Machine Learning Assisted Raman Spectroscopic Identification of Edible Oils in Fried Food Matrix
4 affiliations
- 1Department of Food and Nutritional Sciences, Sri Sathya Sai Institute of Higher Learning, Vidyagiri, Prasanthi Nilayam, Andhra Pradesh, India
- 2CogniEvolve AI Inc., 410-25 Woodridge Cres., Nepean, Canada, K2B 7T4
- 3Aix-Marseille University
- 4Department of Physics, Sri Sathya Sai Institute of Higher Learning, Vidyagiri, Prasanthi Nilayam, Andhra Pradesh, India
Abstract
Identification of the type of cooking oil used in ready-to-eat (RTE) foods is paramount, as it directly impacts consumers’ health. Comprehensive evaluation of existing protocols reveals a pressing need for developing rapid methodologies with improved operational efficiency. This proof-of-concept study reports development of one such rapid, solvent-free analytical framework by coupling Raman spectroscopy with machine learning. It demonstrates the feasibility of oil type identification from fried food matrices without solvent extraction. Five edible oils (sunflower, soybean, groundnut, palm, and vanaspati) and one complex food matrix (potato chips) were chosen for the study. Five types of paper based absorbents were screened for most effective isolation of the oil from the potato chips. Seven supervised classification algorithms, Logistic Regression, SVC (RBF/Linear), LightGBM, Random Forest Classifier, XGBoost, and KNN were evaluated using stratified five-fold cross-validation. Model performance was assessed using four key metrics: accuracy, precision, recall, and F1-score. Coarse hyperparameter tuning was employed to further enhance the model performance. Comparative analysis of the tested algorithms revealed SVC (RBF) to achieve the best prediction accuracy (97% for oils and 89.8% for chips). The framework reduces the total analysis time from hours to minutes by replacing conventional oil extraction with the newly optimized sampling technique. This new sampling strategy was validated using cross-correlation analysis and non-negative least squares (NNLS) spectral decomposition. The high correlation coefficient (ρ= 0.91) and high spectral contribution (56%) of oils in the chips spectra confirmed the preservation of the oil fingerprints within the chips spectra.
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BibTeX
@misc{shaw2026machine,
title = {{Machine Learning Assisted Raman Spectroscopic Identification of Edible Oils in Fried Food Matrix}},
author = {Amrita Shaw and Deepak L. N. Kallepalli and Chandrasekar S. N. and Sai Muthukumar V. and Jhinuk Gupta},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15008698/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008698/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Machine Learning Assisted Raman Spectroscopic Identification of Edible Oils in Fried Food Matrix
AU - Amrita Shaw
AU - Deepak L. N. Kallepalli
AU - Chandrasekar S. N.
AU - Sai Muthukumar V.
AU - Jhinuk Gupta
PY - 2026
DA - 2026/09/11
PB - ChemRxiv
DO - 10.26434/chemrxiv.15008698/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008698/v1
AB - Identification of the type of cooking oil used in ready-to-eat (RTE) foods is paramount, as it directly impacts consumers’ health. Comprehensive evaluation of existing protocols reveals a pressing need for developing rapid methodologies with improved operational efficiency. This proof-of-concept study reports development of one such rapid, solvent-free analytical framework by coupling Raman spectroscopy with machine learning. It demonstrates the feasibility of oil type identification from fried food matrices without solvent extraction. Five edible oils (sunflower, soybean, groundnut, palm, and vanaspati) and one complex food matrix (potato chips) were chosen for the study. Five types of paper based absorbents were screened for most effective isolation of the oil from the potato chips. Seven supervised classification algorithms, Logistic Regression, SVC (RBF/Linear), LightGBM, Random Forest Classifier, XGBoost, and KNN were evaluated using stratified five-fold cross-validation. Model performance was assessed using four key metrics: accuracy, precision, recall, and F1-score. Coarse hyperparameter tuning was employed to further enhance the model performance. Comparative analysis of the tested algorithms revealed SVC (RBF) to achieve the best prediction accuracy (97% for oils and 89.8% for chips). The framework reduces the total analysis time from hours to minutes by replacing conventional oil extraction with the newly optimized sampling technique. This new sampling strategy was validated using cross-correlation analysis and non-negative least squares (NNLS) spectral decomposition. The high correlation coefficient (ρ= 0.91) and high spectral contribution (56%) of oils in the chips spectra confirmed the preservation of the oil fingerprints within the chips spectra.
ER -