Exploring the volatile metabolome of conventional and organic walnut oils by solid-phase microextraction and analysis by GC-MS combined with chemometrics.

It is challenging to establish a correlation between the agronomical practices and the volatile profile of high-value agricultural products. In this study, the volatile metabolome of walnut oils from conventional and organic farming type was explored by HS-SPME-GC-MS. The SPME protocol was optimized after evaluating the effects of extraction time, extraction temperature, and sample mass. The optimum parameters involved the extraction of 0.500 g walnut oil at 40 °C within 60 min. Twenty Greek walnut oils produced with conventional and organic farming were analyzed and 41 volatile compounds were identified. The determined compounds were semi-quantified, and further processed with chemometrics. Agglomerative hierarchical clustering (AHC) and principal component analysis (PCA) were used. A robust classification model was developed using sparse partial least squares-discriminant analysis (sPLS-DA) for the discrimination of walnut oils into conventional and organic, establishing volatile markers that could be used to guarantee the type of farming.