Volatilomics for food quality and authentication

Abstract

Volatilomics technology plays a significant role in food quality and authenticity assessment. Its main advantage is the generation of a plethora of data that can be rapidly analyzed and provide in short-time estimation on products’ quality. Technical aspects of volatilome analysis, including sample preparation, extraction methods, analytical conditions, instrumentation and multivariate data analysis are considered to be a significant part of the volatilomics approach. Solid phase microextraction (SPME) combined with GC–MS is the most frequently used analytical method while sensor-based techniques such as e-nose are gaining gradually ground in volatilome analysis. Multivariate analysis, in terms of both supervised and unsupervised methods such as principal component analysis (PCA), discriminant analysis (DA) and partial least squares regression (PLS) is of critical importance for the success of the volatilomics concept. Depending on the applied technique, volatilome profile analysis can contribute to the discrimination of samples in groups, the identification of specific compounds (biomarkers) that are crucial for quality and/or authenticity assessment and the prediction of several factors associated with food quality. The fact that each food commodity has its own distinctive volatilome profile that is differentiated depending on environmental conditions, geographical or biological origin, processing conditions and several other factors, renders volatilomics a promising tool in food quality and authenticity assessment.