Classification of Microbial Defects in Milk Using a Dynamic Headspace Gas Chromatograph and Computer-Aided Data Processing. 1. Principal Component Similarity Analysis

There is a need for reliable objective tests for the detection of off-flavors of milk, some of which are caused by bacteriological growth. The objective of the study was to distinguish abnormal from normal milk samples using a low-cost headspace gas chromatograph and principal component similarity analysis. UHT-sterilized milk was inoculated with Pseudomonas fragi, Pseudomonas fluorescens, Bacillus subtilis, Enterobactor aerogenes, Lactococcus lactis, and a mixed culture (P. fragi:E. aerogenes:L. lactis = 1:1:1) to approximately 4.0 log10 CFU mL-1. The samples were stored at 4 °C for 10 days for P. fragi and P. fluorescens and 30 °C for 24 h for the remaining bacteria. A new multiavariate analysis technique, principal component similarity analysis, was capable of distinguishing milk samples inoculated with P. fragi, P. fluorescnes, L. lactis, E. aerogenes, and a mixed culture from uninoculated samples. Keywords: Dynamic headspace gas chromatography; principal component similarity analysis; mulivariate analysis; off-flavor; classification