For the efficient selection of high-quality rice varieties, the near-infrared spectroscopy (NIRS) technique has been widely applied to detect constituents in single rice kernels. Compared with other constituents, amylose content (AC) and fat content (FC) are the key parameters that can affect the quality of rice. Based on two modified AC and FC trace detection methods, two NIRS methods to detect AC and FC in single rice kernels were developed. Using the proposed methods, the AC and FC in two groups of rice kernel datasets were measured. The datasets were collected on two spectrometers with different sample movement states (static and dynamic) and measurement modes (diffuse reflectance (NIRr) and diffuse transmission (NIRt)). By optimizing the pre-treatment method and spectral range, the determination coefficients of cross-validation (R²_cv) and prediction (R²_p) of the NIRS models under different measurement conditions were all above 0.6. The results indicated that the proposed methods were applicable to the rapid, non-destructive detection and sorting of individual rice seeds with different AC and FC, and it was shown that these methods can meet the requirements of the rough screening of rice seed varieties.

Internally mildewed sunflower seeds, which cannot be recognized and discarded based on their appearance, pose a serious risk to human health. Thus, there is a need for a rapid non-destructive mildew grade discrimination method. Currently, few reports are available regarding this process. In this study, a method based on the combination of the near-infrared diffuse reflectance and near-infrared diffuse transmission (NIRr-NIRt) fusion spectra and a one-dimension convolutional neural network (1D-CNN) is proposed. The NIRr-NIRt fusion spectra can provide more complementary and comprehensive information, and therefore better discrimination accuracy, than a single spectrum. The first derivative (FD) preprocessing method could further improve the discrimination effect. By comparison against three conventional machine learning algorithms (artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN)), the 1D-CNN model based on the fusion spectra was found to perform the best. The mean prediction accuracy was 2.01%, 5.97%, and 10.55% higher than that of the ANN, SVM, and KNN models, respectively. These results indicate that the CNN model was able to precisely classify the mildew grades with a prediction accuracy of 97.60% and 94.04% for the training and test set, respectively. Thus, this study provides a non-destructive and rapid method for classifying the mildew grade of sunflower seeds with the potential to be applied in the quality control of sunflower seeds.

Cultivating rice varieties with lower cellulose content in the bran layer has the potential to enhance both the nutritional value and texture of brown rice. This study aims to establish a rapid and accurate method to quantify cellulose content in the bran layer utilizing near-infrared spectroscopy (NIRS), thereby providing a technical foundation for the selection, screening, and breeding of rice germplasm cultivars characterized by a low cellulose content in the bran layer. To ensure the accuracy of the NIR spectroscopic analysis, the potassium dichromate oxidation (PDO) method was improved and then used as a reference method. Using 141 samples of rice bran layer (rice bran without germ), near-infrared diffuse reflectance (NIRdr) spectra, near-infrared diffuse transmittance (NIRdt) spectra, and fusion spectra of NIRdr and NIRdt were used to establish cellulose quantitative analysis models, followed by a comparative evaluation of these models’ predictive performance. Results indicate that the optimized PDO method demonstrates superior precision compared to the original PDO method. Upon examining the established models, their predictive capabilities were ranked in the following order: the fusion model outperforms the NIRdt model, which in turn surpasses the NIRdr model. Of all the fusion models developed, the model exhibiting the highest predictive accuracy utilized fusion spectra (NIRdr-NIRdt (1st der)) derived from preprocessed (first derivative) diffuse reflectance and transmittance spectra. This model achieved an external predictive R²_p of 0.903 and an RMSEP of 0.213%. Using this specific model, the rice mutant O2 was successfully identified, which displayed a cellulose content in the bran layer of 3.28%, representing a 0.86% decrease compared to the wild type (W7). The utilization of NIRS enables quantitative analysis of the cellulose content within the rice bran layer, thereby providing essential technical support for the selection of rice varieties characterized by lower cellulose content in the bran layer.

The chemical composition of individual hybrid rice (F2) varieties varies owing to genetic differences between parental lines, and the effects of these differences on eating quality are unclear. In this study, based on a self-developed near-infrared spectroscopy platform, we explored these effects among a set of 143 hybrid indica rice varieties with different eating qualities. The single-grain amylose content (SGAC) and single-grain protein content (SGPC) models were established with coefficients of determination (R²) of 0.9064 and 0.8847, respectively, and the dispersion indicators (standard deviation, variance, extreme deviation, quartile deviation, and coefficient of variation) were proposed to analyze the variations in the SGAC and SGPC based on the predicted results. Our correlation analysis found that the higher the variation in the SGAC and SGPC, the lower the eating quality of the hybrid indica rice. Moreover, the addition of the dispersion indicators of the SGAC and SGPC improved the R² of the eating quality model constructed using the composition-related physicochemical indicators (amylose content, protein content, alkali-spreading value, and gel consistency) from 0.657 to 0.850. Therefore, this new method proved to be useful for identifying high-eating-quality hybrid indica rice based on single near-infrared spectroscopy prior to processing and cooking.