Establishment and application of risk classification model for lead in vegetables based on spectral clustering algorithms.

This study aims to evaluate the risk of lead pollution in 9 kinds of vegetables consumed by residents in 20 provinces/cities of China. Sampling data and vegetable consumption data from 20 provinces/cities in 2019 were used. Combined with dietary exposure assessment, the vegetable categories and provinces were paired, and a risk classification model based on spectral clustering algorithms was proposed. The results of the spectral clustering algorithm showed that the risk level of lead pollution in vegetables can be divided into five levels. The combination of vegetable-province/cities at the risk level of 1 and 2 accounted for 92.78%, and that at the risk level of 4 and 5 accounted for 2.22%. The high-risk combinations were fresh edible fungus-Shaanxi, fresh edible fungus-Sichuan, and fresh edible fungus-Shanghai and bean sprouts-Guangdong. In the proposed model, objective data were used as the classification index, and the spectral clustering algorithm was employed to select the optimal risk classification in a data-driven way. As a result, the influence of subjective factors was effectively reduced, the risk of lead pollution in vegetables was classified, and the results were scientific and accurate. This study provides a scientific basis of supervision priorities for regulatory departments.

A Voting-Based Ensemble Deep Learning Method Focused on Multi-Step Prediction of Food Safety Risk Levels: Applications in Hazard Analysis of Heavy Metals in Grain Processing Products.

Grain processing products constitute an essential component of the human diet and are among the main sources of heavy metal intake. Therefore, a systematic assessment of risk factors and early-warning systems are vital to control heavy metal hazards in grain processing products. In this study, we established a risk assessment model to systematically analyze heavy metal hazards and combined the model with the K-means++ algorithm to perform risk level classification. We then employed deep learning models to conduct a multi-step prediction of risk levels, providing an early warning of food safety risks. By introducing a voting-ensemble technique, the accuracy of the prediction model was improved. The results indicated that the proposed model was superior to other models, exhibiting the overall accuracy of 90.47% in the 7-day prediction and thus satisfying the basic requirement of the food supervision department. This study provides a novel early-warning model for the systematic assessment of the risk level and further allows the development of targeted regulatory strategies to improve supervision efficiency.