Multiblock chemometrics for the discrimination of three extra virgin olive oil varieties.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).  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).
PLS1-DA
Multiblock chemometrics for the discrimination of three extra virgin olive oil varieties.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).  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).
MB-PLS1-DA
Multiblock chemometrics for the discrimination of three extra virgin olive oil varieties.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).  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). 
GC
Multiblock chemometrics for the discrimination of three extra virgin olive oil varieties.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).  PLS1-DA models using GC data gave better results than those using MIR data. 
MIR