Discriminant analysis is an effective tool for classification of experimental units into groups. ... more Discriminant analysis is an effective tool for classification of experimental units into groups. When the number of variables is much bigger then the number of observations it is necessary to include a variable selection procedure into the inferential process. Typical examples are the classification of subjects according to phenotypes via gene expressions and the classification of different types of food into species via near infrared spectroscopy. We build upon a conjugate normal discriminant model, either liner and quadratic, and include a stochastic search variable selection procedure via an MCMC algorithm. For the spectroscopy example we show how to perform discriminant analysis in the wavelet domain and include a Markov random tree (MRT) prior that maps the connections among wavelets coefficients. We show good performances of the method in simulation studies, gene expression analysis and food authenticity and compare results to several other procedures.
Chemometrics and Intelligent Laboratory Systems, 2007
Classification methods can be used to classify samples of unknown type into known types. Many cla... more Classification methods can be used to classify samples of unknown type into known types. Many classification methods have been proposed in the chemometrics, statistical and computer science literature.
Discriminant analysis is an effective tool for the classification of experimental units into grou... more Discriminant analysis is an effective tool for the classification of experimental units into groups. When the number of variables is much larger than the number of observations it is necessary to include a dimension reduction procedure into the inferential process. Here we present a typical example from chemometrics that deals with the classification of different types of food into species via near infrared spectroscopy. We take a nonparametric approach by modeling the functional predictors via wavelet transforms and then apply discriminant analysis in the wavelet domain. We consider a Bayesian conjugate normal discriminant model, either linear or quadratic, that avoids independence assumptions among the wavelet coefficients. We introduce latent binary indicators for the selection of the discriminatory wavelet coefficients and propose prior formulations that use Markov random tree (MRT) priors to map scale-location connections among wavelets coefficients. We conduct posterior inference via MCMC methods, we show performances on our case study on food authenticity and compare results to several other procedures.
International Journal of Food Science & Technology, 2007
Solutions of casein and carrageenan, prepared at neutral pH, were used to produce edible protein ... more Solutions of casein and carrageenan, prepared at neutral pH, were used to produce edible protein fibres by a wet spinning technique. Variation in fibre breaking strength, shear force, stress relaxation and elasticity as a result of changes in such process variables as fibre wind-up rate, immersed length, precipitation bath salt concentration and precipitation bath temperature were followed. The effect of spinning solution carrageenan level on fibre texture was also investigated. It was possible to alter certain textural properties by changes in the conditions of fibre production.
Food and beverage processors require tools to monitor conformance of finished goods to their defi... more Food and beverage processors require tools to monitor conformance of finished goods to their defined specification; regulatory authorities need appropriate methods for detecting retail fraud. In this report, samples (n = 275) of Belgian and other European beers were collected and analysed using near infrared transflectance spectroscopy; three class-modelling techniques (soft independent modelling of class analogy, SIMCA; potential functions techniques, POTFUN; and unequal dispersed classes, UNEQ) were employed to characterise beer types (firstly Trappist and then Rochefort) while a classification method (partial least squares discriminant analysis, PLS-DA) was applied to discriminate between two final beer classes: Rochefort 8°and Rochefort 10°. The class-models and the classification rules developed were validated by means of an external prediction set. A discussion on the appropriate use of these chemometric approaches is included. Modelling of Trappist beers met with limited success while model efficiencies for Rochefort samples were highest for SIMCA and UNEQ applications i.e. 81.4% and 84.5% respectively. The classification of beers as Rochefort 8˚or Rochefort 10˚was possible with an average correct classification rate of 93.4%.
Discriminant analysis is an effective tool for classification of experimental units into groups. ... more Discriminant analysis is an effective tool for classification of experimental units into groups. When the number of variables is much bigger then the number of observations it is necessary to include a variable selection procedure into the inferential process. Typical examples are the classification of subjects according to phenotypes via gene expressions and the classification of different types of food into species via near infrared spectroscopy. We build upon a conjugate normal discriminant model, either liner and quadratic, and include a stochastic search variable selection procedure via an MCMC algorithm. For the spectroscopy example we show how to perform discriminant analysis in the wavelet domain and include a Markov random tree (MRT) prior that maps the connections among wavelets coefficients. We show good performances of the method in simulation studies, gene expression analysis and food authenticity and compare results to several other procedures.
Chemometrics and Intelligent Laboratory Systems, 2007
Classification methods can be used to classify samples of unknown type into known types. Many cla... more Classification methods can be used to classify samples of unknown type into known types. Many classification methods have been proposed in the chemometrics, statistical and computer science literature.
Discriminant analysis is an effective tool for the classification of experimental units into grou... more Discriminant analysis is an effective tool for the classification of experimental units into groups. When the number of variables is much larger than the number of observations it is necessary to include a dimension reduction procedure into the inferential process. Here we present a typical example from chemometrics that deals with the classification of different types of food into species via near infrared spectroscopy. We take a nonparametric approach by modeling the functional predictors via wavelet transforms and then apply discriminant analysis in the wavelet domain. We consider a Bayesian conjugate normal discriminant model, either linear or quadratic, that avoids independence assumptions among the wavelet coefficients. We introduce latent binary indicators for the selection of the discriminatory wavelet coefficients and propose prior formulations that use Markov random tree (MRT) priors to map scale-location connections among wavelets coefficients. We conduct posterior inference via MCMC methods, we show performances on our case study on food authenticity and compare results to several other procedures.
International Journal of Food Science & Technology, 2007
Solutions of casein and carrageenan, prepared at neutral pH, were used to produce edible protein ... more Solutions of casein and carrageenan, prepared at neutral pH, were used to produce edible protein fibres by a wet spinning technique. Variation in fibre breaking strength, shear force, stress relaxation and elasticity as a result of changes in such process variables as fibre wind-up rate, immersed length, precipitation bath salt concentration and precipitation bath temperature were followed. The effect of spinning solution carrageenan level on fibre texture was also investigated. It was possible to alter certain textural properties by changes in the conditions of fibre production.
Food and beverage processors require tools to monitor conformance of finished goods to their defi... more Food and beverage processors require tools to monitor conformance of finished goods to their defined specification; regulatory authorities need appropriate methods for detecting retail fraud. In this report, samples (n = 275) of Belgian and other European beers were collected and analysed using near infrared transflectance spectroscopy; three class-modelling techniques (soft independent modelling of class analogy, SIMCA; potential functions techniques, POTFUN; and unequal dispersed classes, UNEQ) were employed to characterise beer types (firstly Trappist and then Rochefort) while a classification method (partial least squares discriminant analysis, PLS-DA) was applied to discriminate between two final beer classes: Rochefort 8°and Rochefort 10°. The class-models and the classification rules developed were validated by means of an external prediction set. A discussion on the appropriate use of these chemometric approaches is included. Modelling of Trappist beers met with limited success while model efficiencies for Rochefort samples were highest for SIMCA and UNEQ applications i.e. 81.4% and 84.5% respectively. The classification of beers as Rochefort 8˚or Rochefort 10˚was possible with an average correct classification rate of 93.4%.
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Papers by G. Downey