Search Results (329)

Lychee (Lychee chinensis Sonn.) is a tropical fruit highly appreciated for its vivid red color, sweet flavor, and nutritional properties. However, it is highly perishable, with postharvest losses often due to oxidative browning and dehydration. This study evaluated the organic olive oil coating (OC), a natural lipidic system with the potential to act as a precursor for organogel development, combined with polyamide/polyethylene (PA/PE) packaging under passive modified atmosphere. Fruits were harvested at commercial maturity and divided into two groups: OC-treated and untreated control (CTR). Both groups were stored at 5 ± 1 °C and 90 ± 5% relative humidity and analyzed on days 0, 3, 6, and 9. The OC-treated fruits showed significantly better retention of physical, chemical, microbiological, and sensory qualities. The coating reduced oxidative stress and enzymatic browning, preserving color and firmness. The PA/PE packaging regulated gas exchange, lowering oxygen levels and delaying respiration and ripening. As a result, OC fruits had lower weight loss, a slower increase in browning index and maturity index, and better visual and sensory scores than the CTR group. This dual strategy proved effective in extending shelf life while maintaining the fruit’s appearance, flavor, and nutritional value. It represents a sustainable and natural approach to enhancing the postharvest stability of lychee. Full article

Strawberries are highly perishable fruits prone to postharvest losses due to defects, diseases, and uneven ripening. This study proposes a deep learning-based approach for automated quality assessment using the YOLOv8n object detection model. A custom dataset of 5663 annotated strawberry images was compiled, covering eight quality categories, including anthracnose, gray mold, powdery mildew, uneven ripening, and physical defects. Data augmentation techniques, such as rotation and Gaussian blur, were applied to enhance model generalization and robustness. The model was trained over 100 and 200 epochs, and its performance was evaluated using standard metrics: Precision, Recall, and mean Average Precision (mAP). The 200-epoch model achieved the best results, with a mAP50 of 0.79 and an inference time of 1 ms per image, demonstrating suitability for real-time applications. Classes with distinct visual features, such as anthracnose and gray mold, were accurately classified. In contrast, visually similar categories, such as ‘Good Quality’ and ‘Unripe’ strawberries, presented classification challenges. Full article

Postharvest storage of kiwifruit requires the implementation of precise environmental conditions to maintain fruit quality and reduce decay. In this research, conducted over two years, we examined whether the storage conditions, characterized by low temperature (1 ± 1 °C) and ultra-high relative humidity (higher than 99%, close to saturation), generated by the Xedavap^® machine from Xeda International, were effective in maintaining the fruit quality and reducing postharvest rots compared to standard storage conditions, characterized by involved low temperature (1 ± 1 °C) and high relative humidity (98%). Kiwifruits preserved under the experimental conditions exhibited a significantly lower rot incidence after 60 days of storage, with the treated fruits showing 4.48% rot compared to 23.03% under the standard conditions in the first year, using inoculated fruits, and 6.30% versus 9.20% in the second year using naturally infected fruits, respectively. After shelf life (second year only), rot incidence remained significantly lower in the treated fruits (12.80%) compared to the control (42.30%). Additionally, quality analyses showed better parameters when using the Xedavap^® system over standard methods. The ripening process was effectively slowed down, as indicated by changes in the total soluble solids, firmness, and titratable acidity compared to the control. These results highlight the potential of ultra-high relative humidity conditions to reduce postharvest rot, extend the shelf life, and enhance the marketability of kiwifruit, presenting a promising and innovative solution for the horticultural industry. Full article

Effective postharvest cooling of olive fruit is increasingly critical under rising harvest temperatures driven by climate change. This study models passive cooling dynamics using a trait-based, mixed-effects statistical framework. Ten olive groups—representing seven cultivars and different ripening or size stages—were subjected to controlled cooling conditions. Surface temperature was recorded using infrared thermal imaging, and morphological and compositional traits were quantified. Temperature decay was modeled using Newton’s Law of Cooling, extended with a quadratic time term to capture nonlinear trajse thectories. A linear mixed-effects model was fitted to log-transformed, normalized temperature data, incorporating trait-by-time interactions and hierarchical random effects. The results confirmed that fruit weight, specific surface area (SSA), and specific heat capacity (SHC) are key drivers of cooling rate variability, consistent with theoretical expectations, but quantified here using a trait-based statistical model applied to olive fruit. The quadratic model consistently outperformed standard exponential models, revealing dynamic effects of traits on temperature decline. Residual variation at the group level pointed to additional unmeasured structural influences. This study demonstrates that olive fruit cooling behavior can be effectively predicted using interpretable, trait-dependent models. The findings offer a quantitative basis for optimizing postharvest cooling protocols and are particularly relevant for maintaining quality under high-temperature harvest conditions. Full article

