at higher speeds. Also, the high dependence of density on feed moisture would reflect its influence on elasticity characteristics of the starch- based material. Increased feed moisture content during extrusion might reduce the elasticity of the dough through plasticization of the melt, resulting in reduced gelatinization and increased density of extrudate (Mercier and Feillet, 1975). Identical results were shown by other researchers for extrusion of chick pea flour (Meng et al., 2010), for corn starch, amaranth and rice (Hagenimana et al., 2006). RESULTS AND DISCUSSION Values of physical properties of extrudates are presented in Table 3. Final equations of the fitted models for the selected parameters are depicted in Table 5 along with respective R2 values. The regression models for bulk density, expansion ratio, WAI, WSI, hardness, colour change and overall acceptability were highly significant (P < 0.0001), with a high correlation coefficient (R2 = 0.76, 0.78, 0.82, 0.78, 0.80, 0.84 and 0.90 respectively). None of the models showed significant lack of fit (P > 0.01), indicating that all the second-order polynomial models correlated well with the measured data. All the parameters showed high adequate precision. Expansion Ratio Expansion ratio indicates the extent of puffing of extruded products. The experimental results of expansion ratio of extrudates under different designed extrusion conditions are shown in Table 3. Expansion ratio of extrudates varied between 1.92 and 3.07 for different combinations of extrusion process variables. The highest expansion ratio was 3.07 for extrusion processing at 150oC, 400 rpm screw speed, 14% moisture content and 85% wheat flour in the composition (7.5% carrot pomace powder). On the other hand, the lowest expansion ratio of 1.92 was obtained at 180oC, 400 rpm screw speed, 20% moisture content and 75% wheat flour in the composition (12.5% carrot pomace powder). The linear terms of die temperature, feed moisture and wheat flour in the total composition had significant effects on expansion ratio (p<0.01). Expansion ratio of extrudates increased with an increase in screw speed and wheat flour in the composition, whereas a negative relationship was observed when die temperature and moisture content decreased. Carrot pomace powder had negative impact on the expansion ratio of the extrudates. The regression equation for the relationship between expansion ratio and independent variables in term of the coded variables is shown in Table 5. Bulk Density Table 4 shows the effect of independent variables on bulk density of the extrudates. Bulk density of extrudates ranged between 0.166 and 0.485 g.cm-3 (Table 3). It was apparent that linear terms of feed moisture (p<0.01) and wheat flour in the composition (p<0.05) had significant effect on bulk density of the extrudates (Table 4). Bulk density was highly influenced by moisture content, followed by percentage of wheat flour in the total composition. It is evident from Table 4 that screw speed and wheat flour in the composition had negative correlation with bulk density. However, die temperature and moisture content had positive correlation with bulk density. The quadratic model obtained from regression analysis for density (D) in terms of coded levels of the variables is shown in Table 5. The significance of coefficient of fitted quadratic model was evaluated by using F- test and P- value. The value of R2 was found to be 0.76. Product terms and quadratic terms had insignificant effect on bulk density. 4 Md Shaiq Alam, Harjot Khaira, Shivani Pathania, Sunil Kumar and Baljit Singh JAE : 52 (1) Table 3. Experimental data of extrudates for response surface analysis using four-factor three-level Box-Behnken design Extrusion process variable Die temp. Product quality response (oC) Screw speed (rpm) Feed moisture (%) Wheat lour (%) Bulk density (g.cm-3) Expansion ratio WAI WSI Hardness Colour change Overall acceptability (g.g-1) (%) (N) 150(0) 500(+1) 17(0) 65 0.311 2.50 3.88 23.7 72.48 9.35 6.48 120(-1) 500(+1) 17(0) 75 150(0) 400(0) 20(+1) 85 0.301 2.63 4.01 22.09 0.301 2.60 4.41 18.4 41.39 9.81 6.94 45.42 10.69 6.55 180(+1) 400(0) 14(-1) 75 0.251 2.37 4.30 20.2 31.27 10.89 6.94 150(0) 300(-1) 14(-1) 75 0.254 2.49 4.19 20.2 45.41 11.23 6.52 150(0) 400(0) 17(0) 75 0.257 2.66 4.27 23 40.01 10.77 6.64 150(0) 500(+1) 17(0) 85 0.272 2.77 4.22 20.4 50.94 7.48 6.85 180(+1) 500(+1) 17(0) 180(+1) 400(0) 20(+1) 75 0.311 2.37 4.18 19 67.11 9.13 6.73 75 0.390 1.92 4.31 19.12 58.61 9.43 5.52 150(0) 300(-1) 17(0) 85 0.245 2.71 4.58 20.4 38.16 13.15 7.03 120(-1) 400(0) 17(0) 65 0.296 2.44 3.99 19.6 51.15 8.77 6.12 150(0) 400(0) 20(+1) 65 0.421 2.32 3.99 21.6 76.20 9.59 5.30 150(0) 400(0) 17(0) 75 0.356 2.49 4.32 18.16 38.58 8.70 5.94 120(-1) 400(0) 14(-1) 75 0.172 2.83 4.13 22.8 50.23 14.17 7.24 150(0) 500(+1) 14(-1) 75 0.175 2.66 4.18 28.82 52.00 11.49 7.39 180(+1) 400(0) 17(0) 65 0.405 2.22 4.15 20.4 47.60 10.16 5.97 120(-1) 400(0) 17(0) 85 0.279 2.72 4.47 21.6 37.45 10.95 6.64 150(0) 300(-1) 20(+1) 75 0.485 2.14 4.05 24.2 48.63 11.34 5.18 150(0) 400(0) 14(-1) 85 0.166 3.07 4.46 22.8 51.02 11.69 7.06 150(0) 400(0) 17(0) 75 0.359 2.48 4.55 19.2 53.95 7.63 5.91 120(-1) 400(0) 20(+1) 75 0.403 2.46 3.95 20 40.56 8.50 5.36 150(0) 400(0) 14(-1) 65 0.228 2.53 4.09 24.98 51.61 11.92 6.55 180(+1) 300(-1) 17(0) 75 0.271 2.30 4.14 23.2 47.00 12.71 6.52 150(0) 500(+1) 20(+1) 75 0.327 2.44 4.12 19.11 52.99 6.47 5.82 180(+1) 400(0) 17(0) 85 0.271 2.61 4.59 19.2 51.37 8.87 7.09 150(0) 300(-1) 17(0) 65 0.327 2.36 4.27 24.8 56.91 7.73 5.79 120(-1) 300(-1) 17(0) 75 0.224 2.68 4.36 23.12 40.83 12.47 6.52 Table 4. Analysis of variance of extrusion process variables on the selected responses Responses (F- value) Extrusion process variable Bulk Density (g.cm-3) Expansion Ratio Die temperature 1.72 (A) WAI WSI Hardness Colour Change Overall Acceptability 3.11 0.82 0.003733 (0.1996) (0.1034) (0.3842) (0.9523) 0.22 6.54 15.08 7.23 (0.0377) (0.6469) (0.0252) (0.0022) (0.0197) 1.46 8.5 3.04 16.05 64.56 (0.2502) (0.013) (0.1069) (0.0017) (< 0.0001) -1 (g.g ) (%) (N) 24.7 3.24 1.84 (0.2137) (0.0003) (0.0969) 0.41 3.21 5.46 (0.5329) (0.0983) 40.21 27.57 (< 0.0001) (0.0002) Screw speed (B) Feed moisture (0C) 5 January-March, 2015 Extrusion Process Optimization for Soy-Carrot Pomace Powder incorporated Wheat-based Snacks 7.05 28.67 30.39 4.25 12.1 1.92 25.41 (0.021) (0.0002) (0.0001) (0.0617) (0.0046) (0.1912) (0.0003) 0.14 0.29 2.56 0.85 2.08 0.17 0.14 (0.7191) (0.6013) (0.1355) (0.375) (0.1745) (0.6856) (0.7175) Wheat flour (D) A*B 0.87 0.15 0.55 0.25 7.47 3.6 0.63 (0.3686) (0.7076) (0.474) (0.6262) (0.0182) (0.082) (0.4428) A*C 1.41 0.22 0.032 0.87 1.66 2.45 1.12 (0.2583) (0.6456) (0.8611) (0.3706) (0.2214) (0.1434) (0.3108) 0.63 0.36 0.1 15.88 0.027 5.34 0.18 (0.4433) (0.5621) (0.7568) (0.0018) (0.8714) (0.0393) (0.6795) 0.19 0.15 0.007986 0.1 0.042 10.8 2.35 (0.6698) (0.7064) (0.9303) (0.7547) (0.8404) (0.0065) (0.1508) 0.35 1.21 0.033 0.088 4.97 0.36 1.61 (0.5661) (0.2931) (0.8586) (0.7719) (0.0456) (0.5574) (0.2282) 0.23 2.87 2.03 0.32 0.37 3.36 2.49 (0.6408) (0.1162) (0.1797) (0.5792) (0.5543) (0.0915) (0.1404) 0.82 0.2 5.63 6.6 2.23 0.86 3.1 (0.382) (0.6665) (0.0353) (0.0246) (0.1612) (0.3727) (0.1035) A*D B*C B*D C*D A2 B2 0.24 0.98 5.6 2.04 1.15 5.04 0.35 (0.6338) (0.3424) (0.0357) (0.1791) (0.3053) (0.0444) (0.5657) C2 0.63 2.5 0.04 0.49 4.81 0.16 2.03 (0.4433) (0.1398) (0.8457) (0.4991) (0.0487) (0.6929) (0.1794) CV (%) 16.49 4.54 2.91 8.01 13.65 10.87 4.48 S.D. ( 5% LSD) 0.049 0.11 0.12 1.72 6.77 1.11 0.29 D2 Data in parenthesis are p-values Table 5. Adequacy of model itted Fitted model R2 P value Bulk density (g.cm-3) BD = +0 .32+ 0.019 *A- 9.121 x 10-3 *B+ 0.090 *C- 0.038 *D- 9.063 x 10-3 *AB - 0.023 *AC - 0.020 *BC + 0.011 *BD -0.015 *CD - 0.010 *A2 - 0.019 *B2 - 0.010 *C2 - 0.017*D2 0.82 0.0121 Expansion ratio ER= + 2.54 - 0.16 *A + 0.059 *B -0.17 *C + 0.18 *D + 0.031 *AB - 0.022 *AC + 0.027 *AD + 0.034 *BC – 0.022 *BD - 0.063 *CD - 0.084 *A2 - 0.022 *B2 - 0.049 *C2 + 0.078 *D2 0.89 0.001 Water absorption index (g.g-1) WAI= + 4.38 + 0.064 *A - 0.083 *B -0.043 *C + 0.20 *D + 0.099 *AB + 0.046 *AC - 0.011 *AD + 0.020 *BC + 5.500 x 10-3 *BD + 0.011 *CD - 0.076 *A2 - 0.13 *B2 - 0.13 *C2 - 0.011*D2 0.82 0.0125 Water solubility index (%) WSI= + 20.12 - 0.67 *A - 0.23 *B - 1.45 *C - 1.02 *D - 0.79 *AB + 0.43 *AC - 0.80 *AD -3.43 *BC + 0.27 *BD - 0.26 *CD-0.42 *A2 + 1.91 *B2 + 1.06 *C2 + 0.52 *D2 0.78 0.0299 Hardness (N) H= + 44.18 + 3.45 *A + 5.00 *B + 3.41 *C-6.80 *D + 4.89 *AB + 9.25 *AC + 4.37 *AD + 0.56 *BC - 0.70*BD - 7.55 *CD -1.78 *A2 + 4.38 *B2 +3.14 *C2 + 6.43 *D2 0.80 0.0171 Colour change CC= + 9.04 - 0.29 *A - 1.24 *B - 1.28 *C + 0.44 *D - 0.23 *AB +1.05 *AC - 0.87 *AD - 1.28 *BC -1.82 *BD + 0.33 *CD + 0.88 *A2 + 0.44 *B2 + 1.08 *C2 + 0.19*D2 0.84 0.0063 Overall acceptability OA= + 6.16 - 5.051 x 10-3 *A + 0.22 *B - 0.66 *C + 0.42 *D -0.53 *AB + 0.11 *AC + 0.15 *AD 0.061 *BC -0.22 *BD + 0.18 *CD + 0.20 *A2 + 0.22 *B2 - 0.73 *C2 + 0.18 *D2 0.90 0.0005 Parameter 6 Md Shaiq Alam, Harjot Khaira, Shivani Pathania, Sunil Kumar and Baljit Singh JAE : 52 (1) Table 6. Optimum values of extrusion process parameters and responses Target Process parameter Experimental range Min Max Optimum value Die temperature (0C) range 120 180 136 Screw speed (rpm) range 300 500 490 Feed moisture (%) range 14 20 15 Wheat flour (%) range 65 85 85 Carrot pomace powder (%) range 7.5 17.5 7.5 Defatted soy flour (%) range 7.5 17.5 7.5 Desirability Ingredients proportion Response Predicted value Bulk density(g.cm-3) minimize 0.166 0.485 0.227 Colour change minimize 6.47 14.16 9.52 WAI (g.g-1) maximize 3.88 4.59 4.29 WSI (%) maximize 18.16 28.82 24.68 Hardness (N) minimize 31.27 76.20 49.99 Expansion ratio maximize 1.91 3.07 2.92 Overall acceptability maximize 5.18 7.39 7.18 The effect of each independent variable was evaluated on expansion ratio. Die temperature had significant negative effect on expansion ratio. It was also evident that as the proportion of wheat flour increased, an increase in expansion ratio was apparent. High die temperature increases the superheating of water in the extruder, which encourages bubble formation. However, presence of sugar and carrot pomace powder hindered bubble formation at high temperature, and resulted in low expansion ratio due to expulsion of moisture from the extrudates. Increased feed moisture leads to a sharp decrease in the expansion of extrudate. High dependence of expansion on feed moisture would reflect its influence on elasticity characteristics of the starch-based material. Increased feed moisture content during extrusion might reduce elasticity of the dough through plasticization of the melt, resulting in reduced SME causing reduced gelatinization. This decreased the expansion and increased the density of extrudate. The results were consistent with the studies by Faubion and Hoseney (1982), Launay and Lisch (1983), Fletcher et al. (1985) and Ilo et al. (1999). Hagenimana et al. (2006) had shown that increase in moisture increased the bulk density of extrudates. 0.729 hydrophilic groups, and on the capacity of gel formation of macro molecule (Gomez and Aguilera, 1983). WAI for the extrudates ranged between 3.88 and 4.47 g.g-1 (Table 3). The linear terms of screw speed (P < 0.05) and wheat flour (P < 0.01) had significant effect on WAI. The quadratic terms of feed moisture (P < 0.05) also had significant effect on WAI (Table 4). The quadratic model obtained from regression analysis for density (D) in terms of coded levels of the variables is shown in Table 5. As the data in Table 3 reveals, an increase in screw speed and feed moisture decreased WAI. However, increase in die temperature and wheat flour in total composition increased WAI. It is worth to note that starch granules should undergo a certain degree of conversion to initiate water absorption. Gelatinization, the conversion of raw starch to a cooked and digestible material by application of water and heat is one of the important effects that extrusion has on the starch component of food. WAI has been generally attributed to the dispersion of starch in excess water, and the dispersion is increased by the degree of starch damage due to gelatinization and extrusion-induced fragmentation, i.e., molecular weight reduction of amylase and amylopectin (Yagci and Gogus, 2008). Water absorption index increased with the increase in temperature, probably due to increased dextrinization at higher temperature (Mercier and Feillet, 1975; Kumar et al., 2010). Water Absorption index (WAI) WAI measures the water holding by starch after swelling in excess water. WAI depends on the availability of 7 January-March, 2015 Extrusion Process Optimization for Soy-Carrot Pomace Powder incorporated Wheat-based Snacks Fig. 2: Contour plots depicting the effect of process parameters on product quality responses 8 Md Shaiq Alam, Harjot Khaira, Shivani Pathania, Sunil Kumar and Baljit Singh JAE : 52 (1) on hardness (Fig 2). Die temperature, screw speed and feed moisture content showed positive correlation with hardness, which indicated that hardness increased with an increase in these parameters. WSI, often used as an indicator of degradation of molecular components, measures the degree of starch conversion during extrusion being the amount of soluble polysaccharides released from the starch after extrusion (Ding et al., 2006). The range of WSI of the extrudates varied between 18.4 and 28.82 per cent. With respect to the effect of independent variables on WSI, negative coefficients depicted contrary relationships with WSI. Of all independent variables, WSI was significantly influenced by moisture (P<0.05). The quadratic model obtained from regression analysis for water solubility index (WSI) in terms of coded levels of the variables is shown in Table 5. The product terms of screw speed and feed moisture, and quadratic terms of screw speed had significant effect on WSI (Table 4 and Fig. 2). Lowest WSI was at 150oC dietemperature, 500 rpm screw speed, 14% moisture and 75% wheat flour-whereas highest value of 29.82 was observed at processing conditions of 150oC die temperature, 400 rpm screw speed, 17% moisture content and 75% wheat flour in the composition. This revealed that at constant die temperature and composition, screw speed increase by 100 rpm and moisture decrease by 3% resulted in an increase of 10.66% in WSI value. Increase in hardness with increase in moisture might be due to water acting as a plasticizer to the starch-based material, reducing its viscosity and mechanical energy dissipation in the extruder. The product thus becomes dense and bubble growth gets compressed. Previous studies also reported that hardness of extrudate increased as feed moisture content increased (Badrie and Mellowes, 1991; Liu et al., 2011). However, hardness decreased with increase in wheat flour as more starch is gelatinized with respective less carrot pomace proportion in the composition. Colour Change As a product is cooked, a change in its colour is apparent. The colour change during extrusion was examined and depicted in Table 3. Table 4 illustrates that colour change was greatly influenced by screw speed (P< 0.01) and feed moisture (P< 0.01). The values for colour change ranged from 6.47-14.17 (Table 3). The maximum colour change (14.17) was perceived at 120oC, 400 rpm screw speed and 14% moisture content, while minimum colour change (6.47) was recorded at 150oC, 500 rpm screw speed and 20% moisture content (Table 3). Further, it was observed that the effect of linear terms of screw speed and moisture (p<0.01), product term of screw speed and moisture (p<0.05); screw speed and wheat flour in the composition (p<0.01) and quadratic term of feed moisture were significant (Table 4, Fig. 2). Lower WSI indicates a minor degradation of starch, and such conditions lead to less number of soluble molecules in the extrudate (Hernandez- Diaz et al., 2007). Water is absorbed and bound to starch molecules bringing a change in inherent starch granule structure. Maximum gelatinization occurs at low moisture and high temperature or vice versa (Lawton et al, 1972; Mercier and Feillet, 1975). However, even at higher temperatures, high moisture content can diminish protein denaturation, which subsequently lowers WSI values during extrusion. Badrie and Mellowes (1991) and Hernandez- Diaz et al.(2007) reported similar findings. WSI is also reported to be related to the presence of soluble molecules that have sometimes been attributed to dextrinization (Colonna et al., 1989). Overall Acceptability Sensory properties of the product depicting its overall acceptability scores based on appearance, consistency and mouth feel are illustrated in Table 3. Extruded snacks were in acceptable range as per the sensory scores. The overall acceptability scores ranged from 5.18-7.39. Highest overall acceptability was for product processed at 150oC die temperature, 500 rpm screw speed, 14% feed moisture and 75% wheat flour in the composition whereas lowest overall acceptability was for product prepared at 150oC die temperature, 300 rpm screw speed, 20% feed moisture and 75% wheat flour in the composition. High feed moisture and carrot pomace and legume flour content in blend might have led to low acceptability scores. The linear terms of screw speed, feed moisture and wheat flour in the composition had significant effect on the overall acceptability scores. However, die temperature was observed to have insignificant effect on acceptability scores (Table 4). The product terms and quadratic terms of the Hardness The hardness of expanded extrudates is associated with expansion and cell structure of the product. Hardness is the peak force required for a probe to penetrate the extrudate. The higher the value of maximum peak force, the higher the hardness of the sample. Hardness of the extrudates ranged between 31.27 and 76.20 N (Table 3). The predicted regression model is described by the equation in terms of coded levels in Table 5. The linear terms of screw speed (p<0.05) and wheat flour in the composition (P<0.01) had significant effect on hardness. The product terms of die temperature and feed moisture, and quadratic effect of wheat flour in the total composition had significant effects 9 January-March, 2015 Extrusion Process Optimization for Soy-Carrot Pomace Powder incorporated Wheat-based Snacks 10 Md Shaiq Alam, Harjot Khaira, Shivani Pathania, Sunil Kumar and Baljit Singh JAE : 52 (1) Fig. 3: Overlay contours of different responses for optimization of extrusion process parameters Water solubility index (WSI) independent variables had insignificant effect on extrudate’s properties. Butt et al.