Analysis of Nutritional Content in Rice Seeds Based on Near-Infrared Spectroscopy
The nutritional quality of rice seeds is mainly determined by the content of key components such as protein, fat, and starch. Traditional chemical detection methods are time-consuming, labor-intensive, inefficient, and harmful to the environment. To overcome these limitations, this study developed a...
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MDPI AG
2025-05-01
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| author | Hengyuan Kong Jianing Wang Guanyu Lin Jianbo Chen Zhitao Xie |
| author_facet | Hengyuan Kong Jianing Wang Guanyu Lin Jianbo Chen Zhitao Xie |
| author_sort | Hengyuan Kong |
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| description | The nutritional quality of rice seeds is mainly determined by the content of key components such as protein, fat, and starch. Traditional chemical detection methods are time-consuming, labor-intensive, inefficient, and harmful to the environment. To overcome these limitations, this study developed a non-destructive detection method using near-infrared spectroscopy (1000–2200 nm) combined with linear regression modeling to achieve efficient and simultaneous multi-component analysis through the principle of anharmonic molecular vibration. By combining nutrient data from chemical analysis with spectroscopic measurements, we established a comprehensive rice seed composition dataset. After preprocessing with Gaussian denoising, first-order derivative transformation, SPA wavelength selection, and multiplicative scatter correction (MSC), we constructed partial least squares regression (PLS) and orthogonal partial least squares (OPLS), as well as artificial neural network (ANN) models. The OPLS model performed well in fat prediction (R<sup>2</sup> = 0.971, Q<sup>2</sup> = 0.926, RMSE = 0.175, RMSECV = 0.186), followed by starch (R<sup>2</sup> = 0.956, Q<sup>2</sup> = 0.907, RMSE = 0.159, RMSECV = 0.146) and protein (R<sup>2</sup> = 0.967, Q<sup>2</sup> = 0.936, RMSE = 0.164, RMSECV = 0.156). Our results confirm that the combination of the moving average, first order derivative, SPA, and MSC preprocessing of the OPLS model significantly improves the prediction. The developed non-destructive testing equipment provides a practical solution for automated, high-precision sorting of rice seeds based on nutrient composition. |
| format | Article |
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| institution | DOAJ |
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| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Photonics |
| spelling | doaj-art-6e16e84d12b1440da2f14e04096287e12025-08-20T03:14:39ZengMDPI AGPhotonics2304-67322025-05-0112548110.3390/photonics12050481Analysis of Nutritional Content in Rice Seeds Based on Near-Infrared SpectroscopyHengyuan Kong0Jianing Wang1Guanyu Lin2Jianbo Chen3Zhitao Xie4Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaCollege of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaThe nutritional quality of rice seeds is mainly determined by the content of key components such as protein, fat, and starch. Traditional chemical detection methods are time-consuming, labor-intensive, inefficient, and harmful to the environment. To overcome these limitations, this study developed a non-destructive detection method using near-infrared spectroscopy (1000–2200 nm) combined with linear regression modeling to achieve efficient and simultaneous multi-component analysis through the principle of anharmonic molecular vibration. By combining nutrient data from chemical analysis with spectroscopic measurements, we established a comprehensive rice seed composition dataset. After preprocessing with Gaussian denoising, first-order derivative transformation, SPA wavelength selection, and multiplicative scatter correction (MSC), we constructed partial least squares regression (PLS) and orthogonal partial least squares (OPLS), as well as artificial neural network (ANN) models. The OPLS model performed well in fat prediction (R<sup>2</sup> = 0.971, Q<sup>2</sup> = 0.926, RMSE = 0.175, RMSECV = 0.186), followed by starch (R<sup>2</sup> = 0.956, Q<sup>2</sup> = 0.907, RMSE = 0.159, RMSECV = 0.146) and protein (R<sup>2</sup> = 0.967, Q<sup>2</sup> = 0.936, RMSE = 0.164, RMSECV = 0.156). Our results confirm that the combination of the moving average, first order derivative, SPA, and MSC preprocessing of the OPLS model significantly improves the prediction. The developed non-destructive testing equipment provides a practical solution for automated, high-precision sorting of rice seeds based on nutrient composition.https://www.mdpi.com/2304-6732/12/5/481near infraredseedsspectral analysis |
| spellingShingle | Hengyuan Kong Jianing Wang Guanyu Lin Jianbo Chen Zhitao Xie Analysis of Nutritional Content in Rice Seeds Based on Near-Infrared Spectroscopy Photonics near infrared seeds spectral analysis |
| title | Analysis of Nutritional Content in Rice Seeds Based on Near-Infrared Spectroscopy |
| title_full | Analysis of Nutritional Content in Rice Seeds Based on Near-Infrared Spectroscopy |
| title_fullStr | Analysis of Nutritional Content in Rice Seeds Based on Near-Infrared Spectroscopy |
| title_full_unstemmed | Analysis of Nutritional Content in Rice Seeds Based on Near-Infrared Spectroscopy |
| title_short | Analysis of Nutritional Content in Rice Seeds Based on Near-Infrared Spectroscopy |
| title_sort | analysis of nutritional content in rice seeds based on near infrared spectroscopy |
| topic | near infrared seeds spectral analysis |
| url | https://www.mdpi.com/2304-6732/12/5/481 |
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