Abstract Adulteration of olive oil significantly compromises the interests of both producers and consumers, making its authentication a crucial challenge in the food industry. This study explored the potential of combining Raman spectroscopy with machine learning for discriminating various blended samples and quantifying olive oil content in mixtures. Raman features, such as peak intensities at specific shifts, were extracted from the spectra and analyzed using hierarchical cluster analysis (HCA) and correlation analysis (CA) to identify significant variations corresponding to altered proportions of olive oil. Qualitative and quantitative analyses were performed to classify 10 oil types and predict compositional ratios in binary and ternary blends, comparing different chemometric techniques and input features. Among these, the random forest (RF) model yielded a high classification accuracy (98.9%) and strong predictive performance, with coefficients of determination ( R 2 ) of 0.985 and 0.926 on the binary and ternary samples, respectively. The Shapley additive explanations (SHAP) algorithm was subsequently employed to assess the contribution of key Raman features to the prediction accuracy of superior models. Overall, this novel analytical framework highlights Raman features and offers a promising solution for real-time quality monitoring of olive oil products.
Chen et al. (Fri,) studied this question.