(2004) in a study had observed that overall acceptability of snacks were in acceptable range. The results were in accordance with overall acceptability of rice-mungbean snacks (Sharma, 2012). was given to all the 4 process parameters. However, based on their relative contribution to quality of final product, the importance given to different responses was 3 for SME, WAI, WSI, H, BD, ER and 4 for OA (Table 6). Predicted values of bulk density (0.227), expansion ratio (2.926), water absorption index (4.299), water solubility index (24.68), hardness (49.99 N), colour change (9.52) and overall average acceptability (7.18) were used to overlay plot graphical optimization (Fig 3). Best extrusion conditions were 15 % moisture, 490 rpm screw speed, 136 0C die temperature and 85% wheat flour having desirability value of 0.729 (Fig. 3). Optimization and validation Graphical multi-response optimization technique was adopted to determine the workable optimum conditions for the development of extruded product using Design Expert software (Statease, DE 8.0.6.1). The process parameters were optimized for lower bulk density, colour change, hardness and higher expansion ratio, water absorption index, water solubility index and overall acceptability of wheat flour, carrot pomace and soy-based readyto-eat expanded product. These constraints resulted in “feasible zone” of optimum conditions (shaded area in the superimposed contour plots). Superimposed contour plots having common superimposed area of all the responses for extrusion processes are presented in Fig. 3. CONCLUSIONS Response surface methodology was effective in optimizing extrusion process parameters for twin-screw extruded snacks composed of defatted soybean flour, carrot pomace powder and wheat flour with feed moisture in the range of 14-20 %, die temperature 120-180oC, screw speed 300-500 rpm and 65-85 % wheat flour in the composition. Graphical techniques, in connection with RSM, aided in locating optimum operating conditions, which were experimentally verified and proven to be adequately reproducible. The In order to optimize the process conditions for extrusion process by numerical optimization, which finds a point that maximizes the desirability function; equal importance of ‘3’ 11 January-March, 2015 Extrusion Process Optimization for Soy-Carrot Pomace Powder incorporated Wheat-based Snacks Faubion J M; Hoseney R C. 1982. High-temperature short-time extrusion cooking of wheat starch and flour. I. Effect of moisture and flour type on extrudate properties. Cereal Chem., 59, 529-533. optimum process parameters obtained for development of extrudates were 15% moisture, 490 rpm screw speed, 136 0C die temperature and 85% wheat flour in the composition for achieving the maximum possible expansion ratio, WAI, WSI, overall acceptability with minimum hardness, bulk density and colour change. 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Eng., 50, 168-173. Ding Q; Ainsworth P; Plunkett A; Tucker G; Marson H. 2006. The effect of extrusion conditions on the functional and physical properties of wheat-based expanded snacks. J. Food Eng., 73, 142–148. Lee C H; Kim D C; Chun J H; Kim J B; Son C C. 1987. Food Extrusion Technology (Korean). J. Food Sci. Technol., 15, 392. Fan J; Mitchell J R; Blanchard J M V. 1996. The effect of sugars on the extrusion of maize grits: The role of the glass transition in determining product density and shape. Int. J. Food Sci. Technol., 31, 55–65. Liu C; Yanjun Z; Wei L; Jie W; Weihua W; Wu N; Zuo Y; Zhonglin; Yin Z. 2011. Preparation, physicochemical and texture properties of texturized rice produce by improved extrusion cooking technology. J. Cereal Sci., 54, 473-480. 12 Md Shaiq Alam, Harjot Khaira, Shivani Pathania, Sunil Kumar and Baljit Singh JAE : 52 (1) Meng X; Threinen D; Hansen M; Driedger D. 2010. Effects of extrusion conditions on system parameters and physical properties of a chickpea flour-based snack. Food Res. Int., 43, 650–658. Stojceska V; Ainsworth P; Plunkett A; Ibanoglu E; Ibanoglu S. 2008. Cauliflower by-products as a new source of dietary fibre, antioxidants and proteins in cereal based ready-to-eat expanded snacks. J. Food Eng., 87, 554-563. Mercier C; Feillet P. 1975. Modification of carbohydrate components by extrusion-cooking of cereal products. Cereal Chem., 52, 283–297. Upadhyay A; Sharma H K; Sarkar B C. 2008. Characterization of dehydration kinetics of carrot pomace. Agric. Eng. Int., 10, 1-9. Onyango C; Henle T; Hoffmann T; Bley T. 2004. Production of high energy density fermented uji using a commercial alpha-amylase or by single-screw extrusion. Lebensm-Wiss u-Technol., 37, 401–407. Veronica A O; Olusola O O; Adebowale E A. 2006. Qualities of extruded puffed snacks from maize/soybean mixture, J. Food Process Eng., 29, 149-161. Yagci S; Gogus F. 2008. Response surface methodology for evaluation of physical and functional properties of extruded snack foods developed from food by products. J. Food Eng., 86, 122–132. Sharma C. 2012. Development of extruded snacks utilizing broken rice and mung bean. Unpublished M.Sc Thesis, Punjab Agricultural University, Ludhiana, Punjab, India. 13 January-March, 2015 Optimization of Process Parameters for Production of Palmyrah Palm Jaggery Journal of Agricultural Engineering, Vol. 52 (1): January-March, 2015 Optimization of Process Parameters for Production of Palmyrah Palm Jaggery M. Madhava1, D. Ravindra Babu2, P. C. Vengaiah3 and B. Hari Babu4 Manuscript received:May, 2013 Revised manuscript accepted: February, 2015 ABSTRACT Palmyra palm (Borassus flabellifer L.) is one of the important and an alternate source for production of jaggery. Palmyra jaggery industry is facing technical issues as quantification of lime to be used, heating temperature and heating time of neera for jaggery preparation. A central composite rotatable design was used to optimize the process parameters like quantity of lime, heating temperature and heating time. Mathematical models and response surfaces were developed for estimating the total sugars, ash and moisture content of palmyra palm jaggery. Total sugars increased with increase in temperature and time, but lime had less effect over the optimal value. Ash content was more affected with lime, but less effected by the time and temperature. Moisture content was more affected by temperature and less by time and lime content. The best combination obtained to get good quality solid palmyrah jaggery was use of 2.1% lime, operating temperature of 111°C and 126 min of process time. The jaggery prepared at these conditions had 0.17% fat, 0.98% protein, 2.80% ash and 91.00% carbohydrates at 8.50% moisture content. Sensory evaluation of jaggery showed that the jaggery produced with above combination scored well for all quality attributes as compared to jaggery prepared at 2.1% lime, 1210C and 174 min time. Key words: Process, quality, sensory, lime, model, time, temperature, properties Palmyrah palm (Borassus flabellifer L.) belongs to the family Palmae, used for tapping neera. Neera is the sweet sap of the palm obtained by slicing the spathes of the palmyra or scraping the tendermost part just below the crown. Jaggery is obtained by processing of neera. Among 103 million palms in India, 30% of trees are in Andhra Pradesh (Ghosh et al., 1998). India annually produces about 6 MT of jaggery, which accounts for 70% of the total production in the world. About 65-70% of the total jaggery is produced from sugarcane, and the remaining 30% is from palms (Rao et al., 2009). Palmyra palm is one of the important alternate raw material for production of jaggery (or gur). Price of palm jaggery is determined by its quality, especially the colour, flavour and texture. Palm jaggery industry is an unorganized rural industry. The production process was controlled by persons having less scientific knowledge, which led to lack of process standardization for solid jaggery (Guerra and Mujica, 2009). Palmyra jaggery industry is facing problem of quantifying of lime to be used to prevent fermentation of neera after tapping, heating temperature and heating time of neera for maintaining proper quality, yield and nutritional properties of jaggery. and traditional practices still exist. For better export market, potential and supporting price, optimization of processing parameters plays an important role. Not much research has been conducted on palmyrah as the nutritional quality of liquid and solid jaggery products are not well known, though they are used in the preparation of ayurvedic/traditional medicines helping to reduce the chances of lung cancer, diabetes and obesity (Vengaiah et al., 2011). Hence, the research work reported was conducted to standardise the palm jaggery production process and to analyse the quality parameters of solid jaggery. MATERIALS AND METHODS Sample Preparation Fresh neera was tapped from about 18 trees of about 22 years old and 4-5 m tall in the early morning between 6-7 A.M, using sharp knife. Neera was collected by slicing the spathe two times in a day. Yield of neera varies from tree to tree. Fresh neera was collected in lime-treated earthen pots tied to the inflorescences during previous evening. Nera ejaculates from the inflorescence throughout the night. The pots were removed from the inflorescences on the next day. Collected neera was filtered through fine muslin cloth to remove debris and impurities. Clarification Food product like solid jaggery from palmyrah is not commercialized as the process has not been standardized 1 Assistant Professor, College of Agricultural Engineering, Bapatla. Andhra Pradesh, corresponding author email: madhava12@gmail.com; 2Post graduate student, College of Agricultural Engineering, Bapatla. Andhra Pradesh; 3Scientist, Horticultural Research Station, Pandirimamidi, Andhra Pradesh; 4Assistant Professor, College of Agricultural Engineering, Bapatla. Andhra Pradesh. 14 JAE : 52 (1) M. Madhava, D. Ravindra Babu, P. C. Vengaiah and B. Hari Babu (de-liming) process was done at 40°C to reduce the pH to 7.5 using either phosphoric acid or triple super phosphate solution or citric acid. Clarified neera was transformed into liquid jaggery by boiling in 20-gauge round-bottom thin galvanized iron (GI) pan at 108-110°C for 3-4 h till the syrup attained total soluble solids of around 81°Brix. The clarified neera was stirred continuously during heating to avoid charring. Thick viscous mass was transferred into 100 mm cube wooden moulds, and allowed to cool to room temperature. The viscous mass solidified during cooling. Solid jaggery was removed from the mould and sealed in low density poly ethylene bags. Ease, Inc., MN, USA (2012). Table 1 gives complete experimentation given by Design Expert software. Measurement of Parameters Total Sugars It is an important nutritional component that determines the sweetness of the product. Estimation of total sugars was carried out by Anthrone Method (AOAC, 2005). Ash Ash content of food stuff represents inorganic residue remaining after destruction of organic matter. High ash content indicates presence of adulterants in food material. It was determined according to the method given by AOAC (2005). Experimental Design For avoiding large number of experiments with three independent variables, a Central Composite Rotatable experimental Design (CCRD) was used to design the experiment, keeping optimized values of process parameters. For optimization of independent operational parameters (lime, time, and temperature), 15 experimental runs were carried out according to CCRD. About 10 l of neera sample was used to conduct each run. A CCRD experiment was made with three independent variables viz., quantity of lime (X1), heating time (X2) and temperature (X3). In the study, optimization of process variables was carried out using Design Expert version 8.0.7.1 of Stat- Fat and Protein Fat content in jaggery is very less. It was determined by Soxhlet Method (Ranganna, 1994), and protein content was estimated by Lowry’s Method (AOAC, 2005). Sensory Evaluation Sensory evaluation was done using 9-point hedonic scale method (Ranganna, 1994). Hedonic rating test was used to measure the consumer acceptability of food products. Table 1. Design of experiment for jaggery preparation with three independent variables in CCRD Expt. No. Lime, % (X1) Temperature, °C (X2) Time, min (X3) 1 1.5 (0) 124 (+1.682) 150 (0) 2 0.9 (–1) 121 (+1) 174 (+1) 3 1.5 (0) 117 (0) 150 (0) 4 2.1 (+1) 113 (–1) 174 (+1) 5 0.5 (-1.682) 117 (0) 150 (0) 6 1.5 (0) 117 (0) 190 (1.682) 7 0.9 (-1) 111 (-1) 126 (-1) 8 2.1 (+1) 121 (+1) 174 (+1) 121 (+1) 126 (–1) 9 2.1 (+1) 10 2.1 (+1) 111 (-1) 126 (-1) 11 0.9 (-1) 121 (+1) 126 (-1) 12 0.9 (–1) 111 (–1) 174 (+1) 13 1.5 (0) 117 (0) 110 (-1.682) 14 1.5 (0) 110 (-1.682) 150 (0) 15 2.5 (1.682) 117 (0) 150 (0) 15 January-March, 2015 Optimization of Process Parameters for Production of Palmyrah Palm Jaggery RESULTS AND DISCUSSION temperature rose from 113°C to 121°C at 126 min. Total sugars content followed a decreasing trend up to middle point, and thereafter followed an increasing trend when the time increased from 126 min to 174 min at 1130C temperature. Increase in total sugars was also observed at higher temperature and higher time, and reached a maximum value of 90.8% at constant time of 174 at 1210C. Evaporation of water at higher temperature and processing time increased the total sugars. Fresh neera was analysed for pH and total soluble solids (sugar concentration) in °Brix according to lime percentage. It was observed that fresh neera samples had pH value in the range of 9-14, TSS in the range of 7-15°Brix and lime in the range of 0.9 – 2.5 per cent. Optimization of Responses Total Sugars A nonlinear second-order regression equation was developed as a function of independent real values of quantity of lime in percentage (x1), temperature in degree centigrade (x2) and time in minutes (x3) for the dependent variable total sugars (S), and is expressed as: % 91 ) Total sugars, ( 92 S = 8 9.9 9 + 1.0 8 x1 − 0.1 2 x2 + 0.037 x − 0.1 4 x + 0.037 x R = 0.9 7 ...(1) 2 1 2 2 2 3 2 90 89 88 92 2.10 174.00 1.80 166.00 91 Total sugars, % 158.00 1.50 150.00 142.00 90 134.00 C: Time 89 A: Lime 1.20 126.00 0.90 Fig. 1(b): Effect of lime and time on total sugars at 117°C processing temperature 88 2.10 121.00 92 1.80 119.00 1.50 117.00 113.00 % A: Lime Total sugars , B: Temperature 91 1.20 115.00 0.90 Fig. 1(a): Effect of lime and temperature on total sugars at 150 min processing time Response surface plot Fig. 1(a) shows that the effect of lime on total sugars was less. With increase in temperature, total sugars increased with increase in lime. The behaviour was due to arrest of fermentation of neera with more percent of lime and evaporation of water with increase in temperature. 90 89 88 121.00 174.00 119.00 166.00 158.00 117.00 150.00 142.00 Figure 1 (b) shows that the response surface plot of total sugars as lime and time indicated that the total sugar content decreased from 90.4% to 90.0% when lime percentage increased from 0.9% to 2.1% at constant time of 126 min. Total sugars content slightly decreased from 90.4% to 85.3% when the time increased from 126 min to 150 min. Thereafter, total sugar percentage showed slightly increasing trend till constant time of 174 min was reached. C: Time 115.00 134.00 126.00 B: Temperature 113.00 Fig. 1(c): Effect of temperature and time on total sugars with 1.5% lime use Ash Content A nonlinear second-order regression equation was developed as a function of independent real values of quantity of lime in percentage (x1), temperature in degree centigrade (x2) and time in minutes (x3) for the dependent variable ash (A), as presented in Eq. (2). Figure 1 (c) shows that the response surface plot of total sugars slightly increased from 90.0% to 90.4% when the 16 M. Madhava, D. Ravindra Babu, P. C. Vengaiah and B. Hari Babu ( JAE : 52 (1) ) A = 2.01 + 0.9 6 x1 + 0.1 4 x12 − 0.037 x22 − 0.037 x32 R 2 = 0.9 8 ...(2 ) increased from 126 min to 150 min, and there after increased to 2.2% ash content at 174 min. Increase in ash content with increase in time might be due to increase in boiling of inorganic residuals. 3 Fig. 2(c) shows that ash content increased from 1.8% to 2.5% when temperature increased from 113°C to 121°C at 126 min of processing. Ash content showed a decreasing trend when time reached from 126 min to 150 min, and thereafter percentage of ash content increased when the processing time increased. Ash content finally reached to 1.7%, at 174 min and 113°C temperature. It was concluded that ash content was significantly affected by lime percentage, but was negligibly affected by time and temperature. 2 1.5 1 121.00 2.10 119.00 1.80 117.00 1.20 113.00 A: Lime 2.5 0.90 Fig. 2(a): Effect of lime and temperature on ash content at 150 min processing time % B: Temperature 3 1.50 115.00 2 Ash , Ash , % 2.5 1.5 3 1 2.5 Ash , % 174.00 121.00 166.00 2 119.00 158.00 150.00 117.00 142.00 C: Time 1.5 115.00 134.00 126.00 B: Temperature 113.00 Fig. 2(c): Effect of temperature and processing time on ash content at 1.5% lime use 1 174.00 Moisture Content 2.10 166.00 158.00 150.00 1.50 142.00 C: Time A nonlinear second-order regression equation was developed as a function of independent real values of quantity of lime in percentage (x1), temperature in degree centigrade (x2) and time in minutes (x3) for the dependent variable moisture content (M), and represented as: 1.80 1.20 134.00 126.00 A: Lime 0.90 Fig. 2(b): Effect of lime use and processing time on ash content at 1170C processing temperature ( ) M = 4.9 8 + 0.1 2 x1 − 1.4 9 x24 − 0.017 x12 + 0.0 8 x22 + 0.1 x32 R 2 = 0.9 7 ...