Banana fruits are harvested and then undergo rapid ripening and senescence, sharply limiting their shelf-life and marketability. Myo-inositol (MI) is an important regulator in ethylene production and reactive oxygen species (ROS) accumulation; however, its involvement in the postharvest ripening process of banana remains to be determined. This study found that postharvest application of MI could efficiently delay the fruit ripening and extend the time in which the luster, color, and hardness were maintained in two cultivars with contrasting storage characteristics, storable ‘Brazil’ and unstorable ‘Fenza No. 1’, when stored at room temperature (23 °C ± 2 °C). Moreover, physiological, metabolic, and gene expression analyses indicated that MI application improved MI metabolism and postponed ethylene biosynthesis and cell wall loosening. The decrease in ethylene production was associated with a reduction in the expression of ACS1 and ACO1 genes. MI treatment decreased the expressions of PL1/2, PG, and EXP1/7/8, which may account for the delay in softening. In addition, the application of MI could alleviate ROS-mediated senescence and cell membrane damage by promoting the activities of SOD, POD, and anti-O₂⁻ and decreasing PPO activity. This study shed light on the function of MI in regulating the postharvest ripening and senescence of bananas and provided an efficient strategy for extending shelf-life and reduce losses. Full article

Increasing the shelf-life of fruits and vegetables using edible natural substances after harvest is economically important and can be useful for human health. Postharvest techniques help maintain the quality of edible tissues resulting in extended marketing periods and reduced food waste. The edible coating on perishable commodities is a common technique used by the food industry during the postharvest supply chain. The objective of this research was to study the effect of edible oil to minimize the loss of postharvest physio-chemical and nutritional attributes of bananas. The study selected two banana cultivars (Musa, ‘Cavendish’ and ‘Milk’) to conduct this experiment, and two edible oils (olive oil (Olea europaea) and moringa oil (Moringa peregrina)) were applied as an edible coating under two different storage conditions (15 and 25 °C). The fruit’s physio-chemical properties including weight loss, firmness, color, total soluble solids (TSS), pH, titratable acidity (TA), TSS: TA ratio, and mineral content were assessed. The experiment lasted for 12 days. The physicochemical properties of the banana coated with olive and moringa oils were more controlled than the non-coated (control) banana under both storage temperatures (15 °C and 25 °C). Coated bananas with olive and moringa oils stored at 15 °C resulted in further inhibition in the ripening process. There was a decrease in weight loss, retained color, and firmness, and the changes in chemical parameters were slower in banana fruits during storage in the olive and moringa oil-coated bananas. Minerals were highly retained in coated Cavendish bananas. Overall, the coated samples visually maintained acceptable quality until the final day of storage. Our results indicated that olive and moringa oils in this study have the potential to extend the shelf-life and improve the physico-chemical quality of banana fruits. Full article

Passiflora cincinnata (Mast), native to the Brazilian semi-arid region, produces exotic fruits even under low water availability. However, its green coloration at ripening complicates optimal harvesting, impacting post-harvest fruit quality. Therefore, this study aimed to evaluate the influence of cultivation systems (irrigated and rainfed) and different ripening stages on the physical and post-harvest characteristics of wild passion fruit during the second production cycle. The experiment was conducted using a randomized block design in a 2 × 4 factorial scheme, corresponding to two cultivation systems (irrigated and rainfed) and four fruit ripening stages (60, 80, 100, and 120 days after anthesis—DAA), with five replications. The fruit pulps were analyzed for physicochemical characterization and bioactive compounds. The physical and chemical characteristics of wild passion fruit were influenced by ripening stages and the irrigation system. The rainfed system decreased the total fruit mass by 15.50% compared to the irrigated cultivation. Additionally, the rainfed cultivation reduced the fruit color index by 14.82% and altered the respiratory pattern, causing a linear decrease of 73.37% in the respiration rate during ripening, in contrast to the behavior observed in the irrigated system, which reached an estimated minimum rate of 33.74 mg CO₂ kg⁻¹ h⁻¹ at 110 days after anthesis. Full article

