Decision trees in selection of featured determined food quality.

The determination of food quality, authenticity and the detection of adulterations are problems of increasing importance in food chemistry. Recently, chemometric classification techniques and pattern recognition analysis methods for wine and other alcoholic beverages have received great attention and have been largely used. Beer is a complex mixture of components: on one hand a volatile fraction, which is responsible for its aroma, and on the other hand, a non-volatile fraction or extract consisting of a great variety of substances with distinct characteristics. The aim of this study was to consider parameters which contribute to beer differentiation according to the quality grade. Chemical (e.g. pH, acidity, dry extract, alcohol content, CO(2) content) and sensory features (e.g. bitter taste, color) were determined in 70 beer samples and used as variables in decision tree techniques. This pattern recognition techniques applied to the dataset were able to extract information useful in obtaining a satisfactory classification of beer samples according to their quality grade. Feature selection procedures indicated which features are the most discriminating for classification.

Application of artificial neural network in food classification.

Artificial neural network (ANN) classifiers have been successfully implemented for various quality inspection and grading tasks of diverse food products. ANN are very good pattern classifiers because of their ability to learn patterns that are not linearly separable and concepts dealing with uncertainty, noise and random events. In this research, the ANN was used to build the classification model based on the relevant features of beer. Samples of the same brand of beer but with varying manufacturing dates, originating from miscellaneous manufacturing lots, have been represented in the multidimensional space by data vectors, which was an assembly of 12 features (% of alcohol, pH, % of CO(2) etc.). The classification has been performed for two subsets, the first that included samples of good quality beer and the other containing samples of unsatisfactory quality. ANN techniques allowed the discrimination between qualities of beer samples with up to 100% of correct classifications.

Fuzzy definition of molecular fragments in chemical structures

This paper presents a methodology for seeking the relationships between chemical substructures (molecular fragments) and spectral parameters using a computer collection data of molecular spectra. To establish the spectrum-structure correlations, the program has to search the chemical structure base in order to find compounds containing a given molecular fragment in the molecule. There exists no sole definition of a substructure, as it always depends on the type of problem dealt with. In the problem of structural identification, fuzzy definitions of substructures are applied, and their forms are imposed by the spectral methods used.

Automatic generation of knowledge base from infrared spectral database for substructure recognition

This paper presents a new methodology of chemical substructure recognition by interpretation of an infrared spectrum. The approach in spectrum interpretation is based on the determination of functional groups, which may be present or absent in compounds whose structure is unknown. The process of searching for spectrum-substructure correlation is realized by application of a statistical algorithm. In this method, correlations are generalized and condensed into a set of interpretation rules which are applied to the interpretation of an unknown compound's spectrum in order to predict whether the respective substructures are present or absent in the unknown molecule.