(3) Response surface plot Fig. 2 (a) shows that at lower temperature, ash content decreased from 1.7% to 1.1% when lime percentage increased at 1130C. When temperature increased, ash content increased with increase in lime content. Increase in ash content with increase in temperature might be due to increase in inorganic residue during heating increased evaporation of water content in the form of vapours. Decrease in ash content with increase in lime content might be due to arrest of fermentation of sap causing incomplete destruction of organic matter. Moisture content , % 8 7 6 5 4 121.00 Figure 2(b) shows the response surface plot of ash as lime and time, and indicated that the ash content did not change when the lime percentage increased. Ash content had a relatively less effect of lime, and had more effect of time. Ash content started decreasing in parabolic paths when time 2.10 119.00 1.80 117.00 B: Temperature 1.50 115.00 1.20 113.00 A: Lime 0.90 Fig. 3(a): Effect of lime and temperature on moisture content at 150 min processing time 17 January-March, 2015 Optimization of Process Parameters for Production of Palmyrah Palm Jaggery to 2.1% at 126 min of processing. At lower lime percent, moisture content increased with time; but moisture content decreased at higher time and lime percent. Increase in moisture content with increase in both lime and time might be due to the reason that increase in lime percentage might have increased the hygroscopicity of jaggery, and bound water present in the bio-colloids of jaggery might have taken longer time to separate. Fig. 3(a) shows the response surface plot of moisture content against lime and temperature, and indicated that moisture content decreased from 5.3% to 4.3% when the temperature increased from 113°C to 121°C with use of 0.9% lime. Moisture content decreased with increase in temperature, and did not change when the lime percent increased at lower temperatures, but increased at higher temperatures. Fig. 3(c) shows that the moisture content of jaggery was affected by temperature, but had little effect of both time and lime content. 8 Moisture content , % 7 6 Proximate Composition 5 The proximate composition of best quality jaggery obtained at optimised processing conditions using RSM was at lime 2.1%, temperature of 111°C and time of 126 min. Variation in proximate compositions of solid jaggery samples can be observed from the Table 2. Moisture content of jaggery at optimized conditions was less than control sample. Low moisture content of solid jaggery produced at optimum conditions enhanced its shelf life and provided better keeping quality as compared to the control sample. Same fat content was observed in both the samples. Higher protein content was observed in solid jaggery prepared at optimum processing conditions as compared to the control sample. This might be due to lack of control of production process in traditional practice, resulting in loss of protein in control sample. Ash content was slightly higher for solid jaggery prepared at optimum conditions as compared to control sample, possibly due to minimum loss of minerals during processing at optimum processing conditions. It was also observed that carbohydrates were almost same for both the samples. 4 3 174.00 2.10 166.00 1.80 158.00 150.00 1.50 142.00 C: Time 1.20 134.00 126.00 A: Lime 0.90 Fig. 3(b): Effect of lime and time on moisture content at 117°C processing temperature Moisture content , % 8 7 6 5 4 3 174.00 121.00 166.00 150.00 117.00 142.00 C: Time Sensory Evaluation 119.00 158.00 115.00 134.00 126.00 Solid jaggery samples S4, S5, S7, and S9 had higher scores for colour; S4, S6, S7 and S9 for taste; samples S1, S4 and S7 for flavour and samples S1, S4, S5, and S7 for texture / appearance (Table 3). Among all nine jaggery samples, sample S4 (2.1% lime use, processing at 1260C for 111 min) scored well for all quality attributes, followed by S7 (2.1% lime use, processing at 1210C for 174 min). B: Temperature 113.00 Fig. 3(c): Effect of temperature and time on moisture content at 1.5% lime use Fig. 3(b) shows that moisture content increased drastically from 4.2% to 6.8%, as the lime content increased from 0.9% Table 2. Proximate composition of solid jaggery Sample Preparation Method Moisture, %, d.b Fat, % Protein, % Ash, % Carbohydrate, % Solid jaggery Lime: 2.1% Temperature: 111°C Time: 126 min 8.50 0.17 0. 98 2.8 91.00 Control Jaggery prepared by traditional practice (boiling up to 120°C) 9.19 0.17 0.35 2.5 92.00 18 JAE : 52 (1) M. Madhava, D. Ravindra Babu, P. C. Vengaiah and B. Hari Babu Table 3. Sensory evaluation of solid jaggery samples Sample No. Treatment condition Quality attribute Time (min) Temperature (0C) Lime (%) Colour Taste Flavour Texture S1 174 111 0.9 5 7 8 8 S2 150 124 1.5 5 5 6 6 S3 150 117 2.5 2 3 2 5 S4 126 111 2.1 8 9 8 9 S5 174 121 0.9 7 7 7 8 S6 150 117 0.5 6 8 3 7 S7 174 121 2.1 7 8 8 9 S8 150 117 1.5 5 6 5 6 S9 126 121 0.9 8 8 4 3 7 7 5 6 Control Traditional practice Guerra M J; Mujica M V. 2009. Physical and chemical properties of cane sugar “panelas”. Ciênciae Technologia de, 30(1), 250-257. CONCLUSIONS A central composite rotatable design was used to optimize the process parameters like quantity of lime, heating temperature and heating time. Mathematical models and response surfaces were developed for estimating the total sugars, ash and moisture content of palmyra palm jaggery. Among fifteen combinations, the best combination suited for the preparation of solid jaggery was use of 2.1% lime, processing temperature of 1110C and processing time of 126 min. Sensory scores of jaggery was an average 8.5 for all quality attributes of sensory evaluation. Ranganna S. 1994. Handbook of Analysis and Quality Control for Fruits and Vegetable Products. 2nd Edition, Tata McGraw Hill Publishing Company Limited, New Delhi, pp: 182. Rao P V K J; Das M; Das S K. 2009. Changes in physical and thermo- physical properties of sugarcane, palmyrapalm and date-palm juices at different concentration of sugar. J. Food Eng., 90 (4), 559-566. REFERENCES Vengaiah P C; Raju M S; Prasad K R; Kumari K. U. 2011. Food from Palmyra palm (Borassus labellifer L.) present practices and scope. In: XXIV National Convention of Indian Society of Agricultural Engineers, Tirupati, India, 24-25 January, pp: 162. AOAC. 2005. Association of Oficial Analytical Chemists. 18th Edition, Washington D.C. Ghosh A K; Srivastava A K; Agnihotri V P. 1998. Production Technology of Lump Sugar-Gur/Jaggery. Daya publishing house, Delhi, 178 – 209. 19 Performance Evaluation of a Sunlower Seed Huller January-March, 2015 Journal of Agricultural Engineering, Vol. 52 (1): January-March, 2015 Performance Evaluation of a Sunlower Seed Huller Olaosebikan Layi Akangbe1, Victor Ifeanyi Obiora Ndirika2, Usman Shehu Mohammed3 and Lawan Garba Abubakar4 Manuscript received: August, 2014 Revised manuscript accepted: January, 2015 ABSTRACT A centrifugal sunflower seed huller was developed. Its performance on two promising varieties of sunflower seeds grown in Nigeria was evaluated at varying levels of speed and product feed rate. The tests were run at 5% seed moisture content, dry basis. Both hulling efficiency and kernel breakage were observed to increase with increasing speed and reducing product feed rate, for the two crop varieties. Cleaning efficiency increased with increasing operational speed. Varietal difference had significant effect on kernel breakage. Hulling efficiencies for the two varieties studied were, however, at par. Key words: Sunlower, huller, eficiency, speed, feed rate, variety Sunflower (Helianthus annuus L.) is native to North America (Heiser Jr., 1976). It belongs to a family of flowering plants known as the Asterecaea (formerly Compositae). Two main types of sunflower seeds are grown. They are the high-oil-content and the low-oil-content sunflower seeds. The former is mainly grown and processed for its oil, an edible vegetable oil which is also used as salad oil as also for the manufacture of margarine, soaps and linoleum, among other things. The seed-meal left after oil extraction is incorporated in poultry and stock feeds for its nutritive quality (Senkoylu and Dale, 1999). The second type is used essentially for confectionery purposes. Seeds of sunflower may be roasted and eaten as snack. supplementing groundnut, which was the country’s major oilseed. Although the total number of hectares under cultivation for sunflower seeds in Nigeria is not known, it is being grown as an important crop in some parts of the country, and also as a research crop. Part of the focus of research on sunflower in Nigeria in recent time has included the hulling of sunflower seeds. Sunflower seed has a whitish kernel with a thin translucent skin enclosed in a hull. The high fibre, low protein and high wax content of the hull are major constraints in getting better oil yield during oil extraction (Senkoylu and Dale, 1999). As such, hulling sunflower seeds is widely adopted as a pre-requisite step to oil extraction. Hulling the seed of sunflower involves the separation of the kernel from the hull. Hull removal has not been totally successful (National Sunflower Association, 1994), partly because it is not easy to remove the hulls of high-oil-type sunflower seeds. This has been linked to the tight binding nature of the hulls to the kernels. Incomplete hulling has also been attributed to the shape of sunflower seeds (Bello, 1987). Nag et al. (1983) developed a centrifugal sunflower seed huller with a horizontal type impeller, and reported hulling efficiencies between 53-65% within a speed range of 33.343.4 m.s-1, at which less kernel breakages occurred. Yadav et al. (1996) studied the effects of peripheral speed, feed rate and moisture content of sunflower seeds of EC-68414 variety on hulling efficiency in a centrifugal huller using a vertical type impeller, and reported about 82% hulling efficiency and 15% kernel breakage at about 10% seed moisture content, 150 kg.h-1 product feed rate and 47.1 m.s-1 impeller peripheral speed. Amuthan et al. (2001) modified a centrifugal paddy sheller to hull sunflower seeds of CO-2 variety, and reported a maximum hulling efficiency of 87.72% at about 6.5% seed moisture content using a four-vane impeller with 80 mm vane radius of curvature. Nolasco et al. (2002) studied the effect of heat treatment A number of methods for hulling sunflower seeds have been reported (Beaumont, 1980a, 1980b; Hussain et al., 1980; Nag et al., 1983). Sunflower seeds are hulled based on either of the principles of impact or shear. The most widely adopted principle is that of impact by centrifugation. The production of sunflower seeds in Nigeria started in the 1950’s (Tanimu et al., 1987) with the sole aim of Lecturer, Department of Agricultural and Bioresource Engineering, Abubakar Tafawa Balewa University, P.M.B. 0248 Bauchi, 740001, Nigeria, email: olaosebikanakangbe@yahoo.com; 2Professor, Department of Agricultural Engineering, Michael Okpara University of Agriculture, Umudike, Umuahia, Nigeria; 3Associate Professor, Department of Agricultural Engineering, Ahmadu Bello University, Zaria, Nigeria; 4Associate Professor, Agricultural and Bioresource Engineering Programme, Abubakar Tafawa Balewa University, Bauchi, Nigeria. 1 20 Olaosebikan Layi Akangbe, Victor IfeanyiObiora Ndirika, Usman Shehu Mohammed and Lawan Garba Abubakar JAE : 52 (1) screens were emptied into the pneumatic chamber where free hulls were blown off through the outlet for hulls. The blower was a paddle type centrifugal blower (with 480 mm diameter blade sweep, 580 mm wide) enclosed in a housing. Final product/whole kernel fractions fell through the airstream and were collected at the base of the equipment. on hulling performance and reported hulling efficiency of 53% at optimum moisture content of 3% (wet basis) for an Argentinean variety of sunflower seeds. Thermal treatment was reported to result in higher hulling efficiency with high production of broken kernel fractions. In this study, a centrifugal sunflower seed huller with twin vertical impeller units was designed and fabricated. It was necessary to evaluate the performance of the equipment as influenced by speed, product feed rate and crop variety. MATERIALS AND METHODS Equipment Description and Working Principles Hopper (1), Feed chute (2), Impact ring (3), Impeller disc (4), Impeller vane (5), Impeller hub (6), Cover plate (7), Product ejection spout (8) and Twin hulling unit (9) Fig. 2: Exploded isometric view of hulling unit of sunlower hulling machine Experimental Design Two promising varieties of sunflower seeds grown in Nigeria, namely the Funtua and Isaanka varieties were used for the study. The crops were obtained from the research farm of the Institute for Agricultural Research, IAR, Samaru, Zaria, and kept in storage for more than one year under the same environmental condition. The moisture content of the crops at the time of the study was ≈5%, dry basis. The speed of operation of the equipment was varied at five levels. The speed levels were 26, 33, 39, 44, and 51 m.s-1 peripheral speeds of the impeller. The product feed rate was varied at two levels, namely 79 kg.h-1 and 344 kg.h-1. This gave a total of 20 treatments (5 x 2 x 2), and was considered as a factorial experiment fitted into a completely randomized design. The experiments were run in three repetitions, giving a total of 60 experimental runs. Half and full throat openings of the product feed chute were selected for this study, which when calibrated gave 74 and 344 kg.h-1 product feeds, respectively. Feed rate calibration involved feeding seeds during idle runs of the machine for 1 min, and collecting same at the chute feed outlets; this was done in 10 repetitions for each feed chute. The indicated feed rates were the observed mean values. Corresponding shaft rotational speeds at the shaker at the given impeller test speeds were 153, 191, 229, 260, and 298 rpm, respectively. Shaft rotational speeds at the blower at the given impeller test speeds were 203, 255, 306, 346, and 398 rpm, respectively. Hopper (1), Hulling unit (2), Huller product ejection spout (3), Shaker (4), Broken kernels outlet (5), Blower (6), Final product outlet (7) and Shaker hangers (8) Fig. 1: Front and sideviews of sunlower hulling machine The equipment consisted of two hulling units, each having a vertical type impeller; and two cleaning units, one being mechanical and the other pneumatic type (Fig. 1). A single hopper fed the two hulling units. Each hulling unit consisted of a seed chute, an impeller and an impact surface. The impact surface formed a ring around the impeller (Fig. 2). The impellers were straight vane types with no top constriction plates. Seed was fed by gravity from the hopper through the chute onto the surface of the impeller. The rate of feed of the products from the hopper was controlled using a control flap. Seeds fed were subsequently subjected to high centrifugal forces, made to flow along the straight vane orientations and flung against the surface of impact. The resulting impact caused dry brittle hulls to crack open to let out the kernels. Hulled sunflower seed fractions were evacuated from the hulling chamber via the product ejection spout onto the top screen of the shaker. The shaker was fitted with screens to sort the product fractions. An eccentric drive was incorporated to provide oscillating motion to the shaker. Broken kernels sorted by the shaker were collected through a special outlet. Product fractions which remained on the 21 Performance Evaluation of a Sunlower Seed Huller January-March, 2015 QTH = Total quantity of free hulls collected from all outlets, g, The performance of the equipment was evaluated in terms of hulling efficiency, kernel breakage, cleaning efficiency and product loss using the following relationships:  Q η H = 1001 − U  QO Q  η B = 100 B K   Q0  Q ηC = 100 F H  QT H   η L = 1001 − ηH ηB ηC ηL      QO QT    … … (1) (2) … = Quantity of unhulled seeds, g, QO = Quantity of products collected at all outlets, g, and QT = Mass of sample fed, g. Data Analysis All data were subjected to the analysis of variance. Treatment means were compared using the Duncan’s Multiple Range Test. Analysis of the research data was done using the SAS statistical analysis software. (3)    QU … (4) RESULTS AND DISCUSSION Where, Probability values for the effects of the factors studied and their interactions are presented in Table 1. The main effects of speed are presented in Table 2. Mean hulling efficiency was observed to increase with increasing impeller peripheral speed, being 46.7% at the lowest operational speed of 26 m.s-1 and 74.1% at the highest operational speed of 51 m.s-1. A similar trend was observed with kernel breakage, which also increased with increasing impeller peripheral speed. Mean kernel breakage was 15.5% at 26 m.s-1 and as high as 39.4% at 51 m.s-1. = Hulling efficiency, %, = Kernel breakage, %, = Cleaning efficiency, %, = Product loss, %, QBK = Quantity of broken kernels, g, QFH = Quantity of free hulls and impurities collected at the outlet for hulls, g, Table 1. Effects of the factors studied on equipment performance Source Hulling eficiency Kernel breakage Cleaning eficiency Product loss Speed <0.0001** <0.0001** <0.0001** 0.0008** Feed <0.0001** <0.0001** <0.0001** <0.0001** ns Crop variety Feed rate x Crop variety Speed x Crop variety Speed x Feed rate 0.13 <0.0001** 0.528 0.043* 0.002** 0.1124ns 0.18ns 0.018* 0.03* 0.2294ns 0.08ns 0.448ns <0.0001** <0.0001** <0.0001** 0.2619 ns Speed x Feed rate x Crop variety ns ns ns 0.297 0.7251 ns 0.124 0.111ns ns = not signiicant at 5% level; * = signiicant (at 5% level); ** = highly signiicant (at 1% level) Table 2. Equipment performance as inluenced by operational speed Impeller peripheral speed (m.s-1) Hulling performance (%) Hulling eficiency Kernel breakage Cleaning eficiency Product loss 26 46.7 15.5 6.83 11.5 33 59.6 25.1 17.8 12.9 39 66.0 30.5 31.4 11.3 44 71.5 35.2 42.1 10.7 51 74.1 39.4 57.2 11.0 SE 0.392 0.382 0.379 0.35 Comparisons of means are column-wise; SE = Standard Error of means 22 JAE : 52 (1) Olaosebikan Layi Akangbe, Victor IfeanyiObiora Ndirika, Usman Shehu Mohammed and Lawan Garba Abubakar being 10.7% and 12.3%, respectively. Low rates of feed favoured high hulling efficiency, and resulting lighter kernel fractions were easily lost pneumatically. Higher particle accelerations obtainable at higher operational speeds resulted in greater forces of impact for the seeds. As a result, seed pericarps tended to crack open more readily at high operational speeds. This agrees with findings by Nag et al. (1983), who reported almost proportional increases in hulling efficiency with increasing speed. As the severity of impact increased with increasing operational speeds, kernel fracture and kernel structure failures consequently occurred at high operational speeds. The degree of fragmentation of the kernels is, therefore, a function of the severity of the impact, and of the speed of operation of the equipment. This agrees with findings by Amuthan et al. (2001). Impeller peripheral speed appears to be the most important parameter