Phytomedicine : international journal of phytotherapy and phytopharmacology · 2026
A collaborative strategy of multi-technology integration for mycotoxin risk assessment and early warning in edible and medicinal plants.
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Fang Ling, Liu Xiaokang, Liu Kangnan, Feng Lin, Tang Zijun, Li Zhenwei, Zhang Daidi, Yang Wenzhi, Guo De-An
Аннотация
BACKGROUND: Mycotoxins are toxic secondary metabolites of toxigenic fungi, posing a significant threat to human health due to their widespread contamination in edible and medicinal plants (EMPs) and synergistic toxicity.
PURPOSE: This study aims to design a collaborative multi-technology integration strategy for mycotoxin risk assessment and early warning in EMPs.
METHOD: Ultra-performance liquid chromatography-triple quadrupole linear ion trap mass spectrometry (UPLC-Q-TRAP-MS) was used for the quantitative analysis of 19 mycotoxins in four EMPs, and probabilistic Monte Carlo simulation (MCS) was employed to quantify their dietary exposure risk. Then, single-molecule real-time (SMRT) sequencing was applied to identify toxigenic fungi in the four EMPs. Given the high contamination rates and potential risks of aflatoxin B1 (AFB1) and zearalenone (ZEN) in Coicis semen, as well as ochratoxin A (OTA) and sterigmatocystin (ST) in Lilii bulbus, near-infrared spectroscopy (NIRS) combined with machine learning was utilized for the rapid and non-destructive screening of these mycotoxins.
RESULTS: Among 259 batches, 240 (92.66%) tested positive for at least one mycotoxin. Probabilistic risk assessments highlighted AFB1, ST, alternariol monomethyl ether (AME), and ZEN as priority controls due to public health risks. Moreover, SMRT sequencing not only identified Aspergillus, Fusarium, and Alternaria as the main toxigenic fungi but also revealed a potential association between the abundance of these toxigenic fungi and their secondary metabolites (mycotoxins). After evaluating ten machine learning models, k-nearest neighbor (KNN) and support vector machine (SVM) exhibited excellent performance in low- and high-risk classification, with the test set achieving 100 % classification accuracy for these target mycotoxins. Independent external validation confirmed the models' good applicability, ZEN in Coicis semen reached 90 % predictive accuracy, while that of AFB1 in Coicis semen, OTA and ST in Lilii bulbus all reached 100 %.
CONCLUSION: This study offers valuable insights for EMP mycotoxin risk management and early warning to safeguard public health.
PURPOSE: This study aims to design a collaborative multi-technology integration strategy for mycotoxin risk assessment and early warning in EMPs.
METHOD: Ultra-performance liquid chromatography-triple quadrupole linear ion trap mass spectrometry (UPLC-Q-TRAP-MS) was used for the quantitative analysis of 19 mycotoxins in four EMPs, and probabilistic Monte Carlo simulation (MCS) was employed to quantify their dietary exposure risk. Then, single-molecule real-time (SMRT) sequencing was applied to identify toxigenic fungi in the four EMPs. Given the high contamination rates and potential risks of aflatoxin B1 (AFB1) and zearalenone (ZEN) in Coicis semen, as well as ochratoxin A (OTA) and sterigmatocystin (ST) in Lilii bulbus, near-infrared spectroscopy (NIRS) combined with machine learning was utilized for the rapid and non-destructive screening of these mycotoxins.
RESULTS: Among 259 batches, 240 (92.66%) tested positive for at least one mycotoxin. Probabilistic risk assessments highlighted AFB1, ST, alternariol monomethyl ether (AME), and ZEN as priority controls due to public health risks. Moreover, SMRT sequencing not only identified Aspergillus, Fusarium, and Alternaria as the main toxigenic fungi but also revealed a potential association between the abundance of these toxigenic fungi and their secondary metabolites (mycotoxins). After evaluating ten machine learning models, k-nearest neighbor (KNN) and support vector machine (SVM) exhibited excellent performance in low- and high-risk classification, with the test set achieving 100 % classification accuracy for these target mycotoxins. Independent external validation confirmed the models' good applicability, ZEN in Coicis semen reached 90 % predictive accuracy, while that of AFB1 in Coicis semen, OTA and ST in Lilii bulbus all reached 100 %.
CONCLUSION: This study offers valuable insights for EMP mycotoxin risk management and early warning to safeguard public health.