Toward smart and in-situ mycotoxin detection in food via vibrational spectroscopy and machine learning

作者：Siyu Yao, Tong Yu, Alessandra Fantina Victorio Ramos, Zhongkun Zhang, Zulipikaer Rouzi, Luis Rodriguez‐Saona · 发表于：Food Chemistry X · 年份：2025 · DOI：10.1016/j.fochx.2025.103016 · 被引用次数：3 · 研究领域：Spectroscopy and Chemometric Analyses、Mycotoxins in Agriculture and Food、Identification and Quantification in Food

detection of mycotoxins in complex food matrices. Infrared and spontaneous Raman spectroscopy detect molecular vibrations or compositional changes in host matrices, capturing direct or indirect mycotoxin fingerprints, while surface-enhanced Raman spectroscopy (SERs) amplifies characteristic mycotoxins molecular vibrations via plasmonic nanostructures, enabling ultra-sensitive detection. Machine learning further enhances analysis by extracting subtle and unique mycotoxin spectral features from information-rich spectra, suppressing noise, and enabling robust predictions across heterogeneous samples. This review critically examines recent sensing strategies, model development, application performance, non-destructive screening, and potential application challenges, highlighting strengths and limitations relative to conventional methods. Innovations in portable, miniaturized spectrometers integrated with cloud computation are also discussed, supporting scalable, rapid, and on-site mycotoxin monitoring. By integrating state-of-art vibrational fingerprints with computational analysis, these approaches provide a pathway toward sensitive, smart, and field-deployable mycotoxin detection in food.