affecting both hulling efficiency and kernel breakage (Yadav et al.1996). Crop Variety When the main effect of crop variety was considered, no significant difference was observed among mean values of hulling efficiency (Table 1). Mean hulling efficiency was 63.3% and 63.8% for the Funtua and Isaanka varieties, respectively. A study (Akangbe et al., 2006) on the physical properties of Funtua and Isaanka varieties of sunflower seeds revealed no significant differences in one thousand and single seed and kernel weights of the two varieties. Kernels of the Isaanka variety were, however, observed to disintegrate more easily upon impact, compared to those of the Funtua variety. Cleaning Eficiency Cleaning efficiency increased as speed increased at the blower. Cleaning efficiency was as low as 7% at the least blower operational speed of 203 rpm, and was 57% at 398 rpm. As the velocity of air in the pneumatic separation chamber improved with respect to the steady state velocity of the impurity stream, cleaning efficiency improved. For the interaction of feed rate with crop variety, hulling efficiency was observed to be higher for both the Funtua and Isaanka varieties at 79 kg.h-1; hulling efficiencies for the Funtua variety (66.9%) was at par with that for Isaanka (66.2%) (SE = 0.351) at this product feed setting. At 344 kg.h-1, about 62 and 60% hulling efficiencies were achieved with the Isaanka and Funtua varieties, respectively. Hulling Eficiency For the interactions of speed with crop variety, mean hulling efficiencies recorded for the Funtua variety were at par with those recorded for the Isaanka variety in the 39-51 m.s-1 impeller peripheral speed range, hulling efficiencies for both varieties increasing with increasing speed (Table 3). An increase in hulling efficiency was recorded as product feed was reduced. Hulling efficiency was 61% at 344 kg.h-1 and 67% at 79 kg.h-1(SE = 0.248). More products arrived on the surface of impact per unit time at the higher feed rate. This resulted in clogging of the surface of impact. Consequently, the force of impact was dampened for subsequently fed seeds and hulling efficiency was reduced at the higher feed rates. Increasing the area of the surface of impact to accommodate an increased number of seeds striking the impact surface per unit time at higher feed rates will improve hulling performance. The interaction of speed with product feed rate had significant effect on hulling efficiency (Fig. 3 and 4). A high mean hulling efficiency of 76% was obtained with the lower product feed of 79 kg.h-1 at 51 m.s-1 impeller peripheral speed. Mean hulling efficiency at 79 kg.h-1 and 44 m.s-1 (73.4%) was at par with mean hulling efficiency at 344 kg.h-1 and 51 m.s-1, which was 72.7 per cent. Product Loss Product loss was slightly less at 344 kg.h-1 than at 79 kg.h-1, Table 3. Hulling performances observed for the interactions of speed and crop variety Hulling eficiency (%) Kernel breakage (%) Impeller peripheral speed (m.s-1) Funtua Isaanka Funtua Isaanka 26 45.8 47.7 14.9 16.0 33 58.6 60.6 23.5 26.7 39 66.0 65.9 29.6 31.4 44 71.8 71.2 34.2 36.2 51 74.4 73.8 38.5 40.2 SE 0.554 0.463 Comparisons are performance wise; SE = Standard Error of means 23 Performance Evaluation of a Sunlower Seed Huller January-March, 2015 η H 344 = −0.0404S 2 + 4.2444S − 3 9.138(R 2 = 0.9984)…(8) Regression Equation Equations 5 – 8 are speed-based regression trends generated for hulling efficiency at 79 and 344 kg.h-1 feed rates for Funtua and Isaanka varieties of sunflower seeds (Fig. 3 and 4).The trends were hyperbolic with the coefficients of determination indicated in parenthesis against each function. Where, η H 79 = Hulling efficiency at 79 kg.h-1feed rate, and S = Speed, m.s-1, η H 344 = Hulling efficiency at 344 kg.h-1feed rate. CONCLUSIONS A centrifugal type huller for sunflower seeds was designed and fabricated. Significant increase was observed in hulling efficiency with increasing impeller peripheral speeds, with consequent increase in kernel breakage. Since the force of impact was dampened for seeds striking the surface of impact at higher product feed rates, reduction in kernel breakage could be obtained with high product feed, although with limiting effect on hulling efficiency. Cleaning efficiency improved significantly with increasing blower operational speed. As high as 76% hulling efficiency was recorded at 51 m.s-1 impeller peripheral speed and 79 kg.h-1 product feed rate. Further studies on other factors influencing the performance of this machine are necessary, particularly with a view to achieving reductions in kernel breakage. Impeller peripheral Speed, m.s-1 Fig. 3: Effect of speed and feed rate on hulling eficiency for Funtua variety of sunlower seed ACKNOWLEDGMENT This study was funded by the Institute for Agricultural Research, IAR Samaru, Zaria, Nigeria. REFERENCES Akangbe O.L; Ndirika V.I.O; Mohammed U.S. 2006. Design related physical properties of sunflower seeds. In: Proceedings VII International Conference and XXIIX Annual General Meeting of the Nigerian Institution of Agricultural Engineers, 28, 284 – 288. Amuthan G; Subramanian P; Palaswami P.T. 2001. Modifications made on a centrifugal paddy sheller for sunflower seed shelling.Agric. Mechanization Asia, Africa Latin Amer., 32(3), 51-53. Impeller peripheral Speed, m.s-1 Fig. 4: Effect of speed and feed rate on hulling eficiency for Isaanka variety of sunlower seed Beaumont J.H. 1980a. A hand operated bar mill for decorticating sunflower seeds. Rural Technol. Guide 9, Tropical Products Institute, London, U.K. η H 7 9 = −0.0382 S 2 + 3.8834 S − 2 3.275 (R 2 = 0.9838) ... (5) Hulling efficiency trends for Funtua variety are: η H 344 = −0.0374S 2 + 4.2127 S − 4 4.354(R 2 = 0.9994) … (6) Beaumont J.H. 1980b. A hand operated disc mill for decorticating sunflower seeds. Rural Technol. Guide 10, Tropical Products Institute, London, U.K. η H 7 9 = −0.0303S 2 + 3.2285S − 11.001(R 2 = 0.9737 ) …(7) Hulling efficiency trends for Isaanka variety are: Bello J.O. 1985. Sunflower, a potential oilseed for Nigeria. J. Nigerian Soc. Chem. Eng., 4(1 & 2), 36-38. 24 Olaosebikan Layi Akangbe, Victor IfeanyiObiora Ndirika, Usman Shehu Mohammed and Lawan Garba Abubakar JAE : 52 (1) Nolasco S.M; Riccobene I.C; Fernandez M.B. 2002. Hulling of high oil sunflower seed grown in Argentina. ASAE Paper No. 026123, St. Joseph, MI: ASAE. Heiser Jr. C.B. 1976. The Sunflower. University of Oklahoma Press, Norman: U.S.A. Hussain A.A.M; Sabur M.A; Rahman M.M. 1980. Design and construction of a manually operated castor bean cum sunflower dehuller. Agric. Mechanization Asia, Africa Latin Amer., 11(2), 83-84. Senkoylu N; Dale N. 1999. Sunflower meal in poultry diets: a review. World’s Poultry Sci. J., 55, 153-174. Tanimu B; Ado S.G; Misari S.M. 1987. Association of some morphological characters of sunflower (Helianthus annuusL.) varieties with seed yield. Paper presented at the XIV Annual conference of the Genetics society of Nigeria, Zaria, Nigeria. March 1 – 4. Nag K.N; Singh P; Bhandari R. 1983. A centrifugal impeller–type of sunflower seed decorticator.Agric. Mechanization Asia, Africa Latin Amer., 14(1), 55-58. National Sunlower Association (NSA). 1994. Sunflower meal used in livestock rations.National Sunflower Association, Bismark, North Dakota. Yadav R; Singh P; Tiwani G. 1996. Studies on centrifugal decortication of sunflower seed.Agric.Mechanization Asia, Africa Latin Amer., 27(3), 62-64. 25 January-March, 2015 Effect of Drying Methods and Storage on Quality of Ready–to–eat Dehydrated Carrot Shreds Journal of Agricultural Engineering, Vol. 52 (1): January-March, 2015 Effect of Drying Methods and Storage on Quality of Ready-to-eat Dehydrated Carrot Shreds V.R. Sagar1 Manuscript received: October, 2013 Revised manuscript received: January, 2015 ABSTRACT The investigation was carried out to evaluate the effect of drying methods and storage conditions on the quality of ready-to-eat dehydrated carrot shreds made from tropical orange coloured mature carrots. The prepared carrot shreds were blanched in boiling water for pre-standardized time, and dipped in sugar solution for 30 min. The shreds were drained, and dehydrated in a cabinet dryer a low-temperature dryer at temperature of 58±2oC and 40±2oC, respectively, up to final moisture content of 3-5 per cent. It took 9 and 12 h, respectively, for drying in cabinet dryer and low temperature dryer. Results indicated that cabinet dryer was better for dehydration of carrot shreds as compared to low temperature dryer, as it retained higher amount of β-carotene, total carotenoids, rehydration ratio, and low non-enzymatic browning in the finished product, and also took less time for bulk drying of the product. Dehydrated carrot shreds were packed in 200g HDPE pouches and stored at room temperature and low temperature for storage study. It was found that carrot shreds could be stored at room temperature up to 3 months without loss of colour, flavour and texture; and up to 6 months at low temperature (7oC). However, the quality of the product was significantly affected by storage temperature and period of storage. Key words: Carrot, Carotenoids, β–carotene, storage temperature, colour, texture, sensory quality Carrot (Daucus carota L.) is the second most popular root vegetable, after potato, in the world. It finds wide application in the day-to-day use in making curries, salads, juices, pickles, preserves, sweet meats and soups. Consumption of carrot is preferred by the consumers due to its high nutritive value, antioxidant, anticancerous and other medicinal properties (Suvarnkuta et al., 2005). Carrot root contains β-carotene which is a precursor of vitamin A, and is an essential component of the visual pigments in the retina. Its deficiency leads to xerophthalmia and night blindness in human beings. The supply of 5.2 - 6.0 mg b-carotene per day can prevent cancer, as carotenoids may act as antioxidant by quenching singlet oxygen and triplet excited states (Palozza and Krinsky, 1992). rice pullaw, etc. Dehydrated carrot shreds can reduce the cost of transportation and storage without affecting their quality, and could be added as a natural antioxidants to develop new commercial products. The present investigation was, therefore, undertaken with the objective to optimize the drying method and storage conditions to enhance the shelf life of ready-to-eat dehydrated carrot shreds. MATERIALS AND METHODS Material Preparation Mature tropical orange coloured carrots were obtained from experimental field, Division of Vegetable Science, Indian Agricultural Research Institute, New Delhi. The roots of carrots were washed with running tap water, peeled by scrapping with a sharp stainless steel knife, washed again to remove scrapped material and finally made into shreds of about 5 mm length with the help of a shredding machine. During peak season, abundant supply of carrots keep the selling price very low causing losses to the growers. To preserve the carrots for off-seasons, dehydration is one of the important methods. Dehydrated products require less storage and transportation cost. Blanching and sugar Treatment Prepared shreds were blanched in boiling water (shreds: water ratio of 1:2) for 4 min (pre-standardized time) as pretreatment to inactivate the peroxidase enzymes. Blanched shreds were dipped in an equal amount (by weight) of 70o B sugar solution containing 0.25% potassium metabisulphate (KMS) for 30 min to ensure complete immersion. Consumers presently have been increasingly conscious about the quality of food. There has been growing demand for ready-to-eat foods like “ready-to-eat carrot shreds”, which can be eaten as such and also used for preparation of other carrot products like halwa, ice cream, shakes, biscuits, 1 Division of Food Science and Postharvest Technology, Indian Agricultural Research Institute, New Delhi- 110012; e-mail: vidyaram_sagar @yahoo.com 26 JAE : 52 (1) V.R. Sagar Dehydration and Drying Rate (7.0°C) for storage study. The product was evaluated at intervals of one month, up to 3 months, for storage performance. After 30 min, the treated shreds were drained through sieve and spread in the form of thin layer @ 2.5 kg.m-2 density and dried in cross-flow hot air cabinet dryer (Kilburn make, model-0248, air flow rate 1.20- 1.80 m.s-1, temperature 58±2°C and RH 25-45%) to a moisture content of 3-4% in the finished product. Sensory Evaluation To evaluate the sensory qualities of dehydrated carrot shreds during storage, carrot halwa and carrot burffee were prepared from it through reconstitution with equal amount of milk and dry fruits. Carrot halwa and carrot burfee were subjected to sensory evaluation by a panel of 7 semi-trained members for colour, flavour, texture and overall acceptability. Attributes were scored using a 9-point Hedonic scale. A score of 5.5 and above was rated acceptable (Szezeniak, 1983). Treated shreds were also dried using a low temperature dryer (LTD) with air flow rate of 0.12-0.16 m.s-1 at 40±20C and 20-50% RH in thin layer @ 1.0 kg.m-2 to a moisture content of 4-5 per cent. During drying, the samples were turned periodically every 2 h for uniform drying. Drying rate was computed by recording the loss in weight at half an hour interval, and expressed as the rate of residual water to dry matter (kg of water per kg of dry matter). Statistical Analysis Data obtained in the present study were subjected to statistical analysis of variance techniques as suggested by Panse and Sukhatme (1989). The critical difference (C.D) value at 5% level of probability was compared for making the comparison among different treatments. Physico-chemical Analysis Fresh as well as dehydrated carrot shreds were analysed for titratable acidity and sugars by AOAC (2005) methods. RESULTS AND DISCUSSION Moisture content was determined by drying a known weight of the sample in a hot air oven at 60±5°C to a constant weight, and expressed as per cent. Drying Rate Drying rate was faster when carrot shreds were dried in cabinet dryer as compared to low temperature dryer. It took 9 h for drying carrot shreds to reach a moisture content of 3.4% (w.b), while in case of low temperature dryer (LTD) it took 10 h (Fig. 1). Better performance of cabinet dryer might be due to high temperature and faster removal of water. Total carotenoids, β-carotene, sulphur-dioxide and nonenzymatic browning (NEB) as optical density (OD) of alcoholic extracts of sample were determined by the methods given by Ranganna (2002). Drying and Rehydration Ratio Drying ratio was calculated as net dry weight obtained from fresh weight of the material. Rehydration ratio was determined by taking 5 g of dehydrated sample in a beaker and adding 50 ml of warm (60°C) water in it. After 1 h, the product was kept on blotting paper for 15 min to remove surface water of the product, and then the weight of the rehydrated material was taken. Rehydration ratio was determined as per methods of Ranganna (2002). Packaging CD= Cabinet dryer, LTD= Low temperature dryer On the basis of chemical constituents and sensory score, dehydrated carrot shreds dried in cabinet dryer was selected as of better quality. Such material was packed in 200 gauge HDPE (High density polyethylene) pouches and sealed. Packed pouches were stored at room temperature (15-35°C) as well as at low temperature Fig. 1: Trend of drying rate of carrot shreds in dryers Dehydration and Rehydration Ratio The difference in dehydration ratio among dryers was statistically significant. Similar results have been reported by Jayaraman et al. (1991) for green leafy vegetables. 27 January-March, 2015 Effect of Drying Methods and Storage on Quality of Ready–to–eat Dehydrated Carrot Shreds Rehydration ratio too was higher in the product dehydrated in cabinet dryer as compared to low temperature dryer, Table 1. Higher rehydration ratio might be due to faster drying process that caused less cellular and structural changes in the final product. Poor rehydration ratio in dehydrated carrot shreds dried in low temperature dryer might be due to poor texture of the finished product, which might have absorbed less water during rehydration as compared to reconstituted material dried in cabinet dryer. The difference in rehydration ratio between dryers was statistically significant. Table 1. Effect of dryers on dehydration ratio, rehydration ratio, non-enzymatic browning and drying time Dryer Drying ratio Rehydration ratio NEB (O.D.: 420 nm) Drying time, h 2.89:1 3.3:1 0.235 1:3.33 1:2.73 0.371 0137 0.144 0.346 9 12 - Cabinet dryer Low temp. dryer C.D. at 5% NEB= Non-enzymatic browning Table 2. Effect of dryers on physico-chemical constituents of ready-to-eat dehydrated carrot shreds Dryer Parameter Total sugar, Total carotenoids, β-carotene, SO2, % Reducing sugar, % % mg.100g-1 mg.100g-1 ppm 3.10 0.89 24.83 65.23 42.24 24.82 869 Low temp. dryer 4.72 0.90 24.09 63.78 40.20 22.00 881 C.D.at 5% 0.334 NS 0.528 1.214 2.45 1.69 11.74 Moisture, Acidity, % Cabinet dryer Table 3. Effect of storage on quality of ready-to-eat dehydrated carrot shreds Parameter Storage period, month RT C.D. at 5% LT Initial value 1 2 3 1 2 3 T P TxP Moisture, % 3.00 5.58 4.23 4.88 3.24 3.41 3.62 1.01 0.10 0.59 NEB, O.D at 420 nm 0.143 0.175 0.201 0.233 0.152 0.161 0.168 0.002 0.003 0.004 Reconstitution ratio 1:3.20 3.13 3.05 2.98 3.15 3.11 3.04 NS NS NS Reducing sugar, % 24.43 25.49 26.48 27.77 25.37 25.79 26.03 0.72 NS 0.80 Total sugar, % 65.31 65.99 66.85 66.99 65.44 65.75 66.11 0.56 NS 0.96 Total carotenoids, mg.100g-1 36.04 34.34 30.91 26.72 34.63 33.58 31.72 0.38 0.42 0.40 β- carotene, mg.100g 23.79 22.67 20.61 17.63 22.85 22.18 20.94 0.25 0.31 0.44 869 767.33 657.33 481.00 794.33 739.33 710.33 7.99 9.79 13.85 -1 SO2, ppm Note: n=3, RT= Room temperature, LT= Low temperature, T= Temperature, P= Period Table 4. Sensory score of carrot halwa and carrot burfee prepared from dehydrated carrot shreds after 3 months of storage Storage condition Room temperature Low temperature C.D.at 5% Carrot product Appearance Flavour Texture Overall acceptability Carrot halwa 8.20 8.00 7.80 8.00 Carrot burfee 8.20 7.50 7.50 7.70 Carrot halwa 8.53 8.50 8.40 8.50 Carrot burfee 8.43 8.50 8.30 8.47 - NS 0.741 0.557 0.516 Note: n= 7 panel members, NS= Non signiicant 28 JAE : 52 (1) V.R. Sagar Non–enzymatic Browning in moisture due to variation in dryers were statistically significant. Non–enzymatic browning was lower in the shreds dried in cabinet dryer as compared to low temperature dryer, Table 1. This might be