Discrimination and characterization of different intensities of goaty flavor in goat milk by means of an electronic nose

Abstract

An electronic nose based on metal oxide sensors was used to measure goaty flavor in goat milk samples. To study the relationships between electronic nose data, sensory data, and levels of free fatty acids (FFA), multivariate partial least square regression (PLS) was carried out. The electronic nose system evaluation correlated well with sensory evaluation. The coefficients of determination (R2) of the PLS models reached 90.0%. The electronic nose, combined with principal component analysis and linear discriminant analysis, can discern among goat milk samples with different goaty flavor intensities. In addition, Fisher discriminant analysis and back-propagation neural network were carried out to evaluate goaty flavor intensity, and the prediction accuracies were 98.2 and 100.0%, respectively. The electronic nose is a potentially useful tool to evaluate goaty flavor intensity in goat milk samples.