Search Results (84)

Background/Objective: Previous research has measured the impact of interventions on food purchases and food waste separately. Moreover, food waste studies have rarely included food insecurity measurements, which could help develop more comprehensive interventions. The aim of this article is to evaluate the effect of educational videos on food and fruit and vegetable purchases, waste and food insecurity in low-income households. Methods: This study uses an experimental design involving low-income households in Chile to evaluate the effects of three educational videos: videos of (T0) recipes using regular fruits, (T1) refrigerator cleaning instructions, and (T2) recipes using overripe fruits. Results: The videos featuring fruit-based recipes (T0 and T2) increased fruit purchases and reduced fruit waste. In contrast, vegetable purchases and waste increased, especially under the recipe-based interventions. All interventions led to a decrease in food insecurity. Conclusions: An intervention that leads to a reduction in fruit waste can also hide an increase on vegetable waste, as well as changes on purchases and a decrease of prevalence of food insecurity. These findings highlight the importance of measuring fruit and vegetable purchases and food insecurity as complementary outcomes in food waste studies. Full article

This study demonstrates that the ripeness of avocado fruits can be analyzed using frequency-dependent electrical conductivity and permittivity through a non-invasive Magnetic Induction Spectroscopy (MIS) method. Utilizing an MIS system for conductivity and permittivity measurements of a large sample set ($N=60$) of avocado fruits across multiple frequencies from 100 kHz to 3 MHz enables clear observation of their dispersion behavior and the evolution of their spectra over ripening time in a completely non-contact manner. For the entire sample batch, the conductivity spectrum exhibits a general upward shift and spectral flattening over ripening time. To further quantify these features, normalized gradient analysis and equivalent circuit modeling were employed, and statistical analysis confirmed the correlations between electrical parameters and ripening stages. The trend characteristics of the normalized gradient parameter $P_y$ provide a basis for defining the three ripening stages within the 22-day period: early pre-ripe stage (0–5 days), ripe stage (5–15 days), and overripe stage (after 15 days). The equivalent circuit model, which is both physically interpretable and fitted to experimental data, revealed that the ripening process of avocado fruits is characterized by a weakening of capacitive structures and an increase in extracellular solution conductivity, suggesting changes in cellular integrity and extracellular composition, respectively. The results also highlight significant inter-sample variability, which is inherent to biological samples. To further investigate individual conductivity variation trends, Gaussian Mixture Model (GMM) clustering and Principal Component Analysis (PCA) was conducted for exploratory sample classification and visualization. Through this approach, the sample set was classified into three categories, each corresponding to distinct conductivity variation patterns. 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

This study investigated the impact of slow cooling on browning and fruit quality at three maturity stages (early, mid and late). Slow cooling reduced core browning in early/mid-harvest pears, as the browning indexes of early-, middle- and late-harvested ‘Yali’ pears at 60 d were 0.13, 0 and 0.1, respectively, preserving firmness and soluble solids. Transcriptomic analysis revealed that upregulated genes in ‘Yali’ pears facilitated stress adaptation via enhanced catalytic activity and phosphorylation. Mid-harvested pears exhibited activation of phosphorus metabolism and DNA repair mechanisms to maintain cellular homeostasis, whereas the late-harvested counterparts showed significant suppression of photosynthesis-related pathways and pyrimidine metabolism, which collectively accelerated senescence progression. Universal downregulation of hormone-response pathways such as ethylene and auxin revealed systemic stress adaptation decline. Then, the PbRAV transcription factors’ role was also studied. EMSA confirmed that GST-PbRAV2 binds to the PbLAC15 promoter, linking RAV2 to laccase regulation. Overripe pears showed PbRAV2 dysregulation, impairing LAC15 suppression and accelerating browning. Findings provide a theoretical basis for using slow cooling to mitigate browning in pear storage. Full article

