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收获后农产品变质特征挥发性有机化合物的综合综述。

A comprehensive review of post-harvest agricultural product deterioration signature volatile organic compounds.

作者信息

Sun Lu, Ma Junning, Purcaro Giorgia, Wang Gang, Jin Jing, Xing Fuguo

机构信息

Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences /Key Laboratory of Agro-Products Quality and Safety Control in Storage and Transport Process, Ministry of Agriculture and Rural Affairs, Beijing, 100193, PR China.

Gembloux Agro-Bio Tech, University of Liège, Passage des Déportés 2, 5030 Gembloux, Belgium.

出版信息

Food Chem X. 2025 Aug 4;29:102866. doi: 10.1016/j.fochx.2025.102866. eCollection 2025 Jul.

Abstract

Post-harvest management of agricultural products is crucial for minimizing spoilage and economic losses. Volatile organic compounds (VOCs) have emerged as effective indicators of early-stage deterioration, offering a promising approach to improving detection methods. This review examines the role of VOCs in spoilage identification, emphasizing key markers such as terpenes, ketones, esters, and aldehydes in fruits, vegetables, grains, and legumes. Various detection techniques-including spectrometry, electronic noses, spectroscopy, and sensor arrays-are evaluated and compared for their potential to assess spoilage and freshness by correlating their limits of detection (LOD) with typical VOC concentrations in agricultural scenarios. Future development trend in VOC research focus on enhancing sensor sensitivity, developing portable detection devices, integrating VOC monitoring with smart systems, and leveraging artificial intelligence for predictive analysis. These advancements aim to optimize post-harvest management strategies and improve food safety through more accurate and timely spoilage detection.

摘要

农产品的收获后管理对于最大限度地减少变质和经济损失至关重要。挥发性有机化合物(VOCs)已成为早期变质的有效指标,为改进检测方法提供了一种很有前景的途径。本综述探讨了VOCs在变质识别中的作用,重点介绍了水果、蔬菜、谷物和豆类中的关键标志物,如萜烯、酮、酯和醛。对各种检测技术,包括光谱法、电子鼻、光谱学和传感器阵列进行了评估和比较,通过将其检测限(LOD)与农业场景中的典型VOC浓度相关联,来评估它们评估变质和新鲜度的潜力。VOC研究的未来发展趋势集中在提高传感器灵敏度、开发便携式检测设备、将VOC监测与智能系统集成,以及利用人工智能进行预测分析。这些进展旨在通过更准确、及时的变质检测来优化收获后管理策略并提高食品安全。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8a8/12345322/61e089ea3dc7/gr1.jpg

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