The use of Raman spectroscopy and chemometrics for the discrimination of lab-produced, commercial, and adulterated cold-pressed oils

In this study, lab-produced cold-pressed oil samples of black cumin (CPBCO), almond (CPAO), and walnut (CPWO) were compared with their commercial substitutes. Additionally, adulteration of cold-pressed oils with corn and sunflower oils was determined. Chemical information and fatty acid composition of the samples were obtained using Raman spectroscopy and gas chromatography, respectively. Raman spectra were analyzed with principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The developed PCA models for the discrimination of CPBCO, CPAO, and CPWO from the adulterated samples explained 93.35%, 96.24, and 94.25% of the cumulative variances, respectively. Root mean squared error of calibration (RMSEC), root mean squared error of cross-validation (RMSECV), and root mean squared error of cross-prediction (RMSEP) values were found to be between 0.06 and 0.21, 0.03–0.11, and 0.05–0.21; 0.08–0.24, 0.06–0.15, and 0.08–0.25; and 0.05–0.18, 0.03–0.10, and 0.09–0.20 for the PLS-DA models of CPBCO, CPAO, and CPWO adulterations. The developed PCA models for the discrimination of CPBCO, CPAO, and CPWO from commercial samples explained 99.64%, 91.27, and 92.11% of the cumulative variances, respectively. These results indicate that Raman spectroscopy coupled with chemometrics could be potentially useful both for the discrimination of cold-pressed oils and for the rapid determination of adulteration with cheaper oils.