Detection and prediction of hydrolytic rancidity in milk by multiple regression analysis of short-chain free Fatty acids determined by solid phase microextraction gas chromatography and quantitative flavor intensity assessment.

Abstract

The objective was to establish a method for detecting and predicting hydrolytic rancidity in milk by correlating quantitative sensory data with individual short-chain free fatty acids (FFA) (C(4)-C(12)) in milk determined by solid phase microextraction and gas chromatography (SPME-GC). A FFA-based equation for determining rancid flavor intensities in milk was derived by stepwise regression analysis. A highly significant (p < 0.001) correlation coefficient (R (2)) of 0.84 indicated that rancidity scores were dependent on FFA obtained by SPME-GC and that a good proportion of the variation in the rancidity scores was explained by the model. When rancidity scores were predicted for 19 commercial milks, one sample was found to be distinctly rancid by the statistical model and by the trained sensory panel. The rest of the samples were found to be nonrancid by either method. Thus, the predicting power of the model was shown because there was 100% correct flavor classification for the samples tested.