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. 2024 Oct 24:24:101923.
doi: 10.1016/j.fochx.2024.101923. eCollection 2024 Dec 30.

Raman spectroscopy coupled with the PLSR model: A rapid method for analyzing gamma-oryzanol content in rice bran oil

Pattamapan Lomarat  1 , Chutima Phechkrajang  2 , Pawida Sunghad  3 , Natthinee Anantachoke  3   4
Affiliations

Raman spectroscopy coupled with the PLSR model: A rapid method for analyzing gamma-oryzanol content in rice bran oil

Pattamapan Lomarat et al. Food Chem X. .

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 R2-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.

Keywords: Bioactive compounds; Multivariate analysis; Oryzanol; Quality control; Raman spectroscopy; Rice bran oil.

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Conflict of interest statement

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Natthinee Anantachoke reports financial support was provided by Thailand Research Fund. Natthinee Anantachoke reports article publishing charges was provided by Mahidol University. Natthinee Anantachoke reports equipment, drugs, or supplies was provided by the Center of Excellence for Innovation in Chemistry. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
The schematic of RBO sample arrangement.
Fig. 2
Fig. 2
Raman spectra of RBO samples at various γ-oryzanol concentrations (2500, 5000, 7500, 10,000, 12,500, and 15,000 ppm) prepared using the standard addition method (A) and standard γ-oryzanol (B) were measured using a 10× objective lens, with an acquisition time of 40 s, 2 accumulations, a wavelength of 785 nm, and 100 % laser power.
Fig. 3
Fig. 3
PCA score plots (PA1 vs PC2) of unpretreated data (A) and OSC-pretreated data (B) (n = 100); Calibration plot (C) (n = 100) and prediction plot (D) (n = 20) of the optimal Raman PLSR model; The optimal model was developed using OSC-pretreated data (800–1800 cm−1). The relationship between the γ-oryzanol content in RBO samples, as predicted by the optimal model and analyzed by HPLC, was determined using Unscramble X version 10.5.
Fig. 4
Fig. 4
Bland-Altman plot of the prediction results showed randomly scattered data points for the validation samples within ±2 SD of the mean difference.
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