Banana (Musa paradisiaca) is a climacteric fruit with high postharvest perishability, limiting its export potential. This study evaluated the effectiveness of a natural protein–polysaccharide edible coating—comprising whey, agar, cassava starch, and glycerol—on maintaining the physicochemical quality of green bananas during 28 days of refrigerated storage (13 °C, 95% RH). Seven formulations were tested, including an uncoated control. Physicochemical parameters such as weight loss, firmness, fruit dimensions, peel color, titratable acidity, pH, and soluble solids (°Brix) were systematically monitored. Significant differences were observed among treatments (ANOVA, p < 0.001). The most effective coating (T5), composed of 16.7% whey, 16.7% agar, 33.3% cassava starch, and 33.3% glycerol (based on 30 g/L solids), reduced weight loss by 58.8%, improved firmness retention by 48.4%, and limited sugar accumulation by 17.0% compared to the control. It also stabilized pH and acidity, preserved peel thickness and color parameters (L*, a*, b*), and delayed ripening. These findings confirm the coating’s capacity to form a cohesive semipermeable barrier that modulates moisture loss and respiration, making it a functional and sustainable alternative for extending banana shelf life in tropical supply chains. Full article

The accurate classification of cocoa pod ripeness is critical for optimizing harvest timing, improving post-harvest processing, and ensuring consistent quality in chocolate production. Traditional ripeness assessment methods are often subjective, labor-intensive, or destructive, highlighting the need for automated, non-invasive solutions. This study evaluates the performance of R-CNN-based deep learning models—Faster R-CNN and Mask R-CNN—for the detection and segmentation of cocoa pods across four ripening stages (0–2 months, 2–4 months, 4–6 months, and >6 months) using the RipSetCocoaCNCH12 dataset, which is publicly accessible, comprising 4116 labeled images collected under real-world field conditions, in the context of precision agriculture. Initial experiments using pretrained weights and standard configurations on a custom COCO-format dataset yielded promising baseline results. Faster R-CNN achieved a mean average precision (mAP) of 64.15%, while Mask R-CNN reached 60.81%, with the highest per-class precision in mature pods (C4) but weaker detection in early stages (C1). To improve model robustness, the dataset was subsequently augmented and balanced, followed by targeted hyperparameter optimization for both architectures. The refined models were then benchmarked against state-of-the-art YOLOv8 networks (YOLOv8x and YOLOv8l-seg). Results showed that YOLOv8x achieved the highest mAP of 86.36%, outperforming YOLOv8l-seg (83.85%), Mask R-CNN (73.20%), and Faster R-CNN (67.75%) in overall detection accuracy. However, the R-CNN models offered valuable instance-level segmentation insights, particularly in complex backgrounds. Furthermore, a qualitative evaluation using confidence heatmaps and error analysis revealed that R-CNN architectures occasionally missed small or partially occluded pods. These findings highlight the complementary strengths of region-based and real-time detectors in precision agriculture and emphasize the need for class-specific enhancements and interpretability tools in real-world deployments. Full article

A method based on Vis-NIR spectroscopy and machine learning-based modeling for non-destructive detection of the internal disorders of black flesh, spongy tissue, jelly seed, and soft nose in mango fruit was developed using the vis-NIR spectra of intact mango fruit of three cultivars sourced from three orchards in each of the two seasons, with spectra collected both at harvest and after storage. After spectra were acquired of the stored fruit, the fruit cheeks were cut longitudinally to allow visual assessment of the incidence of the internal disorders. Five models were evaluated: two tree-based algorithms (J48 and random forest), one neural network (multilayer perceptron, MLP), and two SVM training algorithms (sequential minimal optimization, SMO, and LibSVM). The models were evaluated using a tenfold cross-validation approach. Non-destructive discrimination of health from all disordered and healthy fruit from fruit with specific disorders was achieved with an accuracy ranging from 72.3 to 97.0% when using spectra collected at harvest and 63.7 to 96.2% when using spectra collected after ripening. No one machine learning algorithm out-performed other methods—for spectra collected at harvest, the highest discrimination accuracy was achieved with RF and MLP for black flesh, J48 for spongy tissue, and LibSVM for soft nose and jelly seed. For spectra collected of stored fruit, the highest discrimination accuracy was achieved with SMO for jelly seed and RF for soft nose. A recommendation is made for the consideration of ensemble models in future. The ability to predict the development of the disorder using spectra of at-harvest fruit offers the potential to guide postharvest practices and reduce incidence of internal disorders in mangoes. Full article

