Nontargeted screening method for detection of illicit adulterants in dietary supplements and herbal medicines using UHPLC-QTOF-MS with fine-tuned Spec2Vec-based spectral similarity and chemical classification filter.

Liquid chromatography-mass spectrometry (LC-MS) is a widely utilized technique for inspecting adulteration. Unscrupulous businesses persistently introduce novel illegal adulterants, making it necessary to develop methods to screen compounds not present in the current library. Conventional cosine similarity for mass spectral libraries matching is limited in their ability to identify structurally similar compounds. In our previous study, comparison of performance among four advanced similarity algorithms revealed that Spec2Vec exhibited the best performance in terms of both detection capability and false discovery rate, making it the chosen method for identifying illegal adulterants. However, Spec2Vec still exhibited worse performance compared to MS2DeepScore and entropy similarity in the aspects of detection capability and false discovery rate, respectively. In this study, our objective was to optimize the performance of spectral similarity for a specific compound class by fine-tuning a pretrained Spec2Vec model. Additionally, we implemented the chemical classification tool CANOPUS to address the issue of similarities in backbone structures between illegal adulterants and compounds found in herbal medicine, which can lead to false positives. We utilized glucocorticoids as potentially illicit adulterants to provide a proof-of-concept, and the results demonstrated that the fine-tuned Spec2Vec model not only exhibits a significant improvement in detection ability compared to the original model but also achieves comparable performance to MS2Deepscore. Moreover, the fine-tuned Spec2Vec model shows notably fewer false positives in comparison to MS2Deepscore. Overall, this proposed pipeline demonstrates high effectiveness and competitiveness in inspecting illegal adulterants, enhancing the analysis of large-scale MS data.

Fast screening and identification of illegal adulteration in dietary supplements and herbal medicines using molecular networking with deep-learning-based similarity algorithms.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a powerful analytical tool used for adulteration inspection. Nevertheless, it is a challenging task to identify illegal adulterants that are not included in the library or are unexpected from large MS data. Molecular networking is a good tool for exploring, visualizing, and organizing MS/MS spectra, and moreover, it employs shifted peak match to calculate spectral similarity, making it capable of identifying adulteration that is not included in the library. The key of molecular networking is spectral similarity algorithms, and therefore, in this study, we compared the performance of four cutting-edge similarity algorithms, modified cosine similarity (shifted peak match), entropy similarity, and two deep-learning-based algorithms, MS2DeepScore and Spec2Vec, in building molecular networking for identification of adulteration that is not included in the library. We conducted an analysis of excluded-query-compound on all MS/MS spectra in test library and performed a large-scale false discovery rate estimation to investigate whether the spectral similarity calculated by each algorithm could represent the actual structural similarity well. The obtained results demonstrated Spec2Vec exhibited good performance in both detection capability and false discovery rate. Further comprehensive evaluation of the performance of Spec2Vec in the identification of adulteration that is not included in the library or is unexpected in different matrices and in application to real samples proved the approach studied here is a promising and powerful tool for adulterant inspection and improved the capability of analyzing large MS data.

Fast Screening and Identification of Illegal Adulterated Glucocorticoids in Dietary Supplements and Herbal Products Using UHPLC-QTOF-MS With All-Ion Fragmentation Acquisition Combined With Characteristic Fragment Ion List Classification.

Ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) with all-ion fragmentation (AIF) acquisition was established for an identification and quantification of illegal adulterated glucocorticoids in dietary supplements and herbal products. Next, a novel method called characteristic fragment ion list classification (CFILC) was developed for a fast screening of adulterated compounds. CFILC could provide the characteristic ions comprehensively and completely through direct extract from the MS2 library instead of finding them manually. This is time-saving and provides fast screening results with a high confidence level by filtering of a pre-calculated threshold of similarity scores for illegal adulterants that are not included in the library as well as for new emerging structural analogs. The obtained results demonstrated the great qualitative and quantitative strength of this approach, providing a promising and powerful method for a routine fast screening of illegal adulterated glucocorticoids.