A Rice Hazards Risk Assessment Method for a Rice Processing Chain Based on a Multidimensional Trapezoidal Cloud Model

A safety risk assessment method based on conditionally constrained game theory and adaptive ensemble learning: Application to wheat flour and rice

Food safety risk control and comprehensive assessment are crucial measures to ensure food safety. However, existing food safety risk assessment methods face challenges, such as unreasonable weight distribution of hazard factors and poor adaptability. Therefore, a safety risk assessment model based on conditionally constrained game theory and adaptive ensemble learning is proposed in this paper. Firstly, new constraints are established on the traditional game theory combination weighting method and solved using the augmented Lagrange multiplier method to obtain the optimal linear combination coefficients and actual composite risk values of the samples, which are taken together with the hazard factor detection data as inputs to the adaptive ensemble learning model. Then, an adaptive ensemble learning model is constructed, which prefers the base learner based on the combined measure of stability and accuracy, and predicts the composite risk value by using robust weighted random forest as the meta-learner. Finally, the model's validity was verified using wheat flour and rice hazard factor detection data. The experimental results indicate that the model's fit on the two datasets is 0.996 and 0.991, respectively, demonstrating strong generalization ability and high prediction accuracy. Meanwhile, unqualified products in wheat flour and rice can be effectively identified through risk thresholds, which helps to provide early warning of potential safety risks.

The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning

A rice classification method for the fast and non-destructive differentiation of different varieties is significant in research at present. In this study, fluorescence hyperspectral technology combined with machine learning techniques was used to distinguish five rice varieties by analyzing the fluorescence hyperspectral features of Thai jasmine rice and four rice varieties with a similar appearance to Thai jasmine rice in the wavelength range of 475-1000 nm. The fluorescence hyperspectral data were preprocessed by a first-order derivative (FD) to reduce the background and baseline drift effects of the rice samples. Then, a principal component analysis (PCA) and t-distributed stochastic neighborhood embedding (t-SNE) were used for feature reduction and 3D visualization display. A partial least squares discriminant analysis (PLS-DA), BP neural network (BP), and random forest (RF) were used to build the rice classification models. The RF classification model parameters were optimized using the gray wolf algorithm (GWO). The results show that FD-t-SNE-GWO-RF is the best model for rice classification, with accuracy values of 99.8% and 95.3% for the training and test sets, respectively. The fluorescence hyperspectral technique combined with machine learning is feasible for classifying rice varieties.