The main goal of this study was to investigate and better understand the evolution of the main non-destructive and destructive quality parameters of peach ‘Redhaven’ during ripening process. This study was conducted from 8 to 21 July 2023, during which peaches ‘Redhaven’ were harvested each second day from a commercial orchard located in Novaki Bistranjski. Maturity categories were defined according to different firmness thresholds: maturity for long-distance chain stores (H1), maturity for medium-distance chain stores (H2), maturity below the defined maximum firmness in order to preserve optimal quality traits (H3), ready to buy (H4), ready to eat (H5), and overripe (H6). The chlorophyll absorbance index was the non-destructive parameter that was mostly distinguished between maturity categories (r = 0.78 with firmness), followed by a* and h° ground colour parameters. During the first three maturity categories (H1–H3), firmness had a notably smaller correlation with titratable acidity and the ratio of total soluble solids and titratable acidity, which is not the case for a* and h° ground colour parameters, chlorophyll absorbance index, and the share of additional colour. During the last three maturity categories (H4–H6), non-destructive parameters are not reliable for maturity prediction. When ground colour parameters are measured near petiole insertion, mostly smaller segregation between maturity categories is obtained compared to when measured at the rest of the fruit. Total polyphenol and flavonoid content in peach juice notably corelated only in the last two maturity categories with L* ground colour parameter. Full article

This study investigated the impact of cultivar, harvest time, and ripening stage of strawberries on their aroma concentration and profile, and colour stability of nectars produced from these strawberries. Purees from 12 different cultivars from two countries, collected at different ripening stages and harvest times, were analysed. Furaneol and mesifuran content was analysed using a gas chromatography–flame ionisation detector (GC-FID), and gas chromatography–mass spectrometry (GC-MS) was used to determine the content of 12 aroma compounds, including esters, C6 compounds, and lactones. Nectars produced from these purees had their colour stability measured over 12 weeks. Both the colour and aroma were greatly influenced by strawberry cultivar. Within cultivars, nectars produced from strawberries that had been harvested overripe showed higher colour stability and higher concentrations of aroma compounds than those harvested ripe from an earlier harvest, although some cultivars were more affected by harvest time than ripening stage. Aroma compounds that correlated significantly (p < 0.05) with a good colour after storage included furaneol, ethyl butanoate, hexanal, γ-decalactone and γ-dodecalactone, as well as the total concentration of aroma compounds. Only γ-decalactone concentrations correlated significantly with overall nectar colour stability, although this could be due to cultivar effects. Full article

Accurate quality detection of fruits and vegetables is crucial for optimizing harvest timing, minimizing post-harvest losses, and reducing waste. This research aims to integrate remote-sensing and deep learning (DL) technologies to develop and evaluate object detection models employing a novel DL approach for precision agriculture through automated quality detection in fruits and vegetables. To achieve this, twelve state-of-the-art object detection models from the MMDetection framework were trained by utilizing a custom-created and annotated dataset that comprises 1535 images and 39 classes of fruits and vegetables categorized into unripe, ripe, and overripe qualities. To evaluate the performance of each model, metrics like loss, mean Average Precision (mAP), receiver operating characteristic (ROC) curve, area under the curve (AUC), and confusion matrix were employed. The results determined that the Detection Transformer with Improved Denoising Anchor Boxes (DINO) and Dynamic Denoising Query (DDQ) models outperformed the others, achieving a mAP of 0.65 and a loss of 1.8 and 1.9, respectively. These metrics demonstrate their ability to distinguish the quality of fruits and vegetables accurately. These findings highlight the potential of DL models for real-world agricultural applications, as they facilitate timely quality assessment and contribute to the development of intelligent solutions. Full article