due to less moisture content in the dehydrated product dried in cabinet dryer and less reactions between amino acid and sugars during storage. Higher nonenzymatic browning in dehydrated carrot shreds might be due to more moisture dried in low temperature dryer, which might have caused faster reactions between amino acids and sugars in the product. The difference in non-enzymatic browning due to dryers was statistically significant. Storage Conditions The effect of storage conditions on the quality of ready-toeat dehydrated carrot shreds is presented in Table 3. The results indicated that there was gain in moisture content in dehydrated carrot shreds. The gain of moisture might be due to absorption of moisture from the atmosphere by the packages. Similar trend was observed by Kadam et al. (2006). However, the gain was statistically not significant both for temperature and period of storage. The nonenzymatic browning (NEB) increased with increase in storage period. This could be attributed to the degradation of SO2 during storage of the product (Sagar et al., 1998). Reconstitution ratio was greatly influenced by the storage period, and the reduction was more or less uniform, irrespective of temperature during storage. Decrease in rehydration ratio might be due to gain of moisture and loss of texture by the dehydrated carrot shreds during storage. Similar trend was observed by Dev et al. (2006) in dehydrated onion slices. Reducing and Total Sugar Reducing and total sugars were higher in the shreds dried in cabinet dryer compared to low temperature dryers, Table 2. This might be due to more reduction of moisture content and more concentration of nutrients available in the product. The differences in reducing sugars due to dryers were statistically significant. However, total sugars and their interaction between dryers for reducing and total sugars were non-significant. Total Carotenoids and β-carotene Content Increase in storage period resulted in significant reduction in both carotenoids and β-carotene. The decrease due to increase in storage period was highly significant at room temperature as compared to low temperature. It is evident from the above that lowering of temperature helped to maintain a higher level of carotenoids and β–carotene, thereby improving the quality of the product. Total carotenoids and β-carotene content were higher in carrot shreds dried in cabinet dryer as compared to low temperature dryer, Table 2. This might be due to exposing the samples for lower period in cabinet dryer as compared to low temperature dryer. However, the other reason might be due to the proportional moisture content and dry matter in the finished product, which might have affected the total carotenoids under both drying conditions. The changes in β-carotene and total carotenoids due to dryers were statistically significant. Similar pattern has been reported by Jayaraman et al. (1991) in some vegetables. Negi and Roy (2001) reported that sun, solar, shade or cabinet drying decreased b-carotene of amaranth and fenugreek significantly. The total sugars were particularly higher at room temperature as compared to low temperature. It could be attributed to more rapid hydrolysis of polysaccharides and their subsequent inversion to reducing sugars. The increase in sugar had also been observed by Sagar et al. (1998) in dehydrated mango slices. Total sugar increased significantly at room temperature as compared to low temperature. Sulphur Dioxide Sulphur dioxide decreased rapidly due to storage temperature and period of storage. Similar observation was made by Nadanasabapathi et al. (1993) in mango fruit bar. Significant reduction in SO2 due to storage temperature and period of storage was observed. Sulphur dioxide was higher in the low temperature dryer compared to cabinet dryer, Table 2. This might be due to effect of low temperature used for dehydration. The variation in SO2 due to dryers was found to be statistically significant. Sensory Evaluation Thus, Cabinet dryer was found to be superior for dehydration of carrot shreds as it reduced the moisture to lower level (Table 2). The superiority of the product dried in cabinet dryer might be due to faster removal of moisture from the product. Similar influence of solar dryer on moisture content has also been reported by Pande et al. (2000). The changes Sensory evaluation (Table 4) of carrot halwa prepared from stored ready-to-eat dehydrated carrot shreds indicated that there was better acceptability (overall acceptability 8.5) when the carrot halwa was prepared from ready-to-eat dehydrated carrot shreds stored at 7°C than those stored at room temperature (overall acceptability 8.0). 29 January-March, 2015 Effect of Drying Methods and Storage on Quality of Ready–to–eat Dehydrated Carrot Shreds Kadam D M; Samuel D V K; Prasad R. 2006. Optimization of pre- treatments of solar dehydrated caulilower. J. Food Eng., 77, 659-664. Appearance, flavour and overall acceptability scores were highest for carrot burfee prepared from dehydrated carrot shreds stored at low temperature as compared to carrot burfee prepared from samples stored at room temperature. This might be due to less moisture content in the dehydrated carrot shreds stored at low temperature causing slightly superior texture of the burfee (Table 4). Significant difference existed in appearance of carrot halwa and carrot burfee. However, carrot halwa and carrot burfee prepared from dehydrated carrot shreds stored at room temperature and low temperature significantly differed in flavour, texture and overall acceptability score. Nadanasabapthi S; Srivatsa A N; Natraju S. 1993. Storage study of mango bar in lexible packaging materials. Ind. Food Packer, 5, 8-12. Negi P S; Roy S K. 2001. Effect of drying conditions on quality of green leaves during long term storage. Food Res. Int., 34, 283-287. Palozza P; Krinsky N I. 1992. Antioxidant effects of carotenoids in vivo and in vitro: An overview. Mds. Enzymol., 213, 403-420. CONCLUSIONS Pande V K; Sonune A V; Philip S K. 2000. Solar drying of coriander and methi leaves. J. Food Sci. Technol., 37, 592-595. Ready-to-eat dehydrated (using cabinet dryer) carrot shreds can be used for preparation of other value-added carrot products like carrot halwa and carrot burfee during off-seasons. With proper packaging, one can safely keep the product at room temperature and low storage up to 3 months without significant variation in nutritional and sensory qualities. Panse V G; Sukhatme P V. 1989. Statistical methods for agricultural workers. Publication and Information Division, ICAR, New Delhi. Ranganna S. 2002. Handbook of Analysis and Quality Control for Fruit and Vegetable Products. 2nd Ed., Tata McGraw Hill Pub. Co. Ltd., New Delhi, India. REFERENCES AOAC. 2005. Official Methods of Analysis, 18 Ed., Association of Official Analytical Chemists, Gaithersburg. Sagar V R; Khurdiya D S; Balakrishnan K A. 1998. Effect of storage temperature and period on quality of dehydrated ripe mango slices. J. Food Sci. Technol., 35 (2), 17-150. Dev R; Subanna V C; Ahlawat O P; Gupta Huddar A G. 2006. Effect of pre treatment in the quality characteristics of dehydrated onion rings during storage. J. Food Sci. Technol., 43(6), 571-574. Suvarnkuta P; Devahastin S; Majumdar A S. 2005. Drying kinetics and b- carotene degradation in carrot undergoing different drying process. J. Food Sci., 70, 520-526. Jayaraman K S; Dasgupta D K; Babu Rao N. 1991. Quality characteristics of some vegetables dried by direct and indirect sun drying. Ind. Food Packer, 45, 16-23. Szezeniak A S. 1983. Physical Properties of Foods: What They are Their Relation to Other Food Properties. In: Physical Properties of Foods, (Eds. Pelly M, Bagley EB), The AVI Publishing Co. Inc, Westport. th 30 JAE : 52 (1) A.R. Mhaske, S.M.Taley, Shinde, R.N. Katkar Journal of Agricultural Engineering, Vol. 52 (1): January-March, 2015 Spatial Assessment of Wastewater Quality of Nag River for Irrigation A.R. Mhaske 1, S.M.Taley2, Shinde3 and R.N. Katkar4 Manuscript received: June, 2014 Revised manuscript accepted: January, 2015 ABSTRACT Rapid industrialization and overpopulation have stimulated increase in both domestic and industrial wastewater. Due to lack of efficient sewerage system and absence of treatment plants, the wastewater is discharged into drainage systems causing environmental and health implications. At various points, the wastewater of these drains is used for irrigation purposes. A study conducted at Nagpur during 2012-13 characterizes the effluent of major drains of Nagpur city and assess its suitability for irrigation. Temporal and spatial wastewater samples were collected from drains in rainy, winter and summer seasons during morning, afternoon; and evening and analysed for various parameters of special concern to irrigation. TSS, TDS, BOD, COD, N, P, EC, pH, micronutrient like Zn, Mn, Cu, Fe and heavy metals Co, Cd ,Cr and Pb varied in concentration at different locations at different time and seasons when compared with BIS and FAO guidelines for irrigation. Regular monitoring and proper treatment of wastewater before discharging into the drains can reduce the pollution in these drains. As the sewage water contains pollutants at higher levels, its direct use for irrigation and other purpose should be restricted. Key words: Industrialization, wastewater quality, drains, irrigation, rainy, winter and summer season, BIS guidelines India is the second most populous country of the world with current population of approximately1210 million according to survey of 2011, and will reach 1530 million by 2030 at the existing growth rate (Population census organization, 2011). Due to water scarcity issues, untreated effluents from the cities are being used for irrigation purposes in many parts of the country, including Nagpur in Maharashtra, having the consistency reported by Naeem (2009). Consequently, urban and peri-urban farmers use marginal or untreated wastewater to cultivate a variety of crops (Ensink et al., 2008). Strauss and Blumenthal (1990) estimated that 73,000 ha in India were irrigated with wastewater. Surely, the typology used to obtain this estimate must have been different from the one used for China, where Mara and Cairncross (1989) estimated 1.3 million ha were irrigated with wastewater. of Pakistan and India. The results showed high values of biochemical oxygen demand (BOD), total organic carbon (TOC), chemical oxygen demand (COD), and trace metals in the samples taken from the Indo-Pak border. The change in physico-chemical properties of the soil is the chronic impact of the usage of wastewater for irrigation, eventually increasing the amount of heavy metals in the soil. Sharma et al. (2007) and Sahu et al. (2007) reported that longterm use of wastewater of drains can lead to accumulation of high amounts of trace elements in soils, and can enter the food chain through absorption via plants. Cd can be easily absorbed and accumulated in plants and animals in very high quantities. Cd level was high at all sites in those plants which were receiving wastewater for irrigation as compared to plants getting tube well irrigation. Similarly, Beta vulgaris (palak) had been highly contaminated with Cd, Pb and Ni due to wastewater irrigation in suburban areas of Varanasi, causing serious health risks for the human beings (Tariq et al.,2006 ). Originally, the purposes of these drains were to collect the flood water and agricultural excess water. But due to increase in population and rapid industrialization of Nagpur, the drains are now mainly used to collect the municipal effluents of diverse composition resulting in increasing pollution in the irrigation outlets (Hamid et al., 2013; WWF, 2007). Afzal et al. (2000) conducted a study to investigate the degree of pollution in the Hudiara drain, caused by disposal of untreated sewage and industrial waste A strong correlation was found between Giardia infections in those households who were using wastewater farming as compared to the ones using regular irrigation water. Cattle grazing on freshly irrigated grasslands with untreated wastewater reportedly suffered from disease (cysticerosis) in Melbourne, Australia and Denmark (Pay et al., 2010). An 1 Department of Agricultural Engineering, Agricultural College under Dr. Panjabrao Deshmukh Krishi Vidyapeeth (Agricultural University), Nagpur-444104 (M.S.) India (Corresponding author email: mhaskear@gmail.com); 2,3,4Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola -444104 (M.S.), India. 31 January-March, 2015 Spatial Assessment of Wastewater Quality of Nag River for Irrigation extensive and seasonal study was carried out by Akhtar and Mohammad (2012) on the effluent quality of six drains of Lahore and their impact on the aquatic life of river Ravi, into which these drains were discharging their effluents. They reported that Ravi river water quality was not by any means suitable for the propagation of fish during dry weather and canal closure period.The present study was conducted to characterize the physical and chemical characteristics of effluents in the Nag river of Nagpur city (Maharashtra) in order to determine suitability of its water for irrigation of agricultural crops. 2. Canal road near “Dande Hospital”, collecting domestic sewage water from left portion of the city and addition of pollutant load from medicines and other materials used in the hospital including the sewage of the patient which is considered to be a separate source of pollution load. MATERIALS AND METHODS Similar sites were selected by Akhtar and Mohammad (2012). Fig. 1 shows the locations of study area. 3. Third site near the “Road Transport Ofice”which collectsdomestic sewage water from right portion of the Nag river. 4. Fourth site at the Inlet of the sewage treatment plant. Selection of Study Area Sampling Procedure Originally, the Nag river was to collect the flood water and agricultural excess water. A dam was later constructed across the river, and upstream water was arrested in the dam. Normally the drains are now mainly used to collect the municipal effluents of diverse composition throughout the year resulting in increasing pollution in the irrigation outlets. Only during overflow of waste weir, this water flows in the river. The study was carried out during the period June, 2012 to May, 2013. During this period, sewage water samples were collected spatially from the mentioned point sources. Sewage influent samples were collected in each month of the period on a fixed day in morning at 6 A.M, in noon 12 P.M and evening at 6 P.M. A total of 144 samples were collected from the all point sources throughout one year. Sampling bottles were soaked overnight in diluted hydrochloric acid before use, and rinsed two times with sample to be collected before filling. Small quantity of Touelen was added as preservative in each sample. The sample bottles were labelled carefully as per the locations and time. Separate samples were taken and preserved at 40C during transportation to the laboratory, and were immediately analysed for BOD, COD, turbidity, pH and faecal coli forms. Four major sampling sites of Nagpur city were selected for the present study and the samples were collected from each site. The sampling sites were as follows. 1. One kilometre upstream from the sewage treatment plant where two tributaries (left and right) joins and is near to “Bhole petrol pump” to access the pollutant load from the petrol pump, particularly heavy metals. Fig.1: Map showing the location of the sampling sites 32 JAE : 52 (1) A.R. Mhaske, S.M.Taley, Shinde, R.N. Katkar Table 1. Instruments/procedures used for the analysis of efluents of the drains Sl.No. Parameter Instrument / Procedure Reference 1. pH Potentiometric method APHA (1985) 2. Electrical conductivity Conductrometric method APHA (1985) 3. Biological oxygen demand Winkler titration method APHA (1985) 4. Chemical oxygen demand Reflux method APHA (1985) 5. Total dissolved solids Gravimetric method APHA (1985) 6. Total suspended solids Gravimetric method APHA (1985) 7. Nitrogen Alkaline distillation APHA (1985) 8. Turbidity Nephelometric method APHA (1985) 9. Colour Visual comparison method APHA (1985) 10. Odour Smell observation ------------ 11. Sulphate Trimetric method APHA (1985) 12. Phosphate Vanadomolybdate phosphoric acid (colorimetric)method APHA (1985). 13. Micronutrient and heavy metal Atomic absorption spectrophotometric method Page et al.(1982) Analytical Methods All analyses were carried out as per standard methods (APHA, 1985). Each sample of wastewater was tested for various physico-chemical parameters as reported. The parameters like total suspended solids, total dissolved solids, biological oxygen demand, chemical oxygen demand, nitrogen, phosphorus, faecal coliform, turbidity, pH, EC, heavy metals (Cd, Co, Cr and Pb) and micronutrients (Cu, Fe, Mn and Zn) were analysed to test the water quality. The results were compared with Bureau of Indian standard specified for irrigation to establish a baseline condition of pollution level in the Nag river. Table 1 shows the instruments and procedures used for analyses of physicochemical parameters, micronutrients and heavy metals in the waste-water. ...(1) If the weights were not specified, they were assumed to be 1 for all data points. ‘p’ is defined between 0 and 1. p = 0 produces a least-squares straight-line fit to the data, while p = 1 produces a cubic spline interpolant. If the smoothing parameter was not specified, it would automatically select in the “interesting range.” However, Smoothing Splines were also piecewise polynomials like cubic spline or shapepreserving interpolants and were considered a nonparametric fit type. The ‘p’ value in this case was near to 1 (e.g. 0.9), indicating a cubic spline interpolant. Therefore, this technique was found to be suitable to fit the noisy data of different pollutant concentration in sewage water. Curves were developed with p=0.9 using Eq. 1. Total dissolved solids, biological oxygen demand, chemical oxygen demand, nitrogen and phosphorus were considered as important parameters for irrigation water, and hence considered for statistical analysis by applying the spline smoothing technique as the data had large variability and erratic. Statistical Analysis The data of different parameters in seasonal sewage water was noisy and erratic.Therefore it was very difficult to fit the data by applying simple models to find out the best trend equations. Therefore, analysis with nonparametric fitting was done. Smoothing Splines technique was used for analysis of noisy and erratic data of pollutant in sewage water, and graphs were drawn by curve-fitting tool box of Mat-lab to find the best trend for the seasonal variation.The smoothing spline"s" was constructed for specified smoothing parameter "φ " specified weight wi. The smoothing spline minimises as: RESULTS AND DISCUSSION The focus of the present study was to quantitatively characterize the pollution load in the Nag river from the month of June 2012 to May 2013, and to assess the suitability of drain water for irrigation as it is being frequently used in irrigation. 33 January-March, 2015 Spatial Assessment of Wastewater Quality of Nag River for Irrigation Climatic Data average weekly evaporation for the months of the study are given in Table 2. The climatic data on average weekly maximum and minimum temperature for the month, monthly rainfall and Table 2. Climatic data of Nagpur station from June 2012 to May 2013 Sl. No. Month Rainfall, C mm Av. weekly evaporation, mm Av. weekly min. temp., Av. weekly max. temp., 0 0 C 1. June, 2012 39.65 28.78 49.7 8.58 2. July, 2012 32.22 25.22 244.8 4.62 3. August, 20!2 28.0 23.83 274.4 2.53 4. September, 2012 31.58 23.90 402 3.0 5. October, 2012 30.48 22.22 19.6 3.48 6. November, 2012 29.73 15.20 4.8 2.86 7. December, 2012 27.9 12.88 0.0 2.52 8. January, 2013 27.9 12.58 8.1 3.2 9. February, 2013 30.25 15.2 19.8 3.83 10. March, 2013 34.85 17.73 7.6 5.3 11. April, 2013 39.08 23.52 3.6 8.04 12. May, 2013 44.25 30.65 0.0 11.75 Physico-chemical Properties The results of the analyses of the physical and chemical parameters that are important for assessing irrigation water quality are given in Tables 3 to 11. Table 3. Monthly analysis of TSS, TDS, BOD and COD of wastewater of river Nag Parameter TSS ( mg.l-1 ) TDS ( mg.l-1 ) BOD ( mg.l-1 ) COD ( mg.l-1 ) 200 2100 100 250 BIS limit Year Site PI. US. RTO. Gokul PI. US. RTO. Gokul PI. US. RTO. Gokul PI. US. RTO. Gokul June, 2012 33.3 26.00 29.000 33.330 365.70 352.3 288.53 339.30 49.44 51.47 34.930 54.110 119.50 222.5 143.57 214.90 July, 2012 33.3 26.00 29.000 33.330 317.07 339.4 289.53 351.70 22.92 29.13 17.690 29.460 91.670 114.7 68.330 104.33 Aug., 2012 15.3 11.00 15.000 15.330 293.93 289.6 296.23 294.50 81.93 82.03 115.60 131.92 314.33 308.3 443.33 517.00 Sept., 2012 19.0 11.33 16.000 19.000 307.73 314.3 312.60 345.27 94.93 82.93 84.230 91.330 349.00 299.7 306.00 445.33 Oct., 2012 13.0 6.00 15.670 13.00 307.73 314.1 312.67 345.40 24.55 40.92 32.75 114.89 98.00 151.7 119.67 410.33 Nov., 2012 14.0 8.00 14.500 14.00 298.50 326.2 271.03 318.93 29.91 14.44 7.80 20.270 113.67 54.00 29.67 74.670 Dec., 2012 15.3 10.00 15.000 15.33 308.50 323.8 298.17 326.00 49.91 49.71 37.49 72.370 183.67 187.3 141.67 274.67 Jan., 2013 14.6 11.67 13.000 14.67 288.90 306.7 269.13 323.00 47.43 58.57 45.50 63.000 172.67 213.7 158.00 244.67 Feb, 2013 18.6 15.60 16.670 18.67 289.00 306.3 269.00 323.00 54.85 70.08 83.21 66.070 199.67 238.3 200.00 247.00 March, 2013 15.6 15.67 17.000 15.67 325.53 312.7 279.00 324.83 76.01 78.70 67.67 65.030 271.67 286.0 241.67 232.33 April, 2013 22.6 26.67 21.330 22.67 347.37 348.4 298.00 346.33 88.81 89.22 74.77 79.470 308.33 305.7 260.00 267.33 May, 2013 34.1 31.19 34.960 34.12 359.63 359.3 317.13 406.07 25.67 24.33 95.00 20.670 100.08 111.0 424.33 77.330 Average 20.7 16.60 19.76 20.76 317.47 324.4 291.75 337.02 56.42 58.83 54.694 71.629 193.52 207.7 211.35 259.15 Note: PI – Plant inlet, US - Upstream, RTO - Road transport ofice, Gokul - Gokulpeth 34 JAE : 52 (1) A.R. Mhaske, S.M.Taley, Shinde, R.N. Katkar Table 4. Monthly analysis of N, P, turbidity, pH and EC of wastewater of river Nag Parameter N (mg.l-1) P ( mg.l-1) 45 10 BIS limit Month Site pH EC (dS.m-1) 6.0-8.5 0.75 -2.25 Tubidity ( NTU) PI US RTO Gokul PI US RTO Gokul PI US RTO Gokul PI US RTO Gokul PI US RTO. Gokul June 1.47 1.43 1.87 1.43 0.73 0.57 0.570 0.53 26.03 15.83 8.130 18.00 6.27 6.93 6.88 7.04 0.60 0.61 0.56 0.61 July 1.03 1.57 2.00 1.87 0.63 0.60 0.570 0.60 26.03 15.83 8.130 18.00 7.40 7.33 7.37 7.17 0.24 0.25 0.22 0.26 Aug 1.90 2.87 3.43 2.83 0.43 0.83 1.130 0.97 34.00 29.67 65.67 30.00 6.97 7.10 7.37 7.20 0.22 0.22 0.22 0.22 Sept 2.13 3.03 3.07 2.83 0.53 0.97 1.330 1.00 32.00 17.33 21.67 20.67 7.17 7.30 7.37 7.20 0.23 0.24 0.31 0.26 Oct 2.43 3.27 2.90 2.90 0.57 1.10 1.47 1.00 7.33 9.67 3.33 16.33 7.17 7.17 7.10 7.20 0.23 0.24 0.31 0.26 Nov 3.23 3.73 2.60 3.10 0.63 1.00 1.87 0.97 8.33 4.33 2.67 5.00 7.30 7.07 7.00 6.97 0.44 0.51 0.48 0.52 Dec 3.33 4.07 2.77 3.03 0.73 1.20 1.77 0.93 21.33 13.00 13.00 18.33 7.30 7.07 6.93 7.03 0.52 0.53 0.51 0.53 Jan 2.20 3.00 2.10 2.57 0.43 0.50 0.17 0.27 19.67 13.33 17.00 19.33 7.20 7.13 7.13 7.00 0.59 0.61 0.55 0.64 Feb 1.97 2.40 1.90 2.10 0.57 0.47 0.37 0.37 25.00 17.33 22.33 20.67 7.20 7.13 7.10 7.13 0.63 0.60 0.56 0.63 March 2.00 1.93 1.87 1.90 0.60 0.47 0.40 0.40 18.00 19.67 23.33 19.67 7.30 7.37 7.17 7.33 0.60 0.62 0.57 0.64 April 2.07 2.43 1.83 1.93 0.56 0.50 0.40 0.33 18.67 18.33 17.00 21.00 7.27 7.37 7.27 7.30 0.71 0.72 0.60 0.75 May 1.93 1.40 1.80 1.70 0.77 0.57 0.60 0.40 20.96 11.37 6.81 13.03 7.10 7.00 6.97 7.10 0.72 0.72 0.61 0.80 2.607 2.345 2.349 0.583 0.746 0.914 21.4 15.4 18.3 7.140 7.264 7.138 7.141 0.48 0.49 0.46 0.51 2.14 Ave 0.67 17.4 Note: PI – Plant inlet, US- Upstream, RTO- Road transport ofice, Gokul - Gokulpeth concentration at different locations. Concentration of TDS was observed more at Gokulpeth, followed by upstream, plant Inlet and RTO sites. The higher values of concentration of TDS at Gokulpeth as compared to other sites might be because of mixing of milk industry water with domestic sewage water, and it is further diluted at Upstream and Plant Inlet with addition of domestic sewage water. At RTO site, only domestic sewage water is discharged, hence lowest TDS concentration. Total Dissolved Solids (TDS) TDS plays an important role in plant growth, crop yield and quality of product (SFWF, 2002). As depicted in Fig.2, it is observed that all the point sources having higher values of TDS at Gokulpeth in the month May was up to 400 mg.l-1, might be due to high evaporation with increasing the ambient temperature. TDS was lower in the month of January due to low ambient temperature. Addition of city storm runoff did not show significant effect on TDS TDS 400 Upstream 380 Plant Inlet RT O Gokulpeth 340 320 1 mg l-1 360 300 280 July September November January March Months Month Fig.2: Trend of monthly TDS in sewage water at different locations 35 May January-March, 2015 Spatial Assessment of Wastewater Quality of Nag River for Irrigation The regression coefficient (R2) was found to be highest at Plant Inlet (0.9764) with minimum RMSE (6.566), Table 5. Low values of the RMSE and high values of the regression coefficient showed that the most of the data at different point sources was explained by best fitted curve. of organic matter through the city storm runoff. From the month of September, COD started decreasing up to about 100 mg.l-1at the Plant Inlet site till the month of November. From the month of November, slight increase in COD was observed till the month of May due to increase in ambient temperature. The overall pattern of spatial trend of COD at different sites were similar, with Gokulpeth site having higher COD, followed by RTO, Upstream and Plant Inlet sites. Higher concentration of COD at Gokulpeth might be due to mixing of milk industry water and the drain carrying discharge was of closed type, and sewage water was not coming in contact with soil and natural vegetation grown along the open drain. COD concentration was a restriction on the use of the raw sewage water for irrigation. Table 5. Statistical parameters for TDS in sewage water R2 Sl. No. Location p value RMSE 1 Plant Inlet 0.9 0.9764** 6.566 2 Upstream 0.9 0.9482** 7.325 3 R. T. O. 0.9 0.854** 10.83 4 Gokulpeth 0.9 0.8987** 13.32 ** Signiicant at 1% level of signiicance In case of COD, highest R2 was observed for Upstreamsite, followed by RTO, Plant inlet and Gokulpeth sites, respectively, suggesting that there was good fitting of the curve and explained the data variation sufficiently, Table 6. Higher values of RMSE suggested more error for smoothing fit. Chemical Oxygen Demand (COD) From Fig. 3, the spatial trend of COD in sewage water was observed to be increasing from the month of June to September. Maximum value of COD was about 500 mg.l-1at Gokulpeth site during the month of August. Higher COD concentration was thus in the rainy season due to addition 500 COD Plant Inlet Upstream 450 RTO 400 Gokulpeth mg l-1 mg L 350 300 250 200 150 100 July September November January May March Month Fig. 3: Spatial trend of monthly COD in sewage water Table 6. Statistical parameters for COD in sewage water Sl. No. Location p-value R2 RMSE 1 Plant Inlet 0.9 0.8972** 43.53 2 Upstream 0.9 0.9254** 27.21 3 R. T. O. 0.9 0.9172** 43.38 4 Gokulpeth 0.9 0.8539** 66.45 ** Signiicant at 1% level of signiicance 36 JAE : 52 (1) A.R. Mhaske, S.M.Taley, Shinde, R.N. Katkar 130 BOD Plant Inlet 110 Upstream 100 RT -1 mg mglL 120 Gokulpeth 90 80 70 60 50 40 30 July September November January March May Months Month Fig.4: Spatial trend of monthly BOD in sewage water Biological Oxygen Demand (BOD) The spatial trend of BOD in sewage water was observed to increase from the month of June till September, Fig. 4. Highest BOD (120 mg.l-1) in sewage water was at Gokulpeth site in the month of August. Higher BOD in the rainy season might be for the same reason as in the case of COD. Between September and November, BOD showed decreasing trend, with BOD of 25 mg.l-1 at RTO site. BOD started increasing from November till the month of May, due to increasing ambient temperature (Nashikkar et al., 1998). The overall trend of BOD variation was similar at all sites; with higher concentration at Gokulpeth site, followed by Upstream, Plant inlet and RTO sites. The reason for higher concentration of BOD at Gokulpeth site is as explained in case of COD. BOD thus was a restriction on the use of the raw sewage water for irrigation. p value R2 RMSE 1. Plant Inlet 0.9 0.8772** 13.41 2. Upstream 0.9 0.9372** 7.70 0.9 0.8923** 14.3 4. Gokulpeth 0.9 0.7025** 24.87 Ammoniacal Nitrogen (N) The spatial trend of monthly N content showed an increasing trend between the month of June and October, and a reverse trend from the month of November till May, Fig. 5. The increasing trend of ammoniacal nitrogen content might be due to the addition of soil residues from the agricultural catchment areas by storm runoffs. Higher N concentration during the winter season might be due to lower ambient temperature, allowing preservation of ammoniacal N in sewage water. Highest N content (3.2 mg.l-1) was observed in the upstream site in the month of October. Decrease in N concentration from the month of December might be due to increase in ambient temperature till May causing the release of ammonia gas from the sewage water. N concentration was higher at the upstream site, followed by Gokulpeth, RTO and Plant Inlet sites. Higher concentration at Upstream site might to be due accelerated nitrification process as compared to other sites. Statistical data suggested that most of the data variation was explained with good fitting of the curve for N concentration (Table 8). Table 7. Statistical parameters for BOD in sewage water Location R. T. O. ** Signiicant at 1% level of signiicance Statistical values of regression coefficients of the trends are given in Table 7. Higher values of RMSE suggested that more error has been taken to filter data of BOD for fitting of the curve. Sl. No. 3. 37 January-March, 2015 Spatial Assessment of Wastewater Quality of Nag River for Irrigation N 3.2 Plant Inlet 3 Upstream 2.8 RTO 2.6 Gokulpeth mgmgl-1L 2.4 2.2 2 1.8 1.6 1.4 1.2 September July November January March May Month Fig.5: Spatial trend of monthly N content in sewage water Table 8. Statistical parameters for N in sewage water Sl. No. Location P value R2 RMSE 1 Plant Inlet 0.9 0.9218** 0.169 2 Upstream 0.9 0.9255** 0.263 3 R. T. O. 0.9 0.9732** 0.118 4 Gokulpeth 0.9 0.9581** 0.157 ** Signiicant at 1% level of signiicance P 1.8 Plant Inlet Upstream 1.6 RTO 1.4 Gokulpeth mg l-1 mg L . 1.2 1 0.8 0.6 0.4 0.2 July September November Months January Month Fig. 6: Monthly spatial trend of P in sewage water 38 March May JAE : 52 (1) A.R. Mhaske, S.M.Taley, Shinde, R.N. Katkar Table 9. Statistical parameters for P in sewage water Sl. No. Location p value R2 RMSE 1. Plant Inlet 0.9 0.8165** 0.076 2. Upstream 0.9 0.9226** 0.123 3. R. T. O. 0.9 0.9309** 0.255 4. Gokulpeth 0.9 0.9470** 0.113 ** Signiicant at 1% level of signiicance followed by Plant Inlet, RTO and Upstream sites (Table 3). No significant difference of TSS was observed in the spatial trend. Phosphorus (P) Fig. 6 revealed that monthly spatial trend of monthly phosphate load was found to be increasing during the month of July and December, while a reverse trend was visible between December and January, and subsequently remained constant up to the month of May. The initial increasing trend might be due to addition of storm runoffs from the agricultural farms adjoining to the Nag river. Decreasing trend of P load from the month of December to February might be due to precipitation of PO4++ ions with Ca++ because of increase of concentration of sewage. The concentration of P was observed to be higher at RTO (1.8 mg.l-1), followed by Upstream, Gokulpeth and plant inlet sites. Statistical results (Table 9) indicted that highest regression coefficient of 0.9470 with low values of RMSE for all point sources, were sufficiently expressed with good fits. Electrical Conductivity (EC) Electrical conductivity of water is governed by the presence of salt.It indicates the soluble salt concentration in water as given by SFWE (2002). Low EC values were observed during the rainy season because of dilution of sewage water by addition of city storm runoffs. EC increased linearly from October to May, which might be due to increased concentration of salt due to decrease in flow rate and increase in ambient temperature. There was no significant change in the spatial trend of the EC. The mean value of EC was found to be higher at Gokulpeth, followed by Upstream, Plant Inlet and RTO sites. pH Turbidity It is an indicator of acidity or alkalinity of water. Irrigation water should have pH between 6.5 and 8.4. pH at all point sources ranged between 6.8 to 7.4, and within acceptable range. pH values showed increasing trend between the month of July and September (rainy season) might be due to addition of rain water, which is slightly alkaline in nature. Further slight increase in pH was observed between October and April, possibly due to increasing use of detergent in surrounding habitats as also increase in ambient temperature causing evaporation of sewage. pH was found to be higher in the Upstream, followed by Gokulpeth, Plant Inlet and RTO sites. Turbidity increased during rainy season (July to September) due to addition of storm water runoffs from the catchment areas. Lowest turbidity was observed during OctoberNovember due to non-addition of the city storm runoff and low ambient temperature. Between November and April, it constantly increased due to decrease of flow rate and increase in the temperature. Erratic peak observed at RTO source point might be due to city storm runoff during sampling. The mean value of turbidity was found to be higher at Plant Inlet, followed by Gokulpeth, RTO and Upstream sites. Total Soluble Solids (TSS) Seasonal concentration of micronutrients like Zn, Fe and Cu in domestic sewage water is mainly due to rusting of metals, plumbing, wood preservatives, roof runoff, cosmetic material, construction material, fungicide etc. as given by Ibrahim and Salaman (1992).The analysis of Zn, Fe and Cu in seasonal trend of sewage water showed (Table 10-12) concentration well below the standard limits given by BIS. However, Mn was found to be in higher concentration than the safe limit, which can cause health hazards if the sewage water is used for irrigation. Main sources of Mn were oil Micronutrients -1 TSS values were higher (up to 35 mg.l ) in the months of May and June. TSS concentration was lowest in the month of October, and intermediate concentration between March and April. The pattern of concentration was likely governed by the ambient temperature. Because of higher temperature in the months of May and June, the evaporation rate was very high, thereby increasing the concentration of the suspended solids in the sewage water. The mean value of TSS in sewage water was higher at Gokulpeth, 39 January-March, 2015 Spatial Assessment of Wastewater Quality of Nag River for Irrigation Table 10. Spatial trend of monthly average concentration of micronutrients and heavy metals in the Nag river sewage water in rainy ((June-Sept) season Sl. No. 1. 2. 3. 4. 5. 6. Parameter Unit Safe limit Location Plant Inlet Up Stream RTO ofice Gokulpeth Gokul Zn Mg.l-1 2.00 0.207 0.128 0.093 0.103 Fe -1 5.00 0.240 0.213 0.221 0.204 -1 0.20 0.032 0.013 0.016 0.025 -1 0.20 0.275 0.155 0.368 0.367 -1 0.05 0.009 0.006 0.006 0.013 -1 0.01 -1 Cu Mn Co Cd Mg.l Mg.l Mg.l Mg.l Mg.l 0.019 0.023 0.019 0.028 7. Cr Mg.l 0.10 0.015 0.007 0.011 0.016 8. Pb Mg.l-1 5.2 0.046 0.043 0.043 0.044 Table 11. Spatial trend ofmonthly average concentration of micronutrients and heavy metals in the Nag river sewage water in winter (Oct– Jan) season Sl. No. 1. 2. 3. Parameter Unit Safe limit Location Plant Inlet Up Stream RTO ofice Gokulpeth Zn Mg.l-1 2.0 0.136 0.108 0.119 0.117 Fe -1 5.0 0.145 0.123 0.099 0.104 -1 0.2 -1 Cu Mg.l Mg.l 0.015 0.012 0.010 0.010 4. Mn Mg.l 0.2 0.149 0.129 0.120 0.116 5. Co Mg.l-1 0.05 0.0199 0.013 0.014 0.020 Cd -1 0.01 0.006 0.006 0.006 0.005 -1 6. Mg.l 7. Cr Mg.l 0.1 0.005 0.006 0.006 0.006 8. Pb Mg.l-1 5.20 0.053 0.047 0.042 0.043 Table 12. Spatial trend of monthly average concentration of micronutrients and heavy metals in the Nag river sewage water in summer (February-May)season Sl. No. 1. 2. 3. Parameter Unit Safe limit Location Plant Inlet Up Stream RTO ofice Gokulpeth Gokul Zn Mg.l-1 2.0 0.156 0.117 0.120 0.112 Fe -1 5.0 0.077 0.067 0.066 0.080 -1 0.2 0.020 0.019 0.016 0.019 -1 Cu Mg.l Mg.l 4. Mn Mg.l 0.2 0.224 0.222 0.204 0.216 5. Co Mg.l-1 0.05 0.005 0.007 0.006 0.005 Cd -1 0.01 -1 6. Mg.l 0.005 0.007 0.008 0.006 7. Cr Mg.l 0.1 0.005 0.006 0.007 0.006 8. Pb Mg.l-1 5.2 0.045 0.035 0.041 0.046 40 JAE : 52 (1) A.R. Mhaske, S.M.Taley, Shinde, R.N. Katkar REFERENCES and lubricants. The results thus indicated that presence of higher concentration of Mn would be a restriction on the use of raw water for irrigation. Afzal S; Ahmad I; Younas M; Zahid M D; Atique K M H; Ijaz A; Ali K. 2000. Study of water quality of Hudiara Drain, India-Pakistan. Environ. Int., 26(1-2), 87-96. doi:10.1016/S0160-4120(00)00086-6 Similarly, Table 10-12 indicated that the concentration of heavy metal like Cd generated through rechargeable batteries, storm water, pesticides, gardening products was higher than the safe limit, and a restriction on the use of raw water. This may cause harmful effect on the vegetables and crops grown by utilizing this water for irrigation without secondary treatment (Ibrahim and Salamon, 1992; Kahlown et al., 2006). However, Co generated through medicine, food products, ointments, paints and pigments was within permissible limit, and did not pose any restriction on direct use of the raw water. Akhtar S; Mohammad N. 2012. Impact of water quality on aquatic life in river Ravi. Pakistan J. Nature Environ. Pollution Tech., 11(2), 219-224. APHA. 1985. APHA (American Public Health Association), Standard methods for the examination of water and wastewater, 19th ed., Washington, DC. Ensink J H J; Van der Hoek W; Simmons Robert W. 2008. Livelihoods From Wastewater: Water Reuse in Faisalabad, Pakistan. IWA Publishing, London, pp. 387400. Heavy metals like Cr generated through phosphate fertilizers and metallurgic industries cause atmospheric deposition. It is also released by tanning, ink manufacture, metal plating, dyes, wood preserving, textile and ceramic industries (Thornton et al., 2006). The sources of Pb in the river water are cleaning products, fire extinguisher, lubricants, health supplement, oil and lubricant, paints and pigments, photo graphics, pesticides and gardening products, etc. Their concentrations were within limits. Hamid Almas; Zeb M; Mehmood A; Akthar S; Saim S. 2013. Assessment of Wastewater Quality of Drains for Irrigation. J. Environ. Protection, 4, 937-945. Ibrahim M; Salamon S. 1992. Chemical composition of Faisalabad city sewage efluent: II Irrigation Quality.J. Agric. Res., (30), 391-340. Kahlown A M; Ashraf M; Hussain M; Salam A H; Bhatti Z A. 2006.Impact assessment of sewerage and industrial efluents on water resources, soil, crops and human health in Faisalabad. Pakistan Council of Research in Water Resources, Lahore, pp: 1-105. Fecal colli presence in the river water was found to be more than 1100 coliform per 100 ml of water in all samples. With FAO recommendation of 100 coliform per 100 ml of water for safe irrigation, the present concentration in the river water does not permit its use for irrigation purposes. Mara D; Cairncross S. 1989. Guidelines for safe use of wastewater and excreta in agriculture and aquaculture. W.H.O, pp:205. CONCLUSIONS Naeem E. 2009. An investigation of the characteristics of efluent mixing in stream. University of Engineering and Technology, Taxila, pp: 144-146. The Nag river flowing through Nagpur city was originally used as storm water drain. But due to increase in urban population and rapid industrialization in the city, the drains are now mainly used to collect the industrial and municipal effluents of diverse nature. The characterization of the waste water of these drains showed that the concentrations of some parameters were higher than the prescribed limits by BIS and FAO for irrigation. These results provide strong evidence that the raw sewage water of these drains was not suitable for irrigation and other domestic purposes. An urgent need exists for treating the domestic and industrial waste water before discharging into these drains so that pollution load can be minimised and the water can be safely used for beneficial purposes. Nashikkar V J; Shende G B. 1998. Effect of long term varying levels of BOD of irrigation waters on crops. Resour. Conserv. Recycling, 1,131-136. Page A L; Miller R H; Keemey D R. 1982. Methods of Soil Analysis. 2nd Edn., Amer. Soc. Agron., Madison, WI., USA. Pay D; Christopher A S; Raschid S L; Mark R; Akiça B.2010. Wastewater irrigation and health: Assessing and mitigating risk in low-income countries. In: Non-Pathogenic Trade-Offs of Wastewater Irrigation, Earths-can, London, pp: 101-126. 41 January-March, 2015 Spatial Assessment of Wastewater Quality of Nag River for Irrigation Sahu K R; Katiyar S; Tiwari J; Kisku C G. 2007. Assessment of drain water receiving efluent from tanneries and its impact on soil and plants with particular emphasis on bioaccumulation of heavy metals. J. Environ. Bio., 28 (3), 685- 690. Reference Centre for Waste Disposal, Duebendorf, Germany. Tariq M; Ali M; Shah Z. 2006. Characteristics of industrial efluents and their possible impacts on quality of underground water. Soil Environ., 25(1), 64-69. SFWF. 2002. TDS and pH. Organization of Safe Drinking Water, Canada, pp: 1-6. Thornton I; Butler D; Docx P; Hession M; Makro-poulos C; McMullen M; Nieuwenhuijsen M; Pitman A; Rautiu R; Sawyer R; Smith S; White D. 2006.Pollutants in urban wastewater and sewage sludge. Final Report, European Union, London, pp: 1-244. Sharma K R; Agrawal M; Marshall F. 2007.Heavy metal contamination of soil and vegetables in suburban areas of Varanasi, India. Ecotoxicology Environ. Safety, 66(2), 258266. doi:10.1016/j.ecoenv.2005.11.007 WWF. 2007. Pakistan’s waters at risk: Water & Health Related Issues in Pakistan & Key Recommendations: A Special Report. WWF, Lahore, pp: 1-25. Strauss M; Blumenthal U. 1990. Human Waste Use in Agriculture and Aquaculture: Utilization Practice and Health Perspectives. IRCWD Report 09/90, International 42 JAE : 52 (1) Shishir Kumar Verma, A. K. A Lawrence, A. K. Tripathi and K. K. Patel Journal of Agricultural Engineering, Vol. 52 (1): January-March, 2015 Assessment of Biofuel Blends Using Diesel-Biodiesel and Alcohol Shishir Kumar Verma1, A. K. A Lawrence1, A. K. Tripathi1 and K. K. Patel2 Manuscript received: April, 2013 Revised manuscript accepted: February,, 2015 ABSTRACT The main characteristics fuel properties of diesel, jatropha curcus oil and fuel prepared from blends of diesel- jatropha curcus oil and diesel-jatropha curcus oil-ethanol in various proportions were studied. The relative density, API gravity, kinematic viscosity, gross heat of combustion, cloud and pour point, flash and fire point, carbon residue, ash content and total acid values were recorded and compared with diesel to study their compatibility as compression ignition engine fuel. The characteristic fuel property of jatropha curcus-diesel in 90:10 proportion was found to be close to that of diesel. The fuel properties of diesel – ethanol blends in 75:25 proportion was also found to be close to that of diesel. However, the other fuels obtained from blending jatropha curcus-diesel as well as from jatropha curcus-diesel-ethanol in various proportions deviated from diesel when higher proportions of jatropha curcus oil were blended with diesel. Key word : Jatropha surcus, diesel, ethanol, Fuel, Fuel properties exhaust emissions have necessitated the substitution of fossil fuels with less polluting and easily available renewable fuels for use in IC engines. Keeping these facts in view, researchers and environmentalists are working in coordination to conserve fossil fuels and explore renewable or bio-energy resources. Several alternative fuels such as biodiesel, ethanol, methanol, hydrogen, CNG, LNG, producer gas, biogas, vegetable oils etc. are being explored around the globe to supplement fuels and reduce environmental pollution. Hydrogen, CNG, LNG and LPG have met with limited success owing to considerable technological glitches and logistic bottlenecks. Renewable alternative liquid fuels (such as biodiesel) are inding prominent place in search for alternative energy sources. Since biodiesel is a biodegradable, sustainable and clean energy source (Atadashi et al., 2011) deined as the mono-alkyl esters of vegetable oils or animal fats, it is an alternative to diesel fuel that is being accepted in a steadily growing number of countries around the world (Knothe, 2005; Reddy, 2013). Global demand for biodiesel production may rise by 2 MMT to 2.1 MMT, or about 8%, to 29.1 MMT in 2014 as per the Hamburg-based oilseed industry researcher (Ruitenberg, 2014). The large demand (about 78-80%) of import of petroleum products has been a major factor of decelerated rate of economic development of India (Anon, 2015). The current domestic production of crude oil is around 32-34 MMT per year, whereas the estimated demand of petroleum products during 2016-17 would be 186.2 MMT (IANS, New Delhi, 2013). This clearly indicates the burden of import and outflow of foreign exchange for petroleum products (De Gorter et al., 2013). The consumption of commercial energy sources such as coal, oil, gas etc. is doubling every ten years in most of the developing countries. The development path of any society depends on the availability of energy sources and the further growth of energy and emission trajectories. It would also be greatly influenced by technological development, economic cooperation between countries, and global cooperation in limiting greenhouse gas emission. Climate change is basically rooted in this core aspect of economic development process. India is the world’s sixth largest and second fastest growing producer of greenhouse gases (Pandey, 2006). In coming years, not only India but also the world will face great challenges of energy crisis, food shortage and environmental pollution due to growing population, expanding urbanization and rapid industrialization. Bio-fuels like ethanol and bio-diesel are environmentfriendly, and help us to conform to the strict emission norms. Among alcohols, ethanol has been recognized as the most prospective alcohol fuel. In addition to ethanol, methanol has also been used as alternate fuel, but ethanol Energy consumption is an indication of the vibrancy of economy with oil and gas playing a key role in every production sector. Limited stock of fossil fuel causing rising prices of petroleum fuels and stiff regulations on 1 Department of Farm Machinery and Power Engineering, Sam Higginbottom Institute of Agriculture, Technology & Sciences (Formerly Allahabad Agricultural Institute), Allahabad; 2 Department of Post Harvest Engineering and Technology, Aligarh Muslim University, Aligarh-202002, e mail address: k_krishna_374@yahoo.co.in 43 January-March, 2015 Assessment of Biofuel Blends Using Diesel-Biodiesel and Alcohol is more popular as it is a biologically renewable resource, easily produced by fermentation, non-corrosive, non-toxic, has higher heat of combustion, less volatility and provides better water tolerance than methanol. It has also a proven track record as an automobile fuel and is used as octane enhancer and oxygenate. India being an agricultural country has potential of ethanol production of 2512 ML mainly from sugarcane molasses during sugar session 2012-13 (Jha et al., 2012). The Government of India in October of 2007 had set a 20% ethanol blend target for gasoline fuel to be met by 2017 to increase its energy security and independence. However, India had already blended ethanol to the tune of 30 crore litres till April, 2011 out of the 71 crore litres of the required target till 31st October 2011 (Desai, 2015). Currently, India has only 5-6 pilot plants (capacity 10,000 to 250,000 MT per year with the installed capacity to produce 115-130 ML) operating in the country producing biodiesel (Agro Chart, 2014), and it is estimated that India will be able to produce 550 ML of ethanol in calendar year 2014, indicating that ethanol would make up about 2.1% of India’s fuel market. methyl transesterification process parameters of neem oil and characterization of its fuel properties. Jatropha curcus curcas L. (Ratanjot) hitherto considered as a wild oilseed plant of the tropics is now being credited as a most promising bio-fuel crop. Jatropha curcus oil is odourless and colourless when fresh, but becomes yellow on standing. Jatropha curcus oil has high viscosity compared to diesel. The high viscosity of Jatropha curcus curcas oil may contribute to the formation of carbon deposits in engines, incomplete fuel combustion and may result in reducing the life of an engine. The main objective of the present research was to study the properties of stable blends prepared using diesel, jatropha curcus oil and anhydrous alcohol. MATERIALS AND METHODS The experiments were carried out using high speed diesel marketed by Indian Oil Corporation in accordance with IS: 1460-1974 as reference fuel, Jatropha curcus L. (Ratanjot) seed oil and anhydrous ethanol. Anhydrous ethanol (200 0 proof), of Merck make, Germany was procured from the local market and ethanol proofs of 200 0, 190 0, 180 0 and170 0 were prepared from anhydrous ethanol by adding 0%, 5%, 10% and 15% of distilled water, respectively. By using different proportions of Jatropha curcus L. (Ratanjot) seed oil and ethanol (anhydrous), various blends were prepared with diesel. The quality of biodiesel depends on its fuel properties that need less engine modification and can be used in compression ignition engines. For instance, alcohols have high heat of vaporization resulting in excessive cylinder cooling, improper air-fuel mixing, long ignition delay and misfiring making the direct substitution of alcohols quite impossible. In addition, direct substitution of ethanol for diesel would require major redesign of the engine to accommodate the low cetane rating of ethanol. Fuel modification method includes use of alcohol derivatives such as esters, diesel-alcohol blends and emulsions (Koganti et al., 2004). However, ethanol-diesel blends pose many problems such as lowered cetane number, lowered viscosity, etc., but phase separation at lower temperatures or water contamination is most serious limitation (Nayyar, 2010). The water tolerance of blends increases with temperature. If water tolerance can be increased, it would be possible to use lower proof of ethanol for blending with diesel and edible vegetable oils. However, the price of edible vegetable oils is higher than that of the diesel fuel. So, in place of such oils, use of waste vegetable oils (Yu et al., 2002; Sudhir et al., 2007; Ramadhas et al., 2010; Melero et al., 2012; Kumar, 2013) and non-edible crude vegetable oils (Bari, 2002, Pramanik, 2003; Tiwari et al., 2007; Naik et al., 2008; Sahoo et al., 2009; Deng et al., 2011; Rita, 2012; Bolbade and Khyade, 2012; Gashew and Lakachew, 2014) had been considered as potential alternative fuels. In addition, Vashist and Ahmad (2009) have explained the energetic feasibility of Jatropha curcus biodiesel in terms of energy ratio and Ragit et al. (2011) have standardized A total of 49 blends were prepared manually using he different proportions of diesel, Jatropha curcus L oil and anhydrous ethanol (1900, 1800, 1700). Among them, nine samples (Table 1) were sorted out for the present research on the basis of higher stability. The stability or instability of blends was classified on the basis of visual observation. A stable blend is a clear, homogeneous and transparent solution with no sign of phase separation; whereas an unstable blend looks turbid, non-homogeneous and opaque right since the time of formation. These characteristics of the blend do not change even after keeping them for a long period of time. Fuel Property Analyses Relative density and API gravity Relative density of fuel was evaluated at 15°C temperature in accordance with Bureau of Indian Standards IS: 1448 [P: 32]:1992. To maintain the temperature of fuel samples at 15°C, the measurement was recorded in a control chamber (Saveer Biotech walk-in-type) and the relative density was calculated using equation (1): Relative density = 44 Pf Pw ............. (1) JAE : 52 (1) Shishir Kumar Verma, A. K. A Lawrence, A. K. Tripathi and K. K. Patel Where, Where, 0 Hc = ∆T Heat of combustion of fuel sample, Cal.g-1, -3 Pw = Density of distilled water at 15 C (0.9904 g.cm ), and Wc = ∆T Water equivalent of calorimeter assembly, (2883.32), Cal.°C-1, PT = Density of fuel at 150C, g.cm-3. ∆T = Rise in temperature, °C, and Subsequently, the gravity of a fuel was calculated using the equation 2 (ASTM D-287) for different ethanol proofs and blends as: Fuelgravity (API Degrees) = Ms = Mass of sample burnt, g. 141.5 − 131.5 Relative density o f fuel a t 150 C Cloud and pour point Cloud and Pour point were measured using method described by IS: 1448 [P: 10]: 1970 with the help of Cloud and Pour point apparatus. It consisted of 120 mm high glass tubes of 30 mm diameter. These tubes are enclosed in an air jacket, filled with a freeze mixture of crushed ice and sodium chloride crystals. For cloud point, at every 10C interval, as the temperature fell the glass tube containing fuel sample was taken out from the jacket and inspected for cloud formation. The point at which a haze was first seen at the bottom of the sample was taken as the Cloud point. ... (2) Kinematic viscosity Kinematic viscosity of the fuels was determined at 38°C using a Redwood Viscometer No.1 (Make: Toshniwal) and calculated as following (Nakra and Chaudhary, 1985): υ = At − B t … (3) υ = Kinematic viscosity, cSt, For Pour point, samples were pre-heated to 480C and then cooled to 350C in air before it was filled in the glass tube. Thereafter, the cooled samples were placed in the apparatus and withdrawn from the cooling bath at 10C temperature interval for checking its flow ability. The Pour point was taken to be the temperature 10C above the temperature at which no motion of fuel was observed for five seconds on tilting the tube to a horizontal position. Three replications were made for each fuel type. Where, t = Time of efflux, s (or degrees for Engler viscometer), and A, B = Constants applicable to type of viscometer. The commonly accepted values of A are 0.26, 0.22 and 1.47 and of B are 172, 180 and 374 for different viscometers such as Redwood, Saybolt and Engler, respectively. The value of A as 0.62, and the value of B as 172 for Redwood viscometer was used in the research. However, the working principle of the apparatus was based on the time of gravity flow in seconds of a fixed volume (50 ml) of liquid through a specified hole in an agate piece (as per IP 70 / 62 issued by Institute of Petroleum, London) (BIS, 1976). Flash point and ire point Using Bureau of Indian Standards IS: 1448 [P: 21]: 1992, Flash and Fire points of all fuel blends were determined using a Pensky Martens apparatus. The apparatus had a fuel cup of 50 mm diameter and 55 mm depth. The fuel to be tested was placed in the cup up to the mark and heated indirectly at 5°C per minute by rising heated air bath. After every 1°C temperature rise, flame was introduced for a moment with the help of a shutter and the temperature at which a flash appears in the form of sound and light was recorded as the Flash point. The procedure was continued until fuel vapour caught fire and the flame sustained for a minimum of five seconds. This temperature was recorded as the Fire point of the fuel. Gross heat of combustion (ghc) Gross heat of combustion, or calorific value, was estimated as per Bureau of Indian Standards (IS: 1448 [P: 10]: 1984) using an Isothermal Bomb Calorimeter. Fuel sample (1 ml) was burnt in the calorimeter bomb in presence of pure oxygen. The rise in temperature was measured. The water equivalent (effective heat capacity of system) was also determined using pure and dry benzoic acid as test fuel. The heat of combustion of the fuel samples was calculated as: Hc = Wc ∆T Ms … Ash content The ash content of diesel, jatropha curcus oil and its blends with diesel was measured as per the standard ASTM D482IP 4 of Institute of Petroleum, London using an electric muffle furnace (Wiswo make). Known weight of a sample (4) 45 January-March, 2015 Assessment of Biofuel Blends Using Diesel-Biodiesel and Alcohol was burnt at 775 ± 250C for 2 h, cooled in desiccators to room temperature, weighed and ash content calculated as following: As = Wa ×100 Ws … At = (5) … (7) Where, At = Total acidity, mg KOH.g-1, Where, V = Volume of potassium hydroxide solution, ml, As= Ash content, percent, N = Normality of potassium hydroxide solution, and Wa= Weight of ash, g, and W = Weight of sample, g. Ws= Weight of sample, g. All measurements were carried out in triplicate, and the average values reported. Carbon residue Carbon residue of different fuels was determined using a carbon residue apparatus in accordance with the ASTM D189–IP 13 of Institute of Petroleum, London. The percentage of carbon residue of samples was calculated as: Where, 5 6.1 N × V W Wc Cr = × 100 Ws … RESULTS AND DISCUSSION Relative Density and API Gravity The relative density and API gravity of diesel, different proofs of ethanol and ethanol blends fuels recorded at 15°C are presented in Table 1. Relative density of diesel was 0.838. Goering et al. (1983) reported value of 0.840, and similarly 0.8291 by Chatterjee (2000) and 0.846 by many researchers. (6) Cr = Carbon residue, %, Wc = Weigh of carbon residue, g, and However, relative density of Jatropha curcus oil was recorded as 0.9182, little higher (9.5%) than diesel. The relative density of diesel increased from 0.838 to 0.879 % as the per cent of Jatropha curcus oil blend increased from 0% to 30 per cent. This increase was slightly retarded when anhydrous ethanol was mixed with diesel and Jatropha Ws = Weight of sample, g. Acid value Total acid value of different fuel samples was measured as per ASTM D974–IP 1/64 of Institute of Petroleum, London, and calculated as: Table 1. Properties of different blends formulated with diesel, Jatropha curucus oil and anhydrous ethanol S. No. Blend [Diesel: Jatropha curcus oil: Ethanol (anhydrous)] Relative density, kg.cm-3 API gravity 1. 100:0:0 0.838 2. 0:100:0 0.918 3. 75:0:25 4. 90:10:0 5. Kinematic viscosity, cSt Gross heat of combustion, MJ.kg-1 Cloud point (0C) Pour point (0C) 38.74 3.07 44.9 5 -1.3 22.54 31.15 42.8 1 -6.5 0.829 37.78 2.51 47.91 4 3.0 35.0 0.855 34.75 3.66 49.16 3 -1.8 58.5 80:20:0 0.860 32.92 4.30 48.34 2 -3.4 6. 70:30:0 0.879 31.33 5.77 47.33 1.3 -5.0 7. 70:20:10 0.848 37.44 3.90 45.94 1.8 8. 60:30:10 0.861 34.75 4.94 44.27 1.3 9. 50:40:10 0.869 33.21 5.25 43.66 10. 40:40:20 0.876 31.82 6.26 11. 0:85:15 0.884 28.45 15.2 Fire point (0C) Carbon residue (%) Ash content (%) Total acidity (mg. KOH.g-1) 58.0 64.5 0.142 0.0009 0.23 236.0 251.5 4.850 0.165 2.24 41.0 0.101 0.0035 0.43 64.7 0.1086 0.0052 0.47 59.5 65.5 1.998 0.0096 0.77 60.0 67.5 2.332 0.0120 0.89 -3.5 44.0 50.0 0.732 0.0033 0.51 -3.5 46.0 54.0 1.449 0.0090 1.29 1.4 -4.5 48.0 56.0 1.901 0.0105 1.70 42.42 1.0 -6.8 49.0 56.5 2.987 0.0140 2.05 44.47 4.5 -8.3 50.0 57.5 3.976 0.0195 2.08 46 Flash point (0C) JAE : 52 (1) Shishir Kumar Verma, A. K. A Lawrence, A. K. Tripathi and K. K. Patel oils in the fuel lines. Thus, the viscosity is not a major/ unsolvable problem, and neat vegetable oil is still used as alternative fuels for diesel engines in countries as cold as Germany and Ireland. curcus oil. The maximum relative density was recorded for blend of 0 % diesel, 85% Jatropha curcus oil and 15% ethanol (anhydrous) fuel (Table 1). But, when diesel and ethanol (anhydrous) were blended in 75:25 ratio, the relative density of the blend was noticed to be 2.1 % lower than that of diesel. The results indicated that the Jatropha curcus oil, as well as all the blends had kinematic viscosity higher than that of diesel, except the blend of diesel-anhydrous ethanol prepared in 75:25 proportion. This blend was found to have kinematic viscosity of 18.29 cSt, lower than diesel. However, the blends of diesel- Jatropha curcus oil and diesel- Jatropha curcus oil - anhydrous ethanol prepared at the selected proportions had kinematic viscosity within the permissible range of 2.0 to 7.5 cSt (IS : 1460-1974), except for the blend of 85% Jatropha curcus oil and 15% anhydrous ethanol. Wrage and Goering (1980) have reported kinematic viscosity of anhydrous ethanol, diesel and blend of dieselethanol (80:20) as 1.11, 2.46 and 1.83 cSt, respectively. On the basis of observations and the standard proposed by IS: 1460-1974, it was found that most of the selected blends had their kinematic viscosity within the specified range. The observations on API gravity showed that blends containing higher proofs of ethanol had higher API gravity compared to the blends prepared from ethanol of lower proofs. This was because higher proof ethanol had lower relative density than that of lower proof ethanol. The API gravity of fuel types also indicated that all the blends, different ethanol proofs and Jatropha curcus oil were lighter than diesel fuel. Among the fuel blends, maximum API gravity was recorded for blends 75:0:25 and 70:20:10, as 37.78 and 37.44, respectively (Table 1). Kinematic Viscosity Viscosity of a fuel affects its atomization and injection system lubrication upon injection into the combustion chamber that leads the formation of engine deposits. Thus, higher the value of viscosity, greater will be the tendency of fuel to cause such problems as high fuel viscosity reduces the fuel amount vaporised prior to combustion (Owen and Coley, 1995). The kinematic viscosity of diesel was recorded as 3.07 cSt (32.7 Redwood seconds). Boruff et al. (1982) had observed kinematic viscosity of diesel as 2.46 cSt. Some researchers have reported kinematic viscosity of diesel fuel at 38 0C between 1.6 and 7.5 cSt, and minimum kinematic viscosity was recorded as 2.51 cSt for 75% diesel and 25 % ethanol (anhydrous) when no Jatropha curcus oil was used. Gross Heat of Combustion (GHC) Gross heat of combustion of fuels shows the capacity of heat production within the engine that enables the engine to do work. Diesel had 44.9 MJ.kg-1 of gross heat of combustion, and was in agreement with the findings reported by Goering et al. (1981), Hansen et al. (1989), Chatterjee (2000) as 43.57 MJ.kg-1, 44.96 MJ.kg-1, 45.12 MJ.kg-1, respectively. However, the gross heat of combustion of jatropha curcus oil and anhydrous ethanol (2000 proof) was found to be 42.8 MJ.kg-1 and 30.66 MJ.kg-1, and slightly differed from the findings reported by Kumar et al. (2002) as 39.77 MJ.kg-1 for Jatropha curcus oil. With 0% anhydrous ethanol, as the blending of Jatropha curcus oil with diesel (100 to 70%) increased from 0 to 30%, the kinematic viscosity also increased from 3.07 to 5.77 cST (Table 1). Pramanik (2003) had reported that Jatropha curcus oil and diesel blend in proportion of 20:80 had kinematic viscosity of 6.93 cSt. However, when 10% anhydrous ethanol was mixed with diesel, kinematic viscosity increased from 3.90 to 4.94, and then to 5.25 with increase in proportion of Jatropha curcus oil (from 20% to 30% and 40%), and was less than 0% mix anhydrous ethanol. Maximum kinematic viscosity of 31.15 cSt was noted for Jatropha curcus oil, Table 1. However, according to Knothe (2005) the kinematic viscosity of standard biodiesel should be 1.9–6.0 mm2.s-1 (ASTM D6751) and 3.5–5.0 mm2.s-1 as per EN 14214. Present technologies can mitigate the problem of such high viscosity (31.15 cSt) in neat vegetable oils or alternative for diesel engines by using waste heat from the engine to heat the vegetable As the blend percent of Jatropha curcus oil was increased from 10% to 30% in diesel, the GHC of diesel-jatropha curcus blend decreased from 49.16 to 47.33 MJ.kg-1. Similarly, when diesel was mixed with 10% anhydrous ethanol (90:10) and the percent blend of Jatropha curcus oil was increased from 20% to 30% and then to 40% (70:20:10, 60:30:10, 50:40:10), the GHC was 45.94 to 43.66 MJ.kg-1. In addition, blend of 40:40:20 proportions had GHC of 42.42 MJ.kg-1. The GHC of Jatropha curcus oil-anhydrous ethanol blend mixed in 85:15 proportions was found to be 44.47 MJ.kg-1, which was 12.63% lower than that of diesel. It is also evident from the observations that the GHC of diesel- Jatropha curcus oil blends were 3.4 to 7.0% lower, and that of diesel-Jatropha curcus oil-anhydrous ethanol was 9.5 to 16.6% lower than that of diesel. 47 January-March, 2015 Assessment of Biofuel Blends Using Diesel-Biodiesel and Alcohol Cloud and Pour Point lower than diesel. The maximum Fire point was recorded for Jatropha curcus oil. The Cloud and Pour point of diesel was 5 and -1.3°C, respectively, while for the other fuels prepared by blending in different proportions of diesel, Jatropha curcus oil and ethanol (anhydrous), they varied from 1 to 4.5 and -8.3 to 3.0°C, respectively. The Cloud point of Jatropha curcus oil and blend of 40% diesel, 40% Jatropha curcus oil and 20% anhydrous ethanol was lowest at 1, which is in agreement with the findings of Okullo et al. (2012) for crude jatropha curcus oil as 1± 0.50. As the per cent of Jatropha curcus oil in blends increased from 10% to 30% in diesel, the value of cloud point decreased from 3 to 1.3. Similarly, as the per cent of Jatropha curcus oil increased from 10% to 30%, the Pour point of blends decreased from -1.8 to -5.0 0 C. However, the Pour point of the fuel with 75% diesel and 25% anhydrous ethanol had higher value of 3°C. A low value (-8.30C) of Pour point was recorded for the fuel with 85% Jatropha curcus oil and 15% anhydrous ethanol. On the other hand, when 10% anhydrous ethanol was mixed in diesel and kept constant, and the ratio of Jatrophe oil was increased from 20% to 40%, the Pour point and Cloud point again decreased from -3.5 to -4.5 0C and 1.8 to 1.4, respectively. Carbon Residue Content Carbon residue correlates with the amount of carbonaceous deposits of the fuel that form in the combustion chamber of an engine. It was observed to be 0.142% in diesel, which was less than the recommended maximum level of 0.2% in diesel fuel (IS: 1460-1974). This finding slightly differs with the results reported by Peterson and Reece (1996) and Mazed et al. (1985) as 0.16 and 0.082%, respectively. The percent carbon residue content of Jatropha curcus oil was recorded as 4.85. Similarly, the blends of diesel-Jatropha curcus oil and diesel-Jatropha curcus oil-anhydrous ethanol had very high carbon residue content compared to diesel. However, the blend of diesel-anhydrous ethanol mixed in 75:25 proportions was found to have its carbon residue content 28.8% less than that of diesel. The observed carbon residue content of Jatropha curcus oil and anhydrous ethanol blend mixed in proportion of 85:15 was observed as 3.976 percent. Such a high carbon residue may cause carbon deposition on the piston head, valves and pose a problem of choking of injector nozzles. Flash and Fire Point Ash Content Flash point measures the tendency of a sample to form a flammable mixture with air. Flash point of diesel fuel was recorded as 58°C, which was similar (60°C) to that observed by Chandra et al. (2003). However, the present result does not agree with the findings reported by Goering et al. (1983) and Clark et al. (1984) as 80 and 78°C, respectively. The Flash point of Jatropha curcus oil was recorded as 236°C, significantly higher than that of diesel. However, fuel blends with diesel and Jatropha curcus oil; and diesel, Jatropha curcus oil and anhydrous (2000 proof) ethanol had Flash points between 440C and 600C. Lowest Flash point was noticed for fuel blend of diesel and anhydrous (2000 proof) ethanol in 75:25 proportions (Table 1) due to presence of highly volatile and flammable material in relatively nonvolatile material. The ash content in diesel, Jatropha curcus oil and other fuels prepared after blending in different ratios and anhydrous ethanol are presented in Table 1. Ash content of diesel was recorded as 0.009%, and differed to the finding reported by Peterson and Reece (1996) as 0.002 percent. On the other hand, the ash content of Jatropha curcus oil was 0.165%, and much higher than the permitted maximum level (0.01%) as specified by IS: 1460-1974. The increase in ash content could be attributed to more amount of metals contained in the fuel. However, the ash content of Jatropha curcus oil and diesel blends was recorded between 0.0052 and 0.0120 percent. Similarly, the blends with diesel, Jatropha curcus oil and anhydrous ethanol had ash content between 0.0033 to 0.014%; while fuels with Jatropha curcus oil and anhydrous ethanol in 85:15 proportion and diesel and anhydrous ethanol in 75:25 proportion had ash content of 0.0035% and 0.0195%, respectively (Table 1). Among these fuels, the ash content of blends 75:0:25, 90:10:0, 80:20:0, 70:20:10 and 60:30:10 were in the range of ash content specified by the Bureau of Indian Standard (IS: 1448 [P: 10]: 1974). The higher ash content in other blends may, therefore, result in deposition inside the combustion chamber on injector nozzle. The observed values are in accordance with the finding reported by Bhatt et al. (2004). The Fire point is an extension of Flash point in a way that it reflects the condition at which vapour burns continuously for five seconds. Table 1 shows the Fire point of fuels and their blends. Fire point of diesel was found to be 64.5°C, and similar (67.70C) to as reported by Chandra et al. (2003). Fire point of Jatropha curcus oil was found to be 251.5°C. Diesel-Jatropha curcus oil blends of 90:10, 80:20 and 70:30 had higher Fire point than that of diesel. However, Fire point of diesel-Jatropha curcus oil- anhydrous ethanol blends of 70:20:10, 60:30:10, 50:40:10, 40:40:20 and 0:85:15 was 48 JAE : 52 (1) Shishir Kumar Verma, A. K. A Lawrence, A. K. Tripathi and K. K. Patel Bari S; Yu C W; Lim T H. 2002. Performance deteroritation and durability issues while running a diesel engine with crude palm oil. In: Proc. Inst. Mech. Eng., Part d, J. Automobile Eng., 216, 785-792. Total Acidity -1 Total acidity of diesel fuel, 0.23 mg KOH.g , was less than half of the permitted level of 0.50 mg KOH.g-1 by the IS: 1460-1974. However, Jatropha curcus oil (2.24 mg KOH.g-1), diesel-Jatropha curcus oil blends (90:10, 80:20 and 70:30) and blends of diesel-Jatropha curcus oil-anhydrous ethanol (70:20:10, 60:30:10, 50:40:10 and 40:40:20) were higher than the recommended level (Table 1). The measured values thus indicated that the total acidity of the blends increased with an increase in level of Jatropha curcus oil in the blend. The total acidity of diesel- anhydrous ethanol blended in 75:25 proportions (0.43 mg KOH.g-1) was the only fuel blend meeting the recommended level. Bhatt Y C; Murthy N S; Datta R K. 2004. Use of Mahua oil (Madhuca Indica) as a diesel fuel extender, IE (I) JournalAG, 85, 10-14. BIS. 1970. Methods of Test for Petroleum and its Products. Cloud Point and Pour Point. IS: 1448, Bureau of Indian Standards, New Delhi, pp: 10. BIS. 1972. Indian Standard Methods of Test for Petroleum and its products: Density and Relative Density (First Revision) IS: 1448, Bureau of Indian Standards (BIS), New Delhi, pp: 32. The high acid value, in case of Jatropha curcus oil and its blend with diesel would thus affect the life of fuel injection pump, injector, cylinder and piston of the engine. So, the total acidity of fuels can be used as guide in quality control of fuel. BIS. 1976. Indian Standard Methods of Test for Petroleum and its products: Determination of Kinematic and Dynamic Viscosity. 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Automobile Eng., 216, 237-242. 51 INFORMATION AND INSTRUCTIONS FOR AUTHORS The Journal of Agricultural Engineering contains original papers only and submission of a paper will be taken to imply that no similar paper has been or is being submitted elsewhere. Manuscripts (three copies alongwith a soft copy) in English, typed in double space on one side of A-4 size paper, should be submitted to the Chief Editor. Manuscripts can also be submitted through e-mail to the Chief Editor, and are preferred. The subject matter relects the wide range and interdisciplinary nature of research in engineering and physics for agriculture. Papers describing engineering innovations for ield crops, horticulture, livestock, and isheries and those reporting research seeking to understand and model the physical processes underlying agricultural systems are welcome. The following three types of papers are invited for publication. 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Madhava, D. Ravindra Babu, P. C. Vengaiah and B. Hari Babu Performance Evaluation of a Sunlower Seed Huller 20 – Olaosebikan Layi Akangbe, Victor Ifeanyi Obiora Ndirika, Usman Shehu Mohammed and Lawan Garba Abubakar Effect of Drying Methods and Storage on Quality of Ready-to-eat Dehydrated Carrot Shreds 26 – V.R. Sagar Spatial Assessment of Wastewater Quality of Nag River for Irrigation 31 – A.R. Mhaske, S.M. Taley, Shinde and R.N. Katkar Assessment of Biofuel Blends Using Diesel-Biodiesel and Alcohol 43 – Shishir Kumar Verma, A. K. A Lawrence, A. K. Tripathi and K. K. Patel Information and Instructions for Authors Printed at New United Process, A-26, Naraina Indl. Area, Ph-II, New Delhi-110028 Phone : 25709125 52