Rapid and Accurate Measurement of Major Soybean Components Using Near-Infrared Spectroscopy
This study addresses the urgent need for the rapid, non-destructive assessment of key soybean components, including moisture, fat, and protein, using near-infrared (NIR) spectroscopy. This study provides technical and theoretical support for achieving the efficient and accurate detection of major so...
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| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/7/1505 |
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| author | Chenxiao Li Jiatong Yu Sheng Wang Qinglong Zhao Qian Song Yanlei Xu |
| author_facet | Chenxiao Li Jiatong Yu Sheng Wang Qinglong Zhao Qian Song Yanlei Xu |
| author_sort | Chenxiao Li |
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| description | This study addresses the urgent need for the rapid, non-destructive assessment of key soybean components, including moisture, fat, and protein, using near-infrared (NIR) spectroscopy. This study provides technical and theoretical support for achieving the efficient and accurate detection of major soybean components and for the development of portable near-infrared (NIR) instruments. Thirty soybean samples from diverse sources were collected, and 360 spectral measurements were acquired using a 900–1700 nm NIR spectrometer after grinding and standardized sampling. To improve model robustness, preprocessing strategies such as standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky–Golay derivatives were applied. Feature selection was conducted using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE), followed by model construction with partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF). Comparative analysis revealed that the RF model consistently outperformed the others across most combinations. Specifically, the SPASNV + D<sub>1</sub>–RF combination achieved an RPD of 14.7 for moisture, CARS–SNV + D<sub>1</sub>–RF reached 5.9 for protein, and CARS–SG + D<sub>2</sub>–RF attained 12.0 for fat, all significantly surpassing alternative methods and demonstrating a strong nonlinear learning capacity and predictive precision. These findings show that integrating optimal preprocessing and feature selection strategies can markedly enhance the predictive accuracy in NIR-based soybean analyses. The RF model offers exceptional stability and performance, providing both technical reference and theoretical support for the development of portable NIR devices and practical rapid-quality assessment systems for soybeans in industrial applications. |
| format | Article |
| id | doaj-art-090ff9a7030d4823a8e4b1771b98a047 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Agronomy |
| spelling | doaj-art-090ff9a7030d4823a8e4b1771b98a0472025-08-20T03:55:48ZengMDPI AGAgronomy2073-43952025-06-01157150510.3390/agronomy15071505Rapid and Accurate Measurement of Major Soybean Components Using Near-Infrared SpectroscopyChenxiao Li0Jiatong Yu1Sheng Wang2Qinglong Zhao3Qian Song4Yanlei Xu5College of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Physics, Jilin University, Changchun 130012, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaThis study addresses the urgent need for the rapid, non-destructive assessment of key soybean components, including moisture, fat, and protein, using near-infrared (NIR) spectroscopy. This study provides technical and theoretical support for achieving the efficient and accurate detection of major soybean components and for the development of portable near-infrared (NIR) instruments. Thirty soybean samples from diverse sources were collected, and 360 spectral measurements were acquired using a 900–1700 nm NIR spectrometer after grinding and standardized sampling. To improve model robustness, preprocessing strategies such as standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky–Golay derivatives were applied. Feature selection was conducted using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE), followed by model construction with partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF). Comparative analysis revealed that the RF model consistently outperformed the others across most combinations. Specifically, the SPASNV + D<sub>1</sub>–RF combination achieved an RPD of 14.7 for moisture, CARS–SNV + D<sub>1</sub>–RF reached 5.9 for protein, and CARS–SG + D<sub>2</sub>–RF attained 12.0 for fat, all significantly surpassing alternative methods and demonstrating a strong nonlinear learning capacity and predictive precision. These findings show that integrating optimal preprocessing and feature selection strategies can markedly enhance the predictive accuracy in NIR-based soybean analyses. The RF model offers exceptional stability and performance, providing both technical reference and theoretical support for the development of portable NIR devices and practical rapid-quality assessment systems for soybeans in industrial applications.https://www.mdpi.com/2073-4395/15/7/1505near-infrared spectroscopysoybeanchemometricsvariable selectionprediction model |
| spellingShingle | Chenxiao Li Jiatong Yu Sheng Wang Qinglong Zhao Qian Song Yanlei Xu Rapid and Accurate Measurement of Major Soybean Components Using Near-Infrared Spectroscopy Agronomy near-infrared spectroscopy soybean chemometrics variable selection prediction model |
| title | Rapid and Accurate Measurement of Major Soybean Components Using Near-Infrared Spectroscopy |
| title_full | Rapid and Accurate Measurement of Major Soybean Components Using Near-Infrared Spectroscopy |
| title_fullStr | Rapid and Accurate Measurement of Major Soybean Components Using Near-Infrared Spectroscopy |
| title_full_unstemmed | Rapid and Accurate Measurement of Major Soybean Components Using Near-Infrared Spectroscopy |
| title_short | Rapid and Accurate Measurement of Major Soybean Components Using Near-Infrared Spectroscopy |
| title_sort | rapid and accurate measurement of major soybean components using near infrared spectroscopy |
| topic | near-infrared spectroscopy soybean chemometrics variable selection prediction model |
| url | https://www.mdpi.com/2073-4395/15/7/1505 |
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