Security Risk Level Prediction of Carbofuran Pesticide Residues in Chinese Vegetables Based on Deep Learning

Examination of the relationship between agricultural carbon emission efficiency and food quality and safety: from the perspective of environmental regulation

An important breakthrough in the coordinated development of China's low-carbon goals and food security strategies is agricultural development oriented toward quality, safety, green, and low carbon. This study integrated command-control and market-incentive environmental regulation (ER), agricultural eco-efficiency (ACEE), and food quality and safety (FQS) into a unified theoretical framework. The unexpected output-oriented Super-SBM model was used to calculate the ACEE of China's provinces and cities from 2011 to 2020 and test the bidirectional causality between ACEE and FQS through the system generalized moment estimation model. A dynamic panel smooth transition (PSTR) model was used to explore the nonlinear impact mechanisms of different types of ERs on ACEE and FQS. The results showed that there was a long-term, two-way causal relationship between ACEE and FQS. The impact of environmental regulations on ACEE and FQS has a nonlinear relationship. Among them, the role of market-incentivized ER is more significant. Therefore, building an interregional coordinated development mechanism, improving the utilization rate of agricultural resources such as fertilizers and pesticides, and coordinating the positive effects of different types of ERs are the keys to improving the ACEE and ensuring the coordinated development of FQS.

Advancing food safety risk assessment in China: development of new approach methodologies (NAMs)

Novel techniques and methodologies are being developed to advance food safety risk assessment into the next-generation. Considering the shortcomings of traditional animal testing, new approach methodologies (NAMs) will be the main tools for the next-generation risk assessment (NGRA), using non-animal methodologies such as in vitro and in silico approaches. The United States Environmental Protection Agency and the European Food Safety Authority have established work plans to encourage the development and application of NAMs in NGRA. Currently, NAMs are more commonly used in research than in regulatory risk assessment. China is also developing NAMs for NGRA but without a comprehensive review of the current work. This review summarizes major NAM-related research articles from China and highlights the China National Center for Food Safety Risk Assessment (CFSA) as the primary institution leading the implementation of NAMs in NGRA in China. The projects of CFSA on NAMs such as the Food Toxicology Program and the strategies for implementing NAMs in NGRA are outlined. Key issues and recommendations, such as discipline development and team building, are also presented to promote NAMs development in China and worldwide.

Prediction of Safety Risk Levels of Benzopyrene Residues in Edible Oils in China Based on the Variable-Weight Combined LSTM-XGBoost Prediction Model

To assess and predict the food safety risk of benzopyrene (BaP) in edible oils in China, this study collected national sampling data of edible oils from 20 Chinese provinces and their prefectures in 2019, and constructed a risk assessment model of BaP in edible oils with consumption data. Initially, the k-means algorithm was used for risk classification; then the data were pre-processed and trained to predict the data using the Long Short-Term Memory (LSTM) and the eXtreme Gradient Boosting (XGBoost) models, respectively, and finally, the two models were combined using the inverse error method. To test the effectiveness of the prediction model, this study experimentally validated the model according to five evaluation metrics: root mean square error (RMSE), mean absolute error (MAE), precision, recall, and F1 score. The variable-weight combined LSTM-XGBoost prediction model proposed in this paper achieved a precision of 94.62%, and the F1 score value reached 95.16%, which is significantly better than other neural network models; the results demonstrate that the prediction model has certain stability and feasibility. Overall, the combined model used in this study not only improves the accuracy but also enhances the practicality, real-time capabilities, and expandability of the model.

Informer-Based Safety Risk Prediction of Heavy Metals in Rice in China

Focused supervision and early warning of heavy metal (HM)-contaminated rice areas can effectively protect people's livelihood security and maintain social stability. To improve the accuracy of risk prediction, an Informer-based safety risk prediction model for HMs in rice is constructed in this paper. First, based on the national sampling data and residential consumption statistics of rice, we construct a dataset of evaluation indicators that can characterize the level of rice safety risk so as to form a safety risk space. Second, based on the K-medoids clustering algorithm, we classify the rice safety risk space into levels. Finally, we use the Informer neural network model to predict the safety risk indicators of rice in each province so as to predict the safety risk level. This study compares the prediction accuracy of a self-constructed dataset of rice safety risk assessment indicators. The experimental results show that the prediction precision of the method proposed in this paper reaches 99.17%, 91.77%, and 91.33% for low, medium, and high risk levels, respectively. The model provides technical support and a scientific basis for screening the time and area of HM contamination of rice, which needs focus.

Prediction of Safety Risk Levels of Veterinary Drug Residues in Freshwater Products in China Based on Transformer

Early warning and focused regulation of veterinary drug residues in freshwater products can protect human health and stabilize social development. To improve the prediction accuracy, this paper constructs a Transformer-based model for predicting the safety risk level of veterinary drug residues in freshwater products in China to conduct a comprehensive assessment and prediction of the three veterinary drug residues with the maximum detection rate in freshwater products, including florfenicol, enrofloxacin and sulfonamides. Using the national sampling data and consumption data of freshwater products from 2019 to 2021, this paper constructs a self-built dataset, combined with the k-means algorithm, to establish the risk-level space. Finally, based on a Transformer neural network model, the safety risk assessment index is predicted on a self-built dataset, with the corresponding risk level for prediction. In this paper, comparison experiments are conducted on the self-built dataset. The experimental results show that the prediction model proposed in this paper achieves a recall rate of 94.14%, which is significantly better than other neural network models. The model proposed in this paper provides a scientific basis for the government to implement focused regulation, and it also provides technical support for the government’s intervention regulation.