Quantitative Analysis of Peanut Skin Adulterants by Fourier Transform Near-Infrared Spectroscopy Combined with Chemometrics
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| Published in: | Foods vol. 14, no. 3 (2025), p. 466 |
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| Main Author: | |
| Other Authors: | , , |
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MDPI AG
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| Online Access: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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| 001 | 3165817809 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2304-8158 | ||
| 024 | 7 | |a 10.3390/foods14030466 |2 doi | |
| 035 | |a 3165817809 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231462 |2 nlm | ||
| 100 | 1 | |a Luo, Wangfei | |
| 245 | 1 | |a Quantitative Analysis of Peanut Skin Adulterants by Fourier Transform Near-Infrared Spectroscopy Combined with Chemometrics | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Skin | ||
| 653 | |a Food safety | ||
| 653 | |a Potatoes | ||
| 653 | |a Algorithms | ||
| 653 | |a Adaptive sampling | ||
| 653 | |a Least squares method | ||
| 653 | |a Data processing | ||
| 653 | |a Infrared analysis | ||
| 653 | |a Starch | ||
| 653 | |a Adulterants | ||
| 653 | |a Chromatography | ||
| 653 | |a Machine learning | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Fourier analysis | ||
| 653 | |a Infrared spectra | ||
| 653 | |a Infrared spectroscopy | ||
| 653 | |a Adaptive algorithms | ||
| 653 | |a Spectrum analysis | ||
| 653 | |a Support vector machines | ||
| 653 | |a Vegetables | ||
| 653 | |a Sweet potatoes | ||
| 653 | |a Near infrared radiation | ||
| 653 | |a Corn | ||
| 653 | |a Quantitative analysis | ||
| 653 | |a Data collection | ||
| 653 | |a Methods | ||
| 653 | |a Peanuts | ||
| 653 | |a Legumes | ||
| 653 | |a Spectroscopic analysis | ||
| 653 | |a Microbiota | ||
| 653 | |a Ipomoea batatas | ||
| 700 | 1 | |a Deng, Jihong | |
| 700 | 1 | |a Li, Chenxi | |
| 700 | 1 | |a Jiang, Hui | |
| 773 | 0 | |t Foods |g vol. 14, no. 3 (2025), p. 466 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3165817809/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3165817809/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3165817809/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |