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 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.