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

Abstract

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.

Phytochemical and pharmacological properties of the genus Alpinia from 2016 to 2023

“…54 Alpinone-3-acetate (297), the acetylated form of 295, was isolated from A. japonica. 64 In addition, chalcone derivatives were found in this species including cardamonin (298), 22,54,55 helichrysetin (299), 22 and avokawain B (300). 58,70 An isoavone…”

“…32 (2R,3S)-Pinobanksin-3-cinnamate (292) was isolated from A. galanga as a avonol-type conjugated with a cinnamic acid. 100 A avanone derivative, pinocembrin (293), was found in A. zerumbet, 20,[53][54][55] and alpinetin (294) is the main active ingredient in A. katsumadae and A. mutica. [56][57][58][59] Pinostrobin (295) and 5,7-dimethoxyavanone (296) were obtained by methylating 293 in a structure-activity study.…”

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

“…Most of the research articles focused on developing AI models to predict the safety levels of certain hazards of different food products. The research team of Dong used AI methods such as neural networks and deep learning to predict the safety risk levels divided by clustering machine learning technique of contaminants such as heavy metals, veterinary drugs (florfenicol, enrofloxacin, and sulfonamide) residues, and arbofuran pesticide residues in different food categories (Jiang et al, 2022a;Jiang et al, 2022b;Dong et al, 2023;Lu et al, 2023). Wang et al (2022) used the voting-ensemble deep learning method and the submodels to analyze the risks of heavy metals in grain products.…”

Prediction of Food Safety Risk Level of Wheat in China Based on Pyraformer Neural Network Model for Heavy Metal Contamination

“…Tavoloni et al [18] performed temporal trend analysis and predicted the heavy metal levels in clams and mussels as a way to assess environmental safety, which allowed for the assessment of contaminants in water bodies and sediments. Lu et al [19] used deep learning algorithms to assess and predict the risk of contamination levels of Cd, Cr and As metals and metalloids in Chinese rice. The vast area of China, the difference in climate between the north and the south, the difference in economic development between the east and the west and the difference in soil topography and geology were expected to lead to heavy metal pollution in soil, showing the characteristics of geological factors [20].…”