Innovative and thorough practice to certify reference materials for sensory defects of olive oil.

An important problem in the olive sector is the occasional mismatch of results obtained by different tasting panels when the same olive oil sample is analysed. These discrepancies could be minimised by using reference materials (RM) for taster training. A comprehensive protocol based on the combined use of sensory and instrumental analysis for the certification of olive oil batches as RMs, developed within the framework of the project 'Operational Group INTERPANEL', is proposed. Similarity indices (R2, cosθ and NEAR) applied on GC-MS fingerprints, allow a successful homogeneity and stability assessment of produced batches. Furthermore, the use of robust statistics combined with a set of instructions developed to remove outliers were applied with excellent results on sensory data set provided by supra-panel composed by more than 100 qualified tasters. This work is the first to provide a comprehensive protocol for certification of real olive oil samples as RM for sensory analysis.

Simple and rapid instrumental characterization of sensory attributes of virgin olive oil based on the direct coupling headspace-mass spectrometry.

A methodology for obtaining reliable qualitative and quantitative information about negative (fusty, muddy sediment, musty, rancid and vinegary) and positive (fruity) sensory attributes of virgin olive oils (lampante and extra) has been developed. The procedure implies the joint use of a headspace autosampler, a mass spectrometer and an adequate chemometric data treatment. For this purpose, soft independent modelling of class analogy (SIMCA) and partial least squares (PLS) regression approaches were used for attribute identification and quantification, respectively. InStep application was employed to generate a decision tree by the combination of both models in order to provide the joint prediction of the sensory attributes of a given virgin olive oil and their respective intensities by means of a single output result. The good prediction results obtained when the decision tree generated were applied to a new set of virgin olive oil samples (viz, a specificity of 100%, an average sensitivity of 86% and a RMSEP<0.8% in the quantification task) reveals its potential applicability in routine analysis.

Usefulness of the direct coupling headspace-mass spectrometry for sensory quality characterization of virgin olive oil samples.

The applicability of the headspace coupled to mass spectrometry for evaluation of the sensory quality of virgin olive oil samples is presented. The volatiles of the oil are directly transferred from the sample vial to the detector without chromatographic separation. The mass spectrum obtained can be considered as a fingerprint of the oil sample and can be used for classification purposes. After a training step with samples previously qualified following the official method, a classification model was created using the supervised technique soft independent modeling of class analogy (SIMCA). Eight negative (rancid, winey-vinegary, muddy sediment, hay-wood, vegetable water, earthy, fusty and musty-humidity) and three principal positive attributes (fruity, bitter and pungent) have been included in this study. With them, a classification model consisting of two main classes (extra- and lampante-virgin olive oil) was constructed. In addition, the unsupervised technique cluster analysis permitted the discrimination among oils with different negative attributes. The proposed procedure has been applied to the classification of commercial samples (as extra- or lampante-virgin olive oils) and the results were compared with those provided by the expert's panel with acceptable correlation.