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Soy Sauce Classification by Geographic Region Based on NIR Spectra and Chemometrics Pattern Recognition
Author(s) -
IIZUKA KEIKO,
AISHIMA TETSUO
Publication year - 1997
Publication title -
journal of food science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.772
H-Index - 150
eISSN - 1750-3841
pISSN - 0022-1147
DOI - 10.1111/j.1365-2621.1997.tb04377.x
Subject(s) - chemometrics , partial least squares regression , linear discriminant analysis , pattern recognition (psychology) , artificial intelligence , mathematics , artificial neural network , statistics , computer science , chemistry , chromatography
Statistical and artificial neural network (ANN) pattern recognition techniques were applied to NIR spectra of 38 soy sauce samples collected from the northern/central, western, and southern regions in Japan and related to differences in food flavorings. Linear discriminant analysis (LDA) and ANN using factor scores calculated from NIR spectra showed more accurate differentiations than those based on the original spectra. In LDA, the correctly assigned ratio was 81.6%. Correct classification ratios shown by Partial least squares (PLS2) were 84.2% and by ANN 76.3% in the cross‐validation test. The differentiations suggested that there are quality differences in soy sauce among the three regions in Japan.