Deng Jihong, Zhao Mingxing, Jiang Hui
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
Foods. 2025 Jul 30;14(15):2688. doi: 10.3390/foods14152688.
Grains and their derivatives play a crucial role as staple foods for the global population. Identifying grains in the food chain that are free from mycotoxin contamination is essential. Researchers have explored various traditional detection methods to address this concern. However, as grain consumption becomes increasingly time-sensitive and dynamic, traditional approaches face growing limitations. In recent years, emerging techniques-particularly molecular-based vibrational spectroscopy methods such as visible-near-infrared (Vis-NIR), near-infrared (NIR), Raman, mid-infrared (MIR) spectroscopy, and hyperspectral imaging (HSI)-have been applied to assess fungal contamination in grains and their products. This review summarizes research advances and applications of vibrational spectroscopy in detecting mycotoxins in grains from 2019 to 2025. The fundamentals of their work, information acquisition characteristics and their applicability in food matrices were outlined. The findings indicate that vibrational spectroscopy techniques can serve as valuable tools for identifying fungal contamination risks during the production, transportation, and storage of grains and related products, with each technique suited to specific applications. Given the close link between grain-based foods and humans, future efforts should further enhance the practicality of vibrational spectroscopy by simultaneously optimizing spectral analysis strategies across multiple aspects, including chemometrics, model transfer, and data-driven artificial intelligence.
谷物及其衍生物作为全球人口的主食发挥着至关重要的作用。识别食物链中未受霉菌毒素污染的谷物至关重要。研究人员探索了各种传统检测方法来解决这一问题。然而，随着谷物消费对时间的敏感性和动态性日益增强，传统方法面临着越来越多的局限性。近年来，新兴技术——特别是基于分子的振动光谱方法，如可见-近红外（Vis-NIR）、近红外（NIR）、拉曼、中红外（MIR）光谱和高光谱成像（HSI）——已被应用于评估谷物及其产品中的真菌污染。本综述总结了2019年至2025年振动光谱在检测谷物中霉菌毒素方面的研究进展和应用。概述了它们的工作原理、信息获取特性及其在食品基质中的适用性。研究结果表明，振动光谱技术可作为识别谷物及相关产品生产、运输和储存过程中真菌污染风险的有价值工具，每种技术都适用于特定应用。鉴于以谷物为基础的食品与人类之间的紧密联系，未来的工作应通过同时在多个方面优化光谱分析策略，包括化学计量学、模型转移和数据驱动的人工智能，进一步提高振动光谱的实用性。
