Kabir Md Ahasan, Lee Ivan, Singh Chandra B, Mishra Gayatri, Panda Brajesh Kumar, Lee Sang-Heon
UniSA STEM, University of South Australia, Mawson Lakes 5095, Australia.
Department of Electronics and Telecommunication Engineering, Chittagong University of Engineering and Technology, Chittagong 4349, Bangladesh.
Toxins (Basel). 2025 Apr 27;17(5):219. doi: 10.3390/toxins17050219.
Cereal grains and nuts are the world's most produced food and the economic backbone of many countries. Food safety in these commodities is crucial, as they are highly susceptible to mold growth and mycotoxin contamination in warm, humid environments. This review explores hyperspectral imaging (HSI) integrated with machine learning (ML) algorithms as a promising approach for detecting and quantifying mycotoxins in cereal grains and nuts. This study aims to (1) critically evaluate current non-destructive techniques for processing these foods and the applications of ML in identifying mycotoxins through HSI, and (2) highlight challenges and potential future research directions to enhance the reliability and efficiency of these detection systems. The ML algorithms showed effectiveness in classifying and quantifying mycotoxins in grains and nuts, with HSI systems increasingly adopted in industrial settings. Mycotoxins exhibit heightened sensitivity to specific spectral bands within HSI, facilitating accurate detection. Additionally, selecting only relevant spectral features reduces ML model complexity and enhances reliability in the detection process. This review contributes to a deeper understanding of the integration of HSI and ML for food safety applications in cereal grains and nuts. By identifying current challenges and future research directions, it provides valuable insights for advancing non-destructive mycotoxin detection methods in the food industry using HSI.
谷物和坚果是世界上产量最高的食品，也是许多国家的经济支柱。这些商品的食品安全至关重要，因为它们在温暖潮湿的环境中极易受到霉菌生长和霉菌毒素污染的影响。本综述探讨了将高光谱成像（HSI）与机器学习（ML）算法相结合，作为一种检测和定量谷物和坚果中霉菌毒素的有前景的方法。本研究旨在（1）批判性地评估当前用于加工这些食品的非破坏性技术以及ML在通过HSI识别霉菌毒素方面的应用，（2）突出挑战和潜在的未来研究方向，以提高这些检测系统的可靠性和效率。ML算法在对谷物和坚果中的霉菌毒素进行分类和定量方面显示出有效性，HSI系统在工业环境中越来越多地被采用。霉菌毒素对HSI内的特定光谱带表现出更高的敏感性，便于准确检测。此外，仅选择相关的光谱特征可降低ML模型的复杂性，并提高检测过程的可靠性。本综述有助于更深入地理解HSI和ML在谷物和坚果食品安全应用中的整合。通过识别当前的挑战和未来的研究方向，它为利用HSI推进食品工业中的非破坏性霉菌毒素检测方法提供了有价值的见解。
