Rapid quantification of single component oil in perilla oil blends by ultraviolet–visible spectroscopy combined with chemometrics

Wang, Yao; Li, Zihan; Wang, Wenqiang; Liu, Peng; Tan, Xiaoyao; Bian, Xihui

doi:10.1016/j.saa.2024.124710

Spectrochimica Acta Part A: Molecular and Biomolecular Spectros

2024

DOI: 10.1016/j.saa.2024.124710

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Rapid quantification of single component oil in perilla oil blends by ultraviolet–visible spectroscopy combined with chemometrics

Yao Wang,

Zihan Li,

Wenqiang Wang

et al.

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Cited by 2 publications

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A portable LED-induced fluorescence system for quantitative detection of different kinds of vegetable oil adulteration

Liu,

Zhang,

et al. 2025

Journal of Food Composition and Analysis

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A portable LED-induced fluorescence system for quantitative detection of different kinds of vegetable oil adulteration

Liu,

Zhang,

et al. 2025

Journal of Food Composition and Analysis

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Quantitative detection of heavy metal Cd in vegetable oils: A nondestructive method based on Raman spectroscopy combined with chemometrics

Jiang,

Wang,

Deng

et al. 2024

Journal of Food Science

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Heavy metal contaminants in vegetable oils can cause irreversible damage to human health. In this study, the quantitative detection of Cd in vegetable oils was investigated based on Raman spectroscopy combined with chemometric methods. The necessary preprocessing of the Raman signal was performed using baseline calibration and the Savitzky–Golay method. Three variable optimization methods were applied to the preprocessed Raman spectra. Namely, bootstrap soft shrinkage, multiple feature spaces ensemble strategy with least absolute shrinkage and selection operator, and competitive adaptive reweighted sampling (CARS), respectively. Partial least squares regression (PLSR) modeling for the determination of Cd in vegetable oils. The results show that three variable optimization algorithms improved the predictive performance of the model. Among them, the CARS–PLSR model has strong generalization performance and robustness. Its prediction coefficient of determination () was 0.9995, the root mean square error of prediction was 0.3533 mg/kg, and the relative prediction deviation was 44.3748, respectively. In summary, rapid quantitative analysis of Cd contamination in vegetable oils can be realized based on Raman spectroscopy combined with chemometrics.

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Rapid quantification of single component oil in perilla oil blends by ultraviolet–visible spectroscopy combined with chemometrics

Cited by 2 publications

References 39 publications

A portable LED-induced fluorescence system for quantitative detection of different kinds of vegetable oil adulteration

A portable LED-induced fluorescence system for quantitative detection of different kinds of vegetable oil adulteration

Quantitative detection of heavy metal Cd in vegetable oils: A nondestructive method based on Raman spectroscopy combined with chemometrics

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