CLASSIFICATION OF MICROBIAL DEFECTS IN MILK USING A DYNAMIC HEADSPACEGAS-CHROMATOGRAPH AND COMPUTER-AIDED DATA-PROCESSING .2. ARTIFICIAL NEURAL NETWORKS, PARTIAL LEAST-SQUARES REGRESSION-ANALYSIS, AND PRINCIPAL COMPONENT REGRESSION-ANALYSIS

Objective, yet cost-effective evaluation of flavor is difficult in qua lity control of milk. Inexpensive gas chromatographs in conjunction wi th computer models make it feasible to construct an objective flavor e valuation system far routine quality control purposes. The purpose of this study was to classify milk with microbial off-flavors using a low -cost headspace gas chromatograph and computer-aided data processing. Principal component similarity (PCS) analysis was discussed in part 1. In part 2, artificial neural networks (ANN), partial least-squares re gression (PLS) analysis, and principal component regression (PCR) anal ysis are examined. UHT milk was inoculated with various bacteria (Pseu domonas fragi, Pseudomonas fluorescens, Lactococcus lactis, Enterobact or aerogenes, and Bacillus subtilis) and a mixed culture (P. fragi:E. aerogenes:L. lactis = 1:1:1) to approximately 4.0 log(10) CFU mL(-1). ANN were able to make better predictions than PLS and PCR. The predict ion ability of PLS was better than PCR. The performance of each method depended on the content of training and testing of data, i.e., more d ata resulted in better predictive ability.