The bagging technique is a traditional preharvest practice used in Vinalopó Bagged Table Grape production to improve fruit quality and protect clusters from environmental stress. However, its influence on grape volatile composition remains underexplored. This study analyzed the volatile profile of three grape varieties (‘Dominga’, ‘Aledo’, and ‘Doña María’) by comparing bagged and non-bagged clusters to assess the effect of bagging on aromatic compounds. Volatiles were extracted using headspace solid-phase microextraction (HS-SPME) and analyzed by gas chromatography–mass spectrometry (GC–MS). A total of 35 volatile compounds were identified and quantified, mainly aldehydes, terpenes, and alcohols. The highest concentration was found in non-bagged ‘Dominga’ grapes (57.17 mg kg⁻¹), and the lowest in bagged ‘Doña María’ grapes (16.36 mg kg⁻¹). Although total volatile content did not differ significantly between treatments, differences were observed in the relative abundance of chemical families. Bagged grapes showed higher proportions of aldehydes, such as hexanal and (E)-2-hexenal, contributing to green, fresh aromas, while non-bagged grapes exhibited more alcohols and esters, linked to fruity and overripe notes. This study offers new insights into the role of preharvest bagging in shaping grape volatile composition, contributing to a better understanding of its impact on fruit aroma and quality. Full article

Drosophila suzukii severely damages the production of berry and stone fruits in large parts of the world. Unlike D. melanogaster, which reproduces on overripe and fermenting fruits on the ground, D. suzukii prefers to lay its eggs in ripening fruits still on the plants. Flies locate fruit hosts by their odorant volatiles, which are detected and encoded by a highly specialised olfactory system before being translated into behaviour. The exact information-processing pathway is not yet fully understood, especially the evaluation of odour attractiveness. It is also unclear what differentiates the brains of D. suzukii and D. melanogaster to cause the crucial difference in host selection. We hypothesised that the basis for different behaviours is already formed at the level of the antennal lobe of D. suzukii and D. melanogaster by different neuronal responses to volatiles associated with ripe and fermenting fruit. We thus investigated by 3D in vivo two-photon calcium imaging how both species encoded odours from ripe fruits, leaves, fermented fruits, bacteria, and their mixtures in the antennal lobe. We then assessed their behavioural responses to mixtures of ripe and fermenting odours. The neural responses reflect species-dependent shifts in the odour code. In addition to this, morphological differences were also observed. However, this was not directly reflected in different behavioural responses to the odours tested. Full article

Determining the maturity of cocoa pods early is not just about guaranteeing harvest quality and optimizing yield. It is also about efficient resource management. Rapid identification of the stage of maturity helps avoid losses linked to a premature or late harvest, improving productivity. Early determination of cocoa pod maturity ensures both the quality and quantity of the harvest, as immature or overripe pods cannot produce premium cocoa beans. Our innovative research harnesses artificial intelligence and computer vision technologies to revolutionize the cocoa industry, offering precise and advanced tools for accurately assessing cocoa pod maturity. Providing an objective and rapid assessment enables farmers to make informed decisions about the optimal time to harvest, helping to maximize the yield of their plantations. Furthermore, by automating this process, these technologies reduce the margins for human error and improve the management of agricultural resources. With this in mind, our study proposes to exploit a computer vision method based on the GLCM (gray level co-occurrence matrix) algorithm to extract the characteristics of images in the RGB (red, green, blue) and LAB (luminance, axis between red and green, axis between yellow and blue) color spaces. This approach allows for in-depth image analysis, which is essential for capturing the nuances of cocoa pod maturity. Next, we apply classification algorithms to identify the best performers. These algorithms are then combined via stacking and voting techniques, allowing our model to be optimized by taking advantage of the strengths of each method, thus guaranteeing more robust and precise results. The results demonstrated that the combination of algorithms produced superior performance, especially in the LAB color space, where voting scored 98.49% and stacking 98.71%. In comparison, in the RGB color space, voting scored 96.59% and stacking 97.06%. These results surpass those generally reported in the literature, showing the increased effectiveness of combined approaches in improving the accuracy of classification models. This highlights the importance of exploring ensemble techniques to maximize performance in complex contexts such as cocoa pod maturity classification. Full article

Bitter Hawkesbury watermelon (BHW) Citrullus lanatus (Thunb.) Matsum. and Nakai (syