Artificial neural networks food analysis

Mocker, J., Wailzer, B., Buchbauer, G. and Wolschann, P. (2002a) Aroma quality differentiation of pyrazine derivatives using selforganizing molecular field analysis and artificial neural network./. Agr. Food Chem., 50,4069—4075. 

When dealing with complex matrices such as those encountered in food and medical samples, it is often difficult to develop a sensor that exhibits a sufficiently selective response for the target analyte. Under these circumstances, arrays and mathematical modeling, for example, principal components analysis, artificial neural networks, or other pattern recognition approaches, may be required [42],... 

Classification problems are numerous in food sdenee, for example, classification methods are nsed to determine a food s origin based on chemical fingerprints [9], but can also discriminate among different fig cultivars with sensory attributes, independent of the source and harvest date of the different cultivars [10]. CVA is just one of maity classification techniques, and the reader is referred to specific articles, e.g., partial least squares discriminant analysis (PLS-DA) [11], artificial neural networks (ANNs) [12], and support vector machines (SVMs) [13]. 

Chtioui, Y, Bertrand, D., Dattee, Y. and Devaux, M.-F. (1996) Identification of seeds by colour imaging comparison of discriminant analysis and artificial neural network. Journal of the Science of Food and Agriculture, 71 433 441. 

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