Nov 2026· Food Research International· Vol 243 Pt 1, pp.
120253
· 0 citations· 134 references
Medicine
Abstract
High perishability of vegetables associated with rapid physiological deterioration and microbial spoilage results in 30-50% post-harvest losses globally. In earlier days, post-harvest diseases were detected by visual inspection, microbial culturing, and molecular assays. These destructive methods are time-consuming and unsuitable for real-time monitoring. Volatile organic compounds (VOCs) have emerged as promising non-destructive biomarkers enhanced for early detection of quality deterioration and pathogen attack, often before visible symptoms appear. This review provides a thorough overview of current knowledge on VOC emissions in postharvest vegetables with their biosynthetic origins, classification, and roles in different kinds of stress responses and host microbe interactions. VOC alterations during spoilage and disease progression are systematically evaluated, highlighting vegetable group-specific patterns and quantitative dynamics of key biomarkers emitted naturally and due to mechanical and microbial spoilage. GC-MS, GC-IMS, PTR-MS, electronic noses, and biosensors are advanced analytical techniques that are critically compared with emphasis on their integration with machine learning for classification accuracy. Despite this significant progress, variability across cultivars and storage conditions, overlap between host- and pathogen-derived volatile metabolites, and an enduring gap between laboratory findings and commercial applications are major challenges that cannot be ignored. The development of real-time monitoring systems, vegetable-specific VOC databases, and integration with smart storage infrastructure powered by the Internet of Things and artificial intelligence must be prioritized in the future.
Postharvest diseases caused by fungal and bacterial pathogens significantly reduce sucrose yield in stored sugarbeet roots in the U.S. and other sugarbeet producing regions. This study investigated volatile organic compounds (VOCs) associated with infections by sugarbeet postharvest pathogens, such as Penicillium expan...
K. Nkanpira, Malick Bill, Shyam L. Kandel et al.· PhytoFrontiers™· 0 citations
This study examines the volatile organic compounds (VOCs) as potential indicators of the presence of rice weevils, Sitophilus oryzae (Linnaeus), in IR64 rice grains. Conventional detection methods have limitations because pest infestation is detected after signs of pest damage have appeared. Meanwhile, identification o...
Background and aims Plant volatile organic compounds (VOCs) change dynamically with plant development and in response to environmental conditions. However, their potential as non-invasive indicators of phenological progression remains poorly explored. In this study, we developed a framework integrating automated VOC sa...
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Tomato quality assessment involves multiple external and internal attributes. Conventional reference methods remain important for accurate characterization but are often destructive, time-consuming, or unsuitable for repeated and online measurements. This review critically examines recent advances in nondestructive sen...
Yi-Rui Huang, Jia-Hao Gu, Ming-Yu Yang et al.· Frontiers in Plant Science· 0 citations
Aquatic products are nutritionally valuable but highly perishable, with quality loss driven by microbial spoilage, biochemical changes, including total volatile basic nitrogen (TVB-N), trimethylamine (TMA) and histamine, and physicochemical deterioration, including lipid oxidation, gas production and texture loss. Conv...
Maria Ali, Abdul Waheed Ghanghro, Bheem Raj Serani et al.· Journal of Food and Drug Ana...· 0 citations
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
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