He Haiyan, Li Zhoutao, Qin Qian, Yu Yue, Guo Yuanxin, Cai Sheng, Li Zhanming
School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
Fujian Putian Sea 100 Food Co., Ltd., Putian 351111, China.
Foods. 2025 Jul 30;14(15):2679. doi: 10.3390/foods14152679.
Pesticide residues in fruits and vegetables pose a serious threat to food safety. Traditional detection methods have defects such as complex operation, high cost, and long detection time. Therefore, it is of great significance to develop rapid, non-destructive, and efficient detection technologies and equipment. In recent years, the combination of spectroscopic techniques and imaging technologies with machine learning algorithms has developed rapidly, providing a new attempt to solve this problem. This review focuses on the research progress of the combination of spectroscopic techniques (near-infrared spectroscopy (NIRS), hyperspectral imaging technology (HSI), surface-enhanced Raman scattering (SERS), laser-induced breakdown spectroscopy (LIBS), and imaging techniques (visible light (VIS) imaging, NIRS imaging, HSI technology, terahertz imaging) with machine learning algorithms in the detection of pesticide residues in fruits and vegetables. It also explores the huge challenges faced by the application of spectroscopic and imaging technologies combined with machine learning algorithms in the intelligent perception of pesticide residues in fruits and vegetables: the performance of machine learning models requires further enhancement, the fusion of imaging and spectral data presents technical difficulties, and the commercialization of hardware devices remains underdeveloped. This review has proposed an innovative method that integrates spectral and image data, enhancing the accuracy of pesticide residue detection through the construction of interpretable machine learning algorithms, and providing support for the intelligent sensing and analysis of agricultural and food products.
水果和蔬菜中的农药残留对食品安全构成严重威胁。传统检测方法存在操作复杂、成本高、检测时间长等缺陷。因此，开发快速、无损且高效的检测技术和设备具有重要意义。近年来，光谱技术和成像技术与机器学习算法的结合发展迅速，为解决这一问题提供了新的尝试。本文综述聚焦于光谱技术（近红外光谱（NIRS）、高光谱成像技术（HSI）、表面增强拉曼散射（SERS）、激光诱导击穿光谱（LIBS））和成像技术（可见光（VIS）成像、NIRS成像、HSI技术、太赫兹成像）与机器学习算法相结合在水果和蔬菜农药残留检测方面的研究进展。同时探讨了光谱和成像技术结合机器学习算法在水果和蔬菜农药残留智能感知应用中面临的巨大挑战：机器学习模型的性能有待进一步提高，成像与光谱数据的融合存在技术难题，硬件设备的商业化仍不发达。本文综述提出了一种整合光谱和图像数据的创新方法，通过构建可解释的机器学习算法提高农药残留检测的准确性，为农产品和食品的智能传感与分析提供支持。
