A comparative study of fluorescence and Raman spectroscopy for discrimination of virgin olive oil categories: Chemometric approaches and evaluation against other techniques

Abstract

Spectroscopic techniques are presented as a reliable and economical alternative for the discrimination of olive oil quality categories. In this work, fluorescence and Raman spectroscopic techniques have been compared by evaluating different characteristics focused on sample preparation and analysis time, as well as classification percentages by chemometric models (PLS-DA) to classify olive oils into three categories: extra virgin (EVOO), virgin (VOO), and lampante (LOO). A statistical evaluation of the quality of the chemometric models was performed by comparing the parameters of sensitivity in terms of the true positive rate (%TPR), specificity (or true negative rate %TNR), and classification error in calibration, cross-validation and prediction. Fluorescence spectroscopy was the screening technique with the best overall accuracy in the ternary models for prediction (78.60%), with higher results than those obtained by Raman (66.03%), the latter being below the average error in the tasting panels. Regarding the classification obtained in binary models (EVOO/Non-EVOO and LOO/Non-LOO), fluorescence spectroscopy continued to present better results than Raman, with classifications of 92.5% and 88.5 % for both models, respectively (Raman obtained percentages of 70.7% and 66.6%). A comparison of the results obtained with confirmation techniques (headspace-gas chromatography-ion mobility spectrometry, HS-GC-IMS) and other spectroscopic techniques (NIR and MIR) studied in previous research was carried out, where it was observed that fluorescence spectroscopy obtained the closest classification percentage to the HS-GC-IMS technique (86.4%) in ternary models. On the other hand, the NIR technique obtained the highest classification percentage in binary models, with 92.6% for EVOO/Non-EVOO and 88.9% for LOO/Non-LOO compared to the value obtained using GC-IMS (95.4 and 86.4 %). Evaluating the rest of the parameters of interest for the olive industry, IR (both NIR and MIR) was the most interesting technique because it is a cheap, simple sensor with fast chemometric treatments and is environmentally-friendly. The results of this comparative study can be used to provide solutions and alternatives for applying new analytical strategies to support the work of tasting panels.