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Quantitative Analysis of Peanut Skin Adulterants by Fourier Transform Near-Infrared Spectroscopy Combined with Chemometrics

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Vydáno v:Foods vol. 14, no. 3 (2025), p. 466
Hlavní autor: Luo, Wangfei
Další autoři: Deng, Jihong, Li, Chenxi, Jiang, Hui
Vydáno:
MDPI AG
Témata:
Skin
Food safety
Potatoes
Algorithms
Adaptive sampling
Least squares method
Data processing
Infrared analysis
Starch
Adulterants
Chromatography
Machine learning
Fourier transforms
Fourier analysis
Infrared spectra
Infrared spectroscopy
Adaptive algorithms
Spectrum analysis
Support vector machines
Vegetables
Sweet potatoes
Near infrared radiation
Corn
Quantitative analysis
Data collection
Methods
Peanuts
Legumes
Spectroscopic analysis
Microbiota
Ipomoea batatas
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Abstrakt:Peanut skin is a potential medicinal material. The adulteration of peanut skin samples with starchy substances severely affects their medicinal value. This study aimed to quantitatively analyze the adulterants present in peanut skin using Fourier transform near-infrared (FT-NIR) spectroscopy. Two adulterants, sweet potato starch and corn starch, were included in this study. First, spectral information of the adulterated samples was collected for characterization. Then, the applicability of different preprocessing methods and techniques to the obtained spectral data was compared. Subsequently, the Competitive Adaptive Reweighted Sampling (CARS) algorithm was used to extract effective variables from the preprocessed spectral data, and Partial Least Squares Regression (PLSR), a Support Vector Machine (SVM), and a Black Kite Algorithm-Support Vector Machine (BKA-SVM) were employed to predict the adulterant content in the samples, as well as the overall adulteration level. The results showed that the BKA-SVM model performed excellently in predicting the content of sweet potato starch, corn starch, and overall adulterants, with determination coefficients (<inline-formula>RP2</inline-formula>) of 0.9833, 0.9893, and 0.9987, respectively. The experimental results indicate that FT-NIR spectroscopy combined with advanced machine learning techniques can effectively and accurately detect adulterants in peanut skin, providing a reliable technological support for food safety detection.
ISSN:2304-8158
DOI:10.3390/foods14030466
Zdroj:Agriculture Science Database