Raman spectroscopy coupled with the PLSR model: A rapid method for analyzing gamma-oryzanol content in rice bran oil
Pattamapan Lomarat, Chutima Phechkrajang, Pawida Sunghad, Natthinee Anantachoke
Abstract
Rice bran oil (RBO) is widely used in food, nutraceutical, and cosmetic industries, due to its γ-oryzanol content, a key quality indicator. This study developed a rapid, non-destructive method for quantifying γ-oryzanol in RBO using Raman spectroscopy combined with partial least squares regression (PLSR). The optimal PLSR model, based on orthogonal signal correction (OSC)-pretreated data of Raman spectra from 800 to 1800 cm −1 , demonstrated high accuracy with a strong R 2 -Pearson correlation coefficient of 0.9827 and low root mean square error of prediction (RMSEP) of 0.5314. Principal component analysis (PCA) of OSC-pretreated data showed improved sample grouping by concentration of γ-oryzanol compared to untreated data. Additionally, Bland-Altman plots comparing results from Raman and HPLC methods showed random scatter within ±2 SD of the mean difference, confirming the method's reliability. This study indicates that Raman spectroscopy can serve as a reliable method for determining γ-oryzanol content in RBO products within the related industries. • A rapid and non-destructive method for determining γ-oryzanol content in rice bran oil. • Raman spectroscopy is an effective tool for quantitative γ-oryzanol analysis. • Orthogonal signal correction and PLSR are statistical tools used for quantitative modeling. • The developed method provided reliable γ-oryzanol content consistent with HPLC results.