International Journal of Science, Engineering and TechnologyAuthors: Ganjave Snehal Laxman, Professor Bangar Abhishek P., Professor Bhosale Sachin B., Professor Karad Vandana A.
Abstract: Postharvest losses of stored onion bulbs (Allium cepa L.) arise from storage rots, sprouting, rooting and respiration-driven weight loss, and are often recognised only after visible or odorous decay has spread through a bin or shed. Because infected bulbs emit altered headspace volatiles before symptoms become obvious, continuous gas sensing combined with machine learning (ML) has been proposed as a basis for early-warning monitoring. A structured, PRISMA-informed literature search was carried out through publisher platforms, PubMed Central, Crossref metadata and institutional repositories, prioritising 2022–2026 publications and retaining foundational onion studies; every bibliographic record was verified against an authoritative source, and 29 sources were retained, including 13 onion-specific and 5 related food-spoilage primary studies. The evidence shows that onion storage pathogens, notably Fusarium oxysporum (basal plate rot), Botrytis allii (neck rot), Burkholderia cepacia (sour skin) and Penicillium spp., alter the abundance of organosulfur compounds such as methyl propyl disulfide, dipropyl disulfide and 2,2-bis(methylthio)propane, but many of these compounds are also emitted by healthy bulbs, so no single gas uniquely indicates spoilage. Metal-oxide sensor arrays, ion-mobility instruments and GC-MS-guided pattern recognition have separated inoculated from healthy bulbs with best reported accuracies of roughly 85–100 %, yet almost all onion studies used small numbers of artificially inoculated bulbs in sealed laboratory containers, random rather than grouped data splits, and no long-term drift or humidity compensation. Commercial-scale validation in onion sheds has not been reported in the peer-reviewed literature. The review contributes (i) a disease-to-volatile evidence table, (ii) a comparative analysis that separates onionspecific from analogous evidence, (iii) an eight-layer, literature-justified conceptual architecture for an edgecapable early-warning system with uncertainty handling and a gated validation loop, (iv) a leakage-resistant evaluation protocol, and (v) a prioritised agenda of research gaps that must be closed before such systems can be deployed with confidence.
