Multiblock chemometrics for the discrimination of three extra virgin olive oil varieties

Abstract

To discriminate samples from three varieties of Tunisian extra virgin olive oils, weighted and non-weighted multiblock partial least squares-discriminant analysis (MB-PLS1-DA) models were compared to PLS1-DA models using data obtained by gas chromatography (GC), or global composition through mid-infrared spectra (MIR). Models performances were determined using percentages of sensitivity, specificity and total correct classification. The choice of threshold level for the interpretation of PLS1-DA results was considered. PLS1-DA models using GC data gave better results than those using MIR data. Even with the most conservative threshold, PLS1-DA on GC data allowed very good predictions for Chemlali variety (99% correct classification), but had more difficulty to discriminate Chetoui and Oueslati samples (95% and 84% correct classification respectively). Non-weighted MB-PLS1-DA models benefiting from the synergy between the two sources of data were more discriminative than simple PLS1-DA, yielding better prediction for Chetoui and Oueslati varieties (98% and 90% correct classification respectively).

Chemometric Discrimination of the Varietal Origin of Extra Virgin Olive Oils: Usefulness of 13C Distortionless Enhancement by Polarization Transfer Pulse Sequence and 1H Nuclear Magnetic Resonance Data and Effectiveness of Fusion with Mid-Infrared Spectroscopy Data

“…Moreover, in a previous study, it was shown that the scaling to compensate for the much larger number of variables in one block than in another strongly reduces the influence of data from the block containing the greater number of variables and, therefore, the interest of the combination. 36 The PLS−PLS1-DA strategy gives perfect predictions for the TA and GA models and very good results for the BL model (100% PPV, 100% NPV, 95% EFF, and 97% BA). The performances are also improved for the SA model, reaching 73% PPV and both EFF and BA over 90%.…”

“…Three repetitions were obtained and averaged for each sample. A typical MIR spectrum with band assignments according to a previous paper is shown in Figure .…”

“…35 For olive oil authentication, multiblock PLS-DA models were developed from gas chromatography (GC) and MIR data sets, with and without weighting the block scores, to evaluate their performance against those of the PLS-DA models applied separately to each data set. 36 However, very few studies have combined 13 C NMR and MIR data. In a recent article, the prediction of crude oil properties was improved by mid-level fusion of 13 C and 1 H NMR and MIR data using PLS scores but not by low-or mid-level fusion using PCA scores.…”

“…Mid-level fusion of 1 H NMR, MIR, NIR, and EEM data also improved the discrimination of wine vinegars from three protected designations of origin . For olive oil authentication, multiblock PLS-DA models were developed from gas chromatography (GC) and MIR data sets, with and without weighting the block scores, to evaluate their performance against those of the PLS-DA models applied separately to each data set . However, very few studies have combined 13 C NMR and MIR data.…”

Chemometric Strategies for Spectroscopy-Based Food Authentication

“…It is also often used in combination with LDA for addressing classification problems-MB-PLS is here known as MB-PLS-LDA or MB-PLSDA. So far, many applications of MB-PLS have been reported in the field of food analysis (see Table 3); it has been resorted to (i) for the investigation of sensory parameters of different nature and of their relationships with technological properties of cheese and bread samples [189,190], (ii) for the prediction of meat spoilage time, wine ageing time and crude protein and moisture content in soybean flour by MIR and NIR spectroscopy [191][192][193], (iii) for the discrimination of botanical varieties of extra virgin olive oil, lemon essential oils and wines of different geographical origin by MIR, NIR and Raman spectroscopy [194][195][196], and (iv) for distinguishing added-value from low-quality products [132].…”