Vibrational spectroscopy and chemometrics tools for authenticity and improvement the safety control in goat milk

Abstract

Goat milk has a potential target of fraud. In this sense, Near Infrared Spectroscopy (NIRS) have been successfully used to detect food fraud. This study aimed to develop multivariate classification models using NIRS to detect adulterants in goat milk. Principal Component Analysis (PCA), control chart, k-Nearest Neighbor (k-NN), Part Least Square-Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogies (SIMCA) were used to detect the adulterants: water, urea, bovine whey and cow's milk in goat's milk samples with concentrations of 0 (control), 1, 5, 10, 15 and 20% v/v, resulting in 300 control samples and 300 adulterated samples. The control chart discriminated authentic and adulterated samples with 95% confidence. The PLS-DA results were better compared to those obtained by k-NN and SIMCA; presenting 100% sensitivity and specificity in calibration, cross validation, and prediction. Therefore, NIRS combined with PLS-DA was adequate to detect goat milk safety control associated with adulteration.