Relating sensory analysis with SPME-GC-MS data for Spanish-style green table olive aroma profiling

Abstract

The sensory profile and volatile composition of 24 samples of Spanish-style green table olives were studied by Quantitative Descriptive Analysis and solid phase micro-extraction gas chromatography coupled to mass spectrometry (SPME-GC-MS), respectively, with the aim to characterize this type of table olive. The aroma of samples was described by the sensory panel using nine descriptors (lactic, green fruit, ripe fruit, grass, hay, musty, lupin, wine, and alcohol). A total of 133 volatile compounds were identified in the headspace of samples. Principal component analysis (PCA) applied to both datasets showed a poor separation of samples according to cultivars, but a trend to separate according to sampling time. Reliable partial least squares (PLS) regression models were developed for four sensory descriptors (lactic, lupin, wine, and alcohol) and allowed identifying the compounds both positively and negatively correlated to such odor sensations. Such models could be used to predict the intensity of the above-mentioned descriptors as a function of SPME-GC-MS data.