The growing demand for sustainable food packaging solutions has increased research into antimicrobial active packaging systems based on biodegradable biopolymers and natural extracts.
Grain stored in jute bags is highly susceptible to loss, prompting the reinforcement of jute with polypropylene (PP) to improve storage characteristics. Jute fiber was reinforced with polypropylene in nine ratios (0-50 %) and evaluated for yarn strength and quality. Reinforced yarns were then woven into 50 kg storage bags for performance comparison when storing pulses (Bengal gram and green gram). Yarn count decreased by 7.1 %, while strength increased by 24.4 % with 50 % polypropylene. Fabric density and moisture regain decreased by 34 % and 59 %, respectively, enhancing moisture resistance and durability. Water vapor transmission rate was reduced by 39 %. The optimal blend of 58.5 % jute and 41.5 % polypropylene was found to be the most effective. The reinforced bags were also reported effectively biodegradable and having good stacking characteristics like jute bags as well as being biodegradable. After six months of storage, the dry weight loss in Bengal gram and green gram was 26 % and 67 %, respectively, in jute bags, compared to under 5 % in reinforced bags. Grain damage and insect infestation were also significantly lower, with 82 % and 87 % grain damage in jute bags, reduced to 29 % and 22 % in reinforced bags for both crops. Moisture and protein content in stored grains were also better preserved. Economic analysis indicated that reinforced bags saved Indian Rupee $7.32-$13.04 per bag, while traditional jute bags incurred losses of $25.62-$28.53 per bag, proving reinforced jute bags to be an efficient, sustainable and cost-effective alternative for grain storage.
Apples are highly perishable and face serious postharvest challenges such as moisture loss, microbial spoilage, oxidative degradation, physiological disorders, and ethylene-induced ripening, all of which accelerate quality deterioration and economic losses. Conventional preservation methods often fall short, highlighting the need for sustainable, consumer-friendly alternatives. This review critically examines edible coatings as a strategy to mitigate postharvest losses in apples, highlighting bio-based materials (polysaccharides, proteins, and lipids), functional enhancements with antioxidants, antimicrobials, and nanomaterials, and key application techniques such as dipping, spraying, and brushing. The discussion is organized around coating mechanisms, performance factors (adhesion, thickness, uniformity), and evaluation parameters including weight loss, firmness, colour stability, microbial safety, and sensory attributes. Edible coatings represent a promising strategy for shelf-life extension and quality preservation, with added potential for intelligent packaging applications, although limitations in consumer perception, regulatory frameworks, cost-effectiveness, and scalability persist. Future research should priorities novel multifunctional formulations, improved application technologies, and integration with other preservation methods to enable successful commercialization.
Chickpea flour is used in a variety of culinary preparations to manufacture foods with high protein content. Chickpea flour’s protein content is commonly measured using the Dumas method, but this process is time-consuming, expensive, and labor-intensive. This study employed near-infrared (NIR) hyperspectral imaging to predict the protein content of chickpea flour. Eight different chickpea varieties with different protein contents were processed into chickpea flour. Chickpea flour samples were subjected to NIR reflectance hyperspectral imaging in the 900–2500 nm spectral region. Using the Dumas combustion method, the protein content of twenty-four samples of chickpea flour (8 var. × 3 replications) was determined. The spectral data of the chickpea flour samples and the observed reference protein content (dependent variables) were correlated. Out of a total of 24 samples, 16 powder samples were used to build the calibration model, and 8 powder samples were used to build the prediction model. While using the full spectrum, the optimum protein prediction model was obtained using Partial least square regression (PLSR) and orthogonal signal correction (OSC) + standard normal variate (SNV) preprocessing, which resulted in correlation coefficient of prediction (R2p) and root mean square error of prediction (RMSEP) values of 0.934 and 1.006, respectively. Additionally, 11 feature wavelengths were chosen from the investigated spectrum using competitive adaptive reweighted sampling (CARS), which also yielded the best PLSR model with R2p and RMSEP of 0.944 and 0.889, respectively. By combining PLSR, OSC + SNV, and CARS chosen wavelength, the optimum model for protein content prediction in chickpea flour was established.
Climate change presents substantial challenges to the integrity and functional attributes of fruits, with profound implications for global food security and economic sustainability. Fluctuations in key climatic factors such as temperature and humidity, erratic weather patterns, and imbalances in gas levels markedly influence fruit production and quality. These conditions further exacerbate critical issues related to ripening, texture, weight loss, and disease susceptibility. The present review explores technological advancements, particularly innovative post-harvest handling techniques such as advanced packaging and storage methods, energy-efficient cold chains, advanced coating methods, and nanotechnology-based interventions as pivotal climate-smart solutions for maintaining fruit integrity and extending shelf life. Furthermore, predictive modelling tools facilitate proactive management and informed decision-making in post-harvest handling by forecasting fruit quality deterioration, shelf life, and storage conditions. Collectively, these insights highlight the pressing need for ongoing research and innovation in practices to bolster food system resilience, minimize losses, and uphold sustainability amid a changing climate.
