title: Pattern analysis techniques to process fermentation curves: application to discrimination of enological alcoholic fermentations.
abstract: In fermentation processes, kinetic curves are generally aimed at control purposes. However, these curves could also contain information about inherent features of the product (such as origin, quality, etc.). This article presents several pattern analysis techniques used to classify fermentation curves. An application to alcoholic fermentation is presented as an illustration: it aims at retrieving the origin of a must from its fermentation curve. The fermentation kinetics of five vineyard musts, harvested over 9 years on the same parcels, were recorded. From these curves two sets of variables were generated: The first (p(1)) gathers all the kinetic curve points. The second (p(2)) contains a restrained number of variables, generated by the expert knowledge of the enologist. The set p(2) was processed by two very different techniques: a linear one (factorial discriminant analysis) and a nonlinear one (artificial neural networks). The set p(1) was processed by a new chemometric technique, the discriminant partial least-squares regression. For all the sets and the techniques used the selection of variables was studied. The interest in the latter is largely demonstrated both by theoretical and practical discussions. The discrimination results (up to 94% of good predictions) enhance the interest of the on-line measurements and their use in such pattern analysis tools.
authors.affiliation: Division GIQUAL - Cemagref, BP 5095 - 34033 Montpellier, 34033 France. jean-michel.roger@cemagref.fr
authors.forename: Jean-Michel
authors.initials: JM
authors.lastname: Roger
authors.forename: Jean-Marie
authors.initials: JM
authors.lastname: Sablayrolles
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Steyer
authors.forename: Véronique
authors.initials: V
authors.lastname: Bellon-Maurel
mesh_terms: D000465:Algorithms; D019149:Bioreactors; D016002:Discriminant Analysis; D000431:Ethanol / Q000378:metabolism; D005285:Fermentation / Q000502:physiology*; D008954:Models, Biological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D011786:Quality Control; D012044:Regression Analysis; D012441:Saccharomyces cerevisiae / Q000145:classification / Q000378:metabolism*; D012680:Sensitivity and Specificity; D013045:Species Specificity; D027843:Vitis / Q000145:classification / Q000378:metabolism*
chemical_list: D000431:Ethanol