Non-destructive food authentication by vapor-assisted desorption chemical ionization mass spectrometry paired with machine learning

作者：Yijun Wang, Shuqing Gao, Junxian Wu, Xiang Li, Yaqiu Zhao, Zhou Li, Yutong Hua, Liping Kang, Zi‐Dong Qiu · 发表于：npj Science of Food · 年份：2025 · DOI：10.1038/s41538-025-00650-1 · 被引用次数：3 · 研究领域：Mass Spectrometry Techniques and Applications、Metabolomics and Mass Spectrometry Studies、Traditional Chinese Medicine Analysis

The in-situ, rapid and non-destructive identification of complex food samples is a long-standing core technical challenge in food chemistry. Herein, vapor assisted desorption chemical ionization mass spectrometry (VADCI-MS) device was independently constructed, which facilitates rapid and non-destructive analysis of complex foods with diverse morphologies. Additionally, an integrated strategy that combines MS fingerprinting, feature selection, and machine learning classification models was established and is particularly effective for complex foods that are challenging to differentiate using conventional methods. As a paradigm, VADCI-MS was employed to rapidly (<1.0 min/sample) and non-destructively obtain the MS fingerprints of 101 batches of Zanthoxylum bungeanum (ZB) samples and 415 feature peaks were selected. Further, using machine learning classification models, a high accuracy differentiation of ZB from different origins was achieved (accuracy rate = 96.88%) and was verified to be substantially improved over traditional GC-MS. Conclusively, VADCI-MS coupled with machine learning holds significant potential for accurate and non-destructive identification of foods.