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Comparative Analysis of Chilling Injury in Banana Fruit During Storage: Physicochemical and Microstructural Changes, and Early Optical-Based Nondestructive Identification

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Publicat a:Foods vol. 14, no. 8 (2025), p. 1319
Autor principal: Ma, Hui
Altres autors: Hu Lingmeng, Zhao, Jingyuan, He, Jie, Wen Anqi, Lv Daizhu, Xu, Zhi, Lan Weijie, Pan Leiqing
Publicat:
MDPI AG
Matèries:
Shanghai China
China
Japan
Physiology
Microstructural analysis
Comparative analysis
Nondestructive testing
Spectral signatures
Metabolism
Moisture content
Machine learning
Fruits
Bananas
Food quality
Water content
Destabilization
Spectrum analysis
Clustering
Temperature
Marketability
Optics
Optical properties
Metabolites
Supply chains
Injury analysis
Hyperspectral imaging
Accés en línia:Citation/Abstract
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Resum:Chilling injury (CI) during postharvest storage seriously impairs bananas’ quality and marketability. This study systematically investigated CI mechanisms through physicochemical, microstructural, and optical analyses and innovatively developed a hyperspectral imaging (HSI)-based approach for early CI detection. Bananas stored at suboptimal (7 °C) and optimal (13 °C) conditions exhibited distinct physicochemical changes. CI progression was related to increased browning symptoms, an abnormal moisture redistribution (reduced pulp moisture content), and delayed softening. Microstructural analysis revealed membrane destabilization, cellular lysis, intercellular cavity formation, and inhibited starch hydrolysis under chilling stress. Hyperspectral microscope imaging (HMI) captured chilling-induced spectral variations (400–1000 nm), enabling the t-SNE-based clustering of CI-affected tissues. Machine learning models using first derivative (1-st)-processed spectra achieved a high accuracy. Both PLS-DA and RF had a 99% calibration accuracy and 98.5% prediction accuracy for CI classification. Notably, HSI detected spectral signatures of early CI (2 days post-chilling treatment) before visible symptoms, achieving a 100% identification accuracy with an optimized PLS-DA combined with 1-st processing. This study provides a theoretical basis for studying fruit CI mechanisms and a novel nondestructive optical method for early CI monitoring in postharvest supply chains.
ISSN:2304-8158
DOI:10.3390/foods14081319
Font:Agriculture Science Database