Research on rapid prediction model of rice moisture content based on near infrared spectroscopy

<b>Objective:</b> In order to established a non-destructive, rapid and efficient method for detecting the moisture content of rice. <b>Methods:</b> This study, 161 rice samples were collected from 5 different regions were studied by near infrared spectroscopy combined with stoichiometry. By eliminating abnormal spectra and preprocessing the spectra, the prediction model of rice moisture content was established by partial least squares regression. <b>Results:</b> 15 abnormal spectrum samples were eliminated using the method of principal component analysis combined with mahalanobis distance. The best spectral pretreatment was to eliminate the constant offset. The prediction model <i>R</i>²_CAL established in the training set was 0.994 3, root mean square error of calibration （RMSEC） was 0.21%, <i>R</i>²_CV was 0.993 6, and root mean square error of cross validation （RMSECV） was 0.32%, which indicated that the cross-validation of the prediction model had high accuracy in predicting sample moisture content. The prediction model was tested with the validation set samples. The validation determination coefficient <i>R</i>²_VAL of the model validation set was 0.980 1, the root mean square error of prediction （RMSEP） was 0.36%, and the relative percent deviation （RPD） was 7.14, which indicated that the prediction model had high prediction accuracy for the unknown samples. The <i>P</i>-value （two-sided） of the mean equation <i>T</i>-test of the measured and predicted values of the samples in the validation set was 0.879, and the difference between the measured and predicted values of the samples in the validation set was not significant, indicating that the prediction results of the prediction model were highly reliable. The error between the predicted value and the measured value of the verification set samples was within ±1%, and more than 90% were within ±0.5%. <b>Conclusion:</b> The established rice moisture prediction model can be applied to actual production, and it provides a non-destructive, rapid and high-accuracy detection method for the inspection of rice harvesting and storage.