Persimmon (Diospyros kaki Thunb.) fruit can accumulate proanthocyanidins (tannins) during development, which causes astringency and affects consumption. The hormone abscisic acid (ABA) has been reported to play a key role in fruit ripening and softening. However, the effect of ABA on postharvest persimmon fruit deastringency remains unclear. In this study, we found that 300 mg/L ABA treatment could decrease the content of soluble tannins, thus leading removal of persimmon fruit astringency. The contents of acetaldehyde and ethanol did not increase during the storage time, indicating that ABA treatment-promoted persimmon fruit deastringency was not due to the acetaldehyde interaction with soluble tannins. Furthermore, the transcriptome analysis showed that 6713 differentially expressed genes (DEGs) were identified, and the WGCNA (weighted gene co-expression network analysis) showed that one module, which comprises 575 DEGs, significantly correlated with the contents of soluble and resoluble tannins. The analysis based on the carbohydrate metabolism pathway indicated that 37 differentially expressed structural genes involved in acetaldehyde metabolism were upregulated by ABA. Real-time quantitative PCR showed that the previously reported key genes, including structural genes and transcription factors, were all upregulated by ABA treatment. The obtained results indicate that ABA treatment, promoting persimmon fruit astringency removal, may occur through gel polymerization of cell wall materials with soluble tannins. Full article

Sarcomyxa edulis is a characteristic low-temperature, edible mushroom in Northeast China. It has a delicious taste and rich nutritional and medicinal value. S. edulis can undergo explosive fruiting, neat fruiting, and unified harvesting, making it suitable for factory production. The molecular mechanisms underlying fruiting body development in S. edulis remain poorly understood. This study employed transcriptome analysis to compare the post-ripening mycelium (NPM) and primordial fruiting bodies (PRMs) of the thermostable S. edulis strain PQ650759, which uniquely forms primordia under constant temperature. A total of 4862 differentially expressed genes (DEGs) (|log2(fold change)| ≥ 1) were identified and found to be predominantly enriched in biological processes such as cell wall organization, DNA replication, and carbohydrate metabolism. KEGG pathway analysis revealed significant enrichment in 20 metabolic pathways, including mismatch repair, yeast cell cycle, and starch/sucrose metabolism. Ten candidate genes (e.g., SKP1, MRE11, GPI) linked to cell cycle regulation, DNA repair, and energy metabolism were randomly selected and prioritized for functional analysis. Quantitative PCR validation confirmed the reliability of transcriptome data, with expression trends consistent across both methods. Our findings provide critical insights into the molecular regulation of fruiting body development in S. edulis and establish a foundation for future mechanistic studies and strain optimization in industrial cultivation. Full article

The accurate classification of banana ripeness is essential for optimising agricultural practices and enhancing food industry processes. This study investigates the classification of banana ripeness using Machine Learning (ML) and Deep Learning (DL) techniques. The dataset consisted of 1565 high-resolution images of bananas captured over a 20-day ripening period using a Canon EOS 90D camera under controlled lighting and background conditions. High-resolution images of bananas at different ripeness stages were classified into ‘unripe’, ‘ripe’, and ‘overripe’ categories. The training set consisted of 1398 images (89.33%), and the validation set consisted of 167 images (10.67%), allowing for robust model evaluation. Various ML models, including Decision Tree, Random Forest, KNN, SVM, CNN, and VGG models, were trained and evaluated for ripeness classification. Among these, DL models, particularly CNN and VGG, outperformed traditional ML algorithms, with the CNN and VGG achieving accuracy rates of 90.42% and 89.22%, respectively. These rates surpassed those of Decision Trees (71.86%), Random Forests (85.63%), KNNs (86.83%), and SVMs (89.22%). The study points out the importance of dataset quality, model selection, and preprocessing techniques in achieving accurate ripeness classification. Practical applications of these results include optimised harvesting practices, enhanced post-harvest handling, improved consumer experience, streamlined supply chain logistics, and automation in sorting systems. These results confirm the feasibility of using deep learning for the automated classification of ripening stages, with implications for reducing postharvest losses and improving supply chain logistics. These findings have significant implications for stakeholders in the banana industry, from farmers to consumers, and pave the way for the development of innovative solutions for banana ripeness classification. Full article

