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Analysis of Nutritional Content in Rice Seeds Based on Near-Infrared Spectroscopy

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Veröffentlicht in:Photonics vol. 12, no. 5 (2025), p. 481
1. Verfasser: Kong Hengyuan
Weitere Verfasser: Wang, Jianing, Lin Guanyu, Chen, Jianbo, Xie Zhitao
Veröffentlicht:
MDPI AG
Schlagworte:
Chemical analysis
Test equipment
Accuracy
Chemical detection
Anharmonicity
Nondestructive testing
Reagents
Artificial neural networks
Calibration
Least squares method
Infrared analysis
Grain
Soybeans
Infrared spectra
Infrared spectroscopy
Seeds
Dietary minerals
Proteins
Agriculture
Preprocessing
Nutritive value
Analytical chemistry
Near infrared radiation
Rice
Neural networks
Glucose
Vibration analysis
Methods
Nutrients
Algorithms
Light
Composition
Starch
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Abstract: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 (R2 = 0.971, Q2 = 0.926, RMSE = 0.175, RMSECV = 0.186), followed by starch (R2 = 0.956, Q2 = 0.907, RMSE = 0.159, RMSECV = 0.146) and protein (R2 = 0.967, Q2 = 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.
ISSN:2304-6732
DOI:10.3390/photonics12050481
Quelle:Advanced Technologies & Aerospace Database