Rapid Identification of Fritillaria Bulbus Botanical Origins and Adulteration by Thermal‐Assisted Plasma Ionization Time‐of‐Flight Mass Spectrometry Coupled With Chemometrics

Fritillaria Bulbus (FB) is a valuable traditional Chinese medicine with diverse pharmacological activities. Due to economic incentives, high-quality FBs are frequently adulterated with morphologically similar counterfeit species in commercial markets. A rapid analytical method integrating thermal-assisted plasma ionization time-of-flight mass spectrometry (TAPI-TOFMS) with chemometric algorithms was developed and validated for the botanical origin authentication and adulteration identification of FB. Featuring simple pretreatment, high throughput, rapid detection, and excellent reproducibility, the established method was systematically verified by analyzing a total of 172 FB samples. Conventional chemometric analyses (PCA, PLS-DA, OPLS-DA) achieved effective sample separation based on botanical origins and different authentic-counterfeit adulteration pairs. The constructed PLS-DA and OPLS-DA models presented satisfactory explanatory and predictive capacities with reliable statistical parameters. Furthermore, three machine learning algorithms (RF, SVM, ELM) were adopted to build identification models for authentic FBs and their adulterants. All models yielded high prediction accuracies for discriminating authentic CBs from counterfeits (> 94.0%) and for the four FB adulteration pairs (87.5%-100.0%). Among them, SVM exhibited the optimal comprehensive performance in accuracy, prediction confidence, and model stability, while all three algorithms achieved perfect identification accuracy for the QB/XJ adulteration pair. Nevertheless, the method still has limitations in low-concentration adulteration detection, with a minimum detectable adulteration level of 20%, which is mainly attributed to the non-chromatographic separation characteristic of TAPI-TOFMS and the high chemical similarity between authentic and counterfeit FB species. This rapid and robust methodology provides promising technical support for the quality supervision and authenticity identification of commercial FBs.