Abstract
Bananas (Musa spp.) are vital for food security and the economy in many developing nations, however, their high perishability due to rapid ripening leads to major postharvest losses. Traditional methods, such as cold storage and chemical treatments, have drawbacks, including potential chilling injury and consumer concerns over chemical residues. As a sustainable alternative, edible coatings have gained prominence for their ability to extend shelf-life while maintaining fruit quality. The comprehensive review of the literature established the fundamental role of edible coatings in postharvest banana preservation. Additionally, the bibliometric analysis highlighted research trends, including the prevalence of polysaccharide-based coatings and the dipping method as the most common application technique. Additionally, integrating bioactive compounds was identified as a key strategy for enhancing coating functionality.
In theme II, the objective was to develop and assess the effectiveness of Opuntia ficus-indica mucilage (OFIM) based edible coatings in regulating banana ripening under controlled retail conditions. The findings demonstrated that OFIM edible coatings modulated ethylene biosynthesis, maintained cell wall integrity, and enhanced antioxidant activity, resulting in shelf-life extension. The OFIM coating delayed softening enzyme activities such as polygalacturonase (PG), pectin methyl esterase (PME), and cellulase (CX), thereby preserving fruit firmness. Furthermore, OFIM treatments significantly reduced ethylene production, respiration rate, and chlorophyll degradation enzymes, preserving the banana colour and slowing ripening. The coating also enhanced antioxidant defence mechanisms by enhancing catalase (CAT), superoxide dismutase (SOD), and phenylalanine ammonia-lyase (PAL) activity while reducing oxidative stress markers. However, the native OFIM exhibited limited mechanical strength and insufficient antimicrobial activity, highlighting the need for formulation optimisation.
In theme III, the objective was to address the limitations of OFIM edible coating by incorporating key ingredients (glycerol and cellulose nanofibers) and functionalise the coating with pomegranate peel extract to enhance the functional properties of OFIM. The effectiveness of the optimised formulation was evaluated through phytochemical attributes, fatty acid composition, volatile profiling, and machine learning predictions of fruit firmness. This study revealed that optimal formulation (OF) improved the structural integrity and antimicrobial properties of the coating, thereby enhancing the overall quality of banana fruit. Moreover, the OF effectively suppressed
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fungal growth (Botrytis sp.) and preserved the fatty acid composition essential for membrane stability. Scanning electron microscopy (SEM) analysis confirmed uniform coverage and reduced microcracks in treated samples, which reinforced the coating’s effectiveness as a protective barrier.
In theme IV, the objective was to evaluate the impact of the optimised coating on the physicochemical properties and volatile profiles of bananas, while applying machine learning models to predict firmness using non-destructive indicators. In the study of volatile organic compounds (VOCs), 22 VOCs were identified over a 10-day storage period. This study revealed that esters were dominating at later ripening stages, while aldehydes and alcohols were more prominent in the early stages. The optimized coating delayed volatile release, prolonging freshness and preserving the characteristic banana aroma profile.
Another study predicted banana firmness using three machine learning (ML) models, namely Partial Least Squares (PLS), Ridge, and Elastic Net regression models. Since edible coatings can alter fruit firmness, which is traditionally measured using destructive methods, ML was applied to predict firmness using non-destructive quality attributes. Correlation and regression analyses confirmed a strong relationship between firmness and non-destructive parameters, supporting the use of ML for predictive modeling. PLS regression (R2= 0.978; RMSE=0.097; MSE= 0.009) showed superior predictive accuracy over Ridge (R2= 0.972; RMSE=0.110; MSE= 0.012) and Elastic Net (R2= 0.956; RMSE=0.142; MSE= 0.020) algorithms. The findings highlight the potential of integrating edible coatings with ML for non-destructive quality assessment, improving quality control, and reducing postharvest waste in the banana industry.
The findings demonstrate that OFIM-based edible coatings offer a sustainable, eco-friendly, and cost-effective approach to reducing postharvest losses in bananas. Furthermore, this study aligns with the United Nations Sustainable Development Goals (SDGs). Overall, this study advances the understanding of edible coatings, particularly OFIM, and provides an optimisation framework, broadening its applicability to other fresh produce. Future research should explore scalability, consumer acceptance, and regulatory aspects to facilitate the broader adoption of this technology in the fresh produce industry.