Quantitative determination of acid value in palm oil during thermal oxidation using Raman spectroscopy combined with deep learning models

Abstract

Accurate monitoring of acid value (AV) is critical for edible oil quality control, yet traditional chemometric methods often face limitations in handling complex spectral data. This study combines Raman spectroscopy with deep learning, including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Transformer, to explore their potential in improving the accuracy and efficiency of AV quantification during the thermal oxidation of palm oil. The results showed that all three deep learning models outperformed traditional chemometric methods in predictive accuracy. The CNN-LSTM model achieved the best performance, with a predicted coefficient of determination (R_p²) of 0.9978, a mean square error of prediction (RMSEP) of 0.0015, and a residual predictive deviation (RPD) of 21.21. This method demonstrates the effectiveness of Raman spectroscopy-driven deep learning for precise AV monitoring and holds promise for further validation with more diverse indicator datasets, providing a novel technical reference for edible oil quality control.