Understanding the effects of edible coatings on postharvest quality and shelf life of ‘Karaerik’ grapes is crucial for improving storage outcomes and reducing losses. However, limited information exists regarding the effectiveness of different coating materials on this regionally significant variety. In this study, ‘Karaerik’ grapes were treated with carboxymethyl cellulose (CMC) and locust bean gum (KB) coatings and stored under cold conditions (0 ± 0.5 °C, 90–95% relative humidity) for 0, 25, 45, and 60 days. Storage duration and coating treatments significantly affected most physical, physiological, and biochemical parameters. During storage, grape weight loss progressively increased, reaching 9.60% in the control by day 60. Coatings slightly reduced this loss, with KB showing the lowest (5.11%) compared to the control (5.69%). Respiration initially declined but surged again at day 60, especially in the control (96.4 μmol CO₂/kg·hour), while coatings helped mitigate this rise. Ethylene release remained unchanged. A slight pH decline (~4.6%) was observed in the control, while KB-treated grapes maintained higher pH and lower acidity. Soluble solids remained stable across treatments. Color changed notably during storage: a* nearly doubled (more redness), b* increased (less blue), and chroma (C*) declined by ~25%, especially in uncoated grapes. Total sugar dropped by ~43% in KB-treated grapes, with the control retaining the most. Tartaric acid decreased by ~55%, notably in KB samples. Antioxidant activity and total phenolics declined significantly (~66%) in the control. CMC coating better-preserved antioxidant capacity, while the control showed the highest phenolic levels overall. Ferulic, gallic, and chlorogenic acids increased toward the end of storage, particularly in coated grapes. In contrast, rutin and vanillic acid peaked mid-storage and were better preserved in the control. The heatmap showed significant metabolite changes in fruit samples across 0D, 25D, 45D, and 60D storage periods under CMC, CNT, and KB treatments, with distinct clustering patterns revealing treatment-specific biochemical responses. The correlation matrix revealed strong positive relationships (r > 0.70) between total sugar, glucose, and fructose levels, while ethylene showed significant negative correlations (−0.65 to −0.85) with maturity index, pH, and total soluble solids, indicating interconnected metabolic pathways during fruit ripening and storage. We conclude that edible coating selection significantly influences grape biochemical stability during cold storage, with CMC emerging as a superior choice for maintaining certain quality parameters. Full article

The objective of this study was to evaluate the feasibility of using visible, near-infrared, and short-wave infrared (VIS/NIR/SWIR) spectroscopy coupled with chemometrics for non-destructive prediction of nutritional components in Galia-type melon fruit. A total of 175 fully ripened melons were analyzed for soluble solids content (SSC), dry matter (DM), pH, and titratable acidity (TA) using partial least squares regression (PLSR), principal components regression (PCR), and multilinear regression (MLR) models. Reflectance spectra were captured at three fruit locations (pedicel, equatorial, and blossom end) in the 350–2500 nm range. The PLSR models yielded the highest accuracy, particularly for SSC (R = 0.80) and SSC/TA (R = 0.79), using equatorial zone data. Variable selection using the genetic algorithm (GA) successfully identified the spectral regions critical for each nutritional parameter at the pedicel, equatorial, and blossom end areas. Key wavelengths for SSC were found around 670–720 nm and 900–1100 nm, with important wavelengths for pH prediction located near 1450 nm, and, for dry matter, in the ranges 1900–1950 nm. Variable importance in projection (VIP) analysis confirmed that specific wavelengths between 680 and 720 nm, 900 and 1000 nm, 1400 and 1500 nm, and 1900 and 2000 nm were consistently critical in predicting the SSC, DM, and SSC/TA ratio. The highest VIP scores for SSC prediction were noted around 690 nm and 950 nm, while dry matter prediction was influenced most by wavelengths in the 1450 nm to 1950 nm range. This study demonstrates the potential of VIS/NIR/SWIR spectroscopy for rapid, non-destructive melon quality assessment, with implications for commercial postharvest management. Full article