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Prediction of paralytic shellfish poisoning in the Bohai Sea by machine learning and its dietary risk assessment
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Li LI1, Huai-Xin LIANG1, *, Bei NIU2, Chang-Qing LIU2, Bin LIU1, Sang BA3
Journal of Food Safety & Quality | 2026, 17(7) : 288 - 294
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Journal of Food Safety & Quality | 2026, 17(7): 288-294
• Food Analysis and Detection •
Prediction of paralytic shellfish poisoning in the Bohai Sea by machine learning and its dietary risk assessment
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Li LI1, Huai-Xin LIANG1, *, Bei NIU2, Chang-Qing LIU2, Bin LIU1, Sang BA3
Affiliations
  • 1.Qinhuangdao Center for Disease Control and Prevention (Qinhuangdao Health Supervision Institute),Qinhuangdao 066000, China
  • 2.Hebei Center for Disease Control and Prevention, Shijiazhuang 050000, China
  • 3.Xizang Ali Center for Disease Control and Prevention, Ali 859000, China
doi: 10.19812/j.cnki.jfsq11-5956/ts.20251112005
Outline
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Objective

To predict the paralytic shellfish poisoning (PSP) in the Bohai Sea, explore the correlation between foodborne multi metallic elements contaminants and the levels of PSP, and complete the dietary risk assessment of PSP.

Methods

This study analyzed samples with relatively high detection rates of 5 kinds of PSP, including saxitoxin (STX), gonyautoxins2/3/4 (GTX2, GTX3, GTX4) and neosaxitoxin (neoSTX), collected in the Bohai Sea from 2018 to 2024. The maximal information coefficient (MIC) was used to analyze the correlation, redundancy and complementarity with the multi metallic element content in bivalves. Based on this, feature selection for multi metallic elements was conducted, and 5 kinds of classical machine learning models to predict PSP levels was utilized. The experiments employed 10-fold cross-validation, paired t-tests and compared the prediction accuracy using a 95% confidence interval (CI). Finally, the point assessment method was used to evaluate the dietary risks of 2 kinds of shellfish, Manila clams and mussels, in the Bohai Sea region for 5 different age groups.

Results

The feature selection method effectively identified multi metallic element hazard factors related to PSP content. The random forest (RF) model showed the largest reductions in mean absolute error (MAE) and root mean square error(RMSE) for predicting GTX3 of gonyautoxin, with reductions of 8.0% (95%CI: –0.0821 to –0.0779, P<0.05) and 9.1% (95%CI: –0.0997 to –0.0823, P<0.05) respectively. The RF model exhibited lower predictive errors, with MAE and RMSE values of 0.147 and 0.183 respectively. The dietary risks of consuming Manila clams and mussels from the Bohai Sea area were acceptable for people in all 5 age groups.

Conclusion

The dynamic feature selection method proposed in this study, which integrates MIC and redundancy-complementarity evaluation, can effectively uncover synergistic and antagonistic effects among multiple metal elements, significantly improving PSP prediction accuracy.Meanwhile, the dietary risks of relevant bivalves for all 5 age groups remain within safe levels, providing scientific support and technical reference for the safety management of bivalves in the Bohai Sea.

paralytic shellfish poisoning  /  multi metallic elements  /  prediction  /  feature selection  /  machine learning
李莉, 梁怀新, 牛蓓, 刘长青, 刘斌, 巴桑. 机器学习预测渤海中麻痹性贝类毒素及其膳食风险评估. 食品安全质量检测学报, 2026 , 17 (7) : 288 -294 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20251112005
Li LI, Huai-Xin LIANG, Bei NIU, Chang-Qing LIU, Bin LIU, Sang BA. Prediction of paralytic shellfish poisoning in the Bohai Sea by machine learning and its dietary risk assessment[J]. Journal of Food Safety & Quality, 2026 , 17 (7) : 288 -294 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20251112005
Year 2026 volume 17 Issue 7
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Article Info
doi: 10.19812/j.cnki.jfsq11-5956/ts.20251112005
  • Receive Date:2025-11-12
  • Online Date:2026-11-05
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  • Received:2025-11-12
Funding
Affiliations
    1.Qinhuangdao Center for Disease Control and Prevention (Qinhuangdao Health Supervision Institute),Qinhuangdao 066000, China
    2.Hebei Center for Disease Control and Prevention, Shijiazhuang 050000, China
    3.Xizang Ali Center for Disease Control and Prevention, Ali 859000, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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