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

• Published in: Food science & nutrition Vol. 10; no. 3; pp. 879 - 887
• Main Authors: Jiang, Tong‐qiang, Wang, Zheng, Zhang, Qing‐chuan, Wang, Zu‐zheng, Cheng, Bao‐lian
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.03.2022; John Wiley and Sons Inc
• Subjects: Algorithms; Beansprouts; Cities; Classification; Clustering; Confidence intervals; Crop diseases; Crops; Diet; Drug dosages; Food safety; Fungi; Health risk assessment; Health risks; International organizations; lead; Lead content; Mathematical functions; Monte Carlo simulation; Original; Original Research; Pollutants; Pollution; Provinces; risk assessment; risk classification; Risk levels; Spectra; spectral clustering algorithms; Vegetables; Fujian China; United States > US; China; risk assessment; risk classification; vegetables; spectral clustering algorithms; lead
• ISSN: 2048-7177; 2048-7177
• DOI: 10.1002/fsn3.2718

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. The high‐risk combinations were fresh edible fungus‐Shaanxi, fresh edible fungus‐Sichuan and fresh edible fungus‐Shanghai and bean sprouts‐Guangdong. The high‐risk combinations mainly indicated the priority of attention.