Global Perspectives on Mycotoxin Reference Materials (Part I): Insights from Multi-Supplier Comparison Study Including Aflatoxin B1, Deoxynivalenol and Zearalenone

We conducted a comprehensive examination of liquid mycotoxin reference standards. A total of 30 different standards were tested, each containing 10 samples of three distinct substances: Aflatoxin B1, Deoxynivalenol, and Zearalenone. The standards were sourced from 10 different global market leading manufacturers. To facilitate comparison, all the standard sets were adjusted to the same concentration level. The standards were analyzed using the techniques LC-MS/MS, HPLC-DAD, and LC-HRMS to assess their quality attributes. Regarding the validation of the reference values, it was observed that 30% of the suppliers provided reference standards that were either below the lower acceptance limit or above the higher acceptance limit, confirmed by both the LC-MS/MS and HPLC-DAD methods. Furthermore, a total of 12 impurities were found in the DON standards, 10 in the AFB1 standards, and 8 in the ZON standards, distributed across all the suppliers. Therefore, this study suggests relevant adjustments to the ISO 17034 standard, proposing that the purity of a raw material should be uniformly based on q-NMR analysis, as most manufacturers state the purity of their certificates is determined using HPLC-UV or LC-MS/MS. Liquid standards with a shelf life of ≤1 year should not exceed an uncertainty of 3%. Standards that have a longer shelf life should not have more than 5% uncertainty. This study also emphasizes the importance of stability. The standards should undergo continuous long-term monitoring; otherwise, products may exhibit a target value of only 80%, as seen in one instance. It is also recommended to include proof of HPLC and LC-MS/MS analyses on the certificate of each released batch of a final product.

Purity Assessment of Dinotefuran Using Mass Balance and Quantitative Nuclear Magnetic Resonance

Dinotefuran (DNT) belongs to the third-generation neonicotinoid pesticides, which are among the most common residuals in a variety of food commodities. To guarantee accurate quantification and traceability of results in food samples, certified reference materials (CRMs) are the indispensable benchmark. In this work, a DNT CRM was characterized and its purity was assessed by two independent methods, including mass balance (MB) and quantitative nuclear magnetic resonance spectroscopy (qNMR). The mass fraction of moisture was 0.33 mg/g, the inorganic impurity was 0.01 mg/g, and no detectable organic solvent was detected. Benzoic acid was chosen as the internal standard for qNMR. Its mass fraction was 997.9 mg/g and 992.9 mg/g by MB and qNMR, respectively. Eventually, the DNT CRM was assigned a mass fraction of 995 mg/g, with expanded uncertainty of 5 mg/g (k = 2). This CRM can be used to prepare calibrant solutions and is applicable to national routine monitoring of DNT residuals in agro-products and food.

Impurity Profiling of Dinotefuran by High Resolution Mass Spectrometry and SIRIUS Tool

Dinotefuran (DNT) is a neonicotinoid insecticide widely used in pest control. Identification of structurally related impurities is indispensable during material purification and pesticide registration and certified reference material development, and therefore needs to be carefully characterized. In this study, a combined strategy with liquid chromatography high-resolution mass spectrometry and SIRIUS has been developed to elucidate impurities from DNT material. MS and MS/MS spectra were used to score the impurity candidates by isotope score and fragment tree in the computer assisted tool, SIRIUS. DNT, the main component, worked as an anchor for formula identification and impurity structure elucidation. With this strategy, two by-product impurities and one stereoisomer were identified. Their fragmentation pathways were concluded, and the mechanism for impurity formation was also proposed. This result showed a successful application for combined human intelligence and machine learning, in the identification of pesticide impurities.

Impurity identification in thiamethoxam by high resolution mass spectrometry and computer assisted elucidation

Thiamethoxam (TMX) is a widely used neonicotinoid insecticide in pest control. Identification of structurally related impurities is very important during certified reference material development and pesticide registration, thus it needs to be carefully characterized. In this study, a combined strategy with liquid chromatography-high resolution mass spectrometry and computer assisted elucidation (SIRIUS) has been developed for the impurity elucidation in TMX material. MS and MS/MS spectra were used to score the impurity candidates by isotope score and fragment tree in SIRIUS. TMX, the main component, worked as an anchor for formula identification and structure elucidation of impurity. With this strategy, four impurities were identified, including two byproducts (TMX-OCH₃ and TMX-Cl) and two metabolites (clothianidin and TMX-urea). Their fragmentation pathways were concluded, and mechanism of impurity formation was also proposed. This result showed successful application of combining human intelligence with machine learning in impurity identification from chemicals.