Rapid and Accurate Quantification Detection of BHT in Edible Oils Using Raman Spectroscopy Combined with Chemometric Models.

The chemical composition of vegetable cooking oils is a key parameter in determining the quality of their products. Antioxidants are widely used in these products to extend their shelf life. In this study, the concentration of butylated hydroxytoluene (BHT) in edible oil was quantitatively determined by Raman spectroscopy combined with chemometrics. Initially, Raman spectra of edible oil samples with varying concentrations of BHT were obtained. Subsequently, three variable selection methods were applied to the pre-processed spectra. Optimised characteristic wavelengths were then used to establish a Radial Basis Function (RBF) neural network and partial least squares (PLS) models. The impact of variable selection on feature wavelengths was evaluated for both models in both independent and combined cases. The results demonstrate that the features identified through multiple variable selection methods correlate highly with the BHT content and can be utilised to develop high-precision detection models. The findings indicate that the PLS model, optimised using competitive adaptive reweighting (CARS), achieved the best prediction performance, with an average RP2 of 0.9687, and RMSEP of 3.1211. These results demonstrate the feasibility of using Raman spectroscopy combined with chemometrics for the rapid screening of BHT in edible oils. While the current study focuses on a broad concentration range to validate the method's linearity, further optimisation is required for trace-level detection to meet strict regulatory limits.