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 (R2) 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.
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References
Lamas, S., Ruano, D., Dias, F. & Barreiro, F. Flavoured and fortified olive oils—pros and cons. Trends Food Sci. Technol. 21, 202301629 (2024).
Suzuki, D., Sato, Y., Mori, A. & Tamura, H. A method for gaining a deeper insight into the aroma profile of olive oil. npj Sci. Food 5, 16 (2021).
Conte, L. et al. Olive oil quality and authenticity: a review of current EU legislation, standards, relevant methods of analyses, their drawbacks and recommendations for the future. Trends Food Sci. Technol. 105, 483–493 (2020).
dos Santos, V. R. et al. Novel time-domain NMR-based traits for rapid, label-free olive oils profiling. npj Sci. Food 6, 59 (2022).
Caño-Carrillo, I., Gilbert-López, B., Ruiz-Samblás, C., Molina-Díaz, A. & García-Reyes, J. F. Virgin olive oil authentication using mass spectrometry-based approaches: a review. TrAC Trends Anal. Chem. 181, 118029 (2024).
Mehany, T., González-Sáiz, J. M. & Pizarro, C. Recent advances in spectroscopic approaches for assessing the stability of bioactive compounds and quality indices of olive oil during deep-frying: Current knowledge, challenges, and implications. Food Chem. 464, 141624 (2025).
Fan, D. et al. Quantitative analysis of blended oils by confocal Raman spectroscopy and chemometrics in situ. Food Control 142, 109244 (2022).
Portarena, S. et al. Lutein/β-carotene ratio in extra virgin olive oil: an easy and rapid quantification method by Raman spectroscopy. Food Chem. 404, 134748 (2023).
Jiménez-Hernández, G., González-Casado, A., Ortega-Gavilán, F., García-Mena, J. & Bagur-González, M. G. Multivariate quantification of olive oil blended with sunflower oil by portable device SORS. Food Control 181, 111766 (2026).
Hua, Y. et al. Rapid analysis of flaxseed oil quality during frying process based on Raman spectroscopy combined with peak-area-ratio method. LWT 196, 115839 (2024).
Stradling, J. et al. Raman on the palm: handheld Raman spectroscopy for enhanced traceability of palm oil. npj Sci. Food 9, 95 (2025).
Soares, W. F., Chinchin-Piñan, B. D., Silva, R. M. & Villa, J. E. L. Interpretable support vector machine for authentication of omega-3 fish oil supplements using Raman spectroscopy. Food Control 166, 110754 (2024).
Moe Htet, T. T. et al. PLS-regression-model-assisted Raman spectroscopy for vegetable oil classification and non-destructive analysis of alpha-tocopherol contents of vegetable oils. J. Food Compos. Anal. 103, 104119 (2021).
Chen, Y. et al. Quantitative analysis of β-carotene and unsaturated fatty acids in blended olive oil via Raman spectroscopy combined with model prediction. Food Chem. 470, 142621 (2025).
Xu, P. et al. A fast and highly efficient strategy for detection of camellia oil adulteration using machine learning assisted SERS. LWT 213, 117069 (2024).
Hassija, V. et al. Interpreting black-box models: a review on explainable artificial intelligence. Cogn. Comput. 16, 45–74 (2024).
Abdalla, Y. et al. Machine learning of Raman spectra predicts drug release from polysaccharide coatings for targeted colonic delivery. J. Control. Release 374, 103–111 (2024).
Pavlidis, D. E. et al. Turn to the wild: a comprehensive review on the chemical composition of wild olive oil. Food Res. Int. 196, 115038 (2024).
Zhao, H. et al. The application of machine-learning and Raman spectroscopy for the rapid detection of edible oils type and adulteration. Food Chem. 373, 131471 (2022).
Fang, P., Wang, H. & Wan, X. Olive oil authentication based on quantitative beta-carotene Raman spectra detection. Food Chem. 397, 133763 (2022).
Wu, X. et al. Raman spectroscopy combined with multiple one-dimensional deep learning models for simultaneous quantification of multiple components in blended olive oil. Food Chem. 431, 137109 (2024).
Novikov, V. S. et al. Relations between the Raman spectra and molecular structure of selected carotenoids: DFT study of alpha-carotene, beta-carotene, gamma-carotene and lycopene. Spectrochim. Acta A Mol. Biomol. Spectrosc. 270, 120755 (2022).
Kakouri, E. et al. Authentication of the botanical and geographical origin and detection of adulteration of olive oil using gas chromatography, infrared and Raman spectroscopy techniques: a review. Foods 10, 1565 (2021).
Ríos-Reina, R. et al. A comparative study of fluorescence and Raman spectroscopy for discrimination of virgin olive oil categories: Chemometric approaches and evaluation against other techniques. Food Control 158, 110250 (2024).
Dos Santos, V. R. et al. Novel time-domain NMR-based traits for rapid, label-free olive oils profiling. npj Sci Food. 6, 59 (2022).
Yang, S. et al. Pulsed electric field treatment improves the oil yield, quality, and antioxidant activity of virgin olive oil. Food Chem. X 22, 101372 (2024).
Windarsih, A. et al. Application of Raman spectroscopy and chemometrics for quality controls of fats and oils: a review. Food Rev. Int. 39, 3906–3925 (2021).
Vargas Jentzsch, P. et al. Raman spectroscopy in the detection of adulterated essential oils: the case of nonvolatile adulterants. J. Raman Spectrosc. 52, 1055–1063 (2021).
de Lima, T. K., Musso, M. & Bertoldo Menezes, D. Using Raman spectroscopy and an exponential equation approach to detect adulteration of olive oil with rapeseed and corn oil. Food Chem. 333, 127454 (2020).
Zhao, X. Y., Liu, G. Y., Sui, Y. T., Xu, M. & Tong, L. Denoising method for Raman spectra with low signal-to-noise ratio based on feature extraction. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 250, 119374 (2021).
Petrov, D. V. Pressure dependence of peak positions, half widths, and peak intensities of methane Raman bands (ν2, 2ν4, ν1, ν3, and 2ν2). J. Raman Spectrosc. 48, 1426–1430 (2017).
Yuan, X. & Mayanovic, R. A. An empirical study on Raman peak fitting and its application to Raman quantitative research. Appl. Spectrosc. 71, 2325–2338 (2017).
Guo, G. et al. Correlation analysis between Raman spectral signature and transcriptomic features of carbapenem-resistant Klebsiella pneumoniae. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 308, 123699 (2024).
Zhu, J. et al. Effect of multiple freeze–thaw cycles on water migration, protein conformation and quality attributes of beef longissimus dorsi muscle by real-time low field nuclear magnetic resonance and Raman spectroscopy. Food Res. Int. 166, 112644 (2023).
Acknowledgements
Y.C. and S.Z. would like to thank the Wuhan Polytechnic University for its support. Raman spectra were measured at the Hubei Provincial Engineering Research Center for Food Quality and Safety Information of Wuhan Polytechnic University. The measurements were supported through Hubei Provincial Key Laboratory of Agricultural Products Processing and Transformation, Wuhan Polytechnic University.
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Y.C. contributed towards experimental design, investigation, data analysis, data interpretation and writing the original draft. R.S. contributed to the experimental design and software. S.Z. contributed to funding acquisition and supervision. B.L. contributed to the visualization and software. H.H. contributed to sample collection and conceptualization. All authors contributed to reviewing and editing the writing.
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Chen, Y., Shao, R., Zeng, S. et al. Unveiling key peak features for olive oil authentication utilizing Raman spectroscopy and chemometrics. npj Sci Food (2026). https://doi.org/10.1038/s41538-026-00738-2
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DOI: https://doi.org/10.1038/s41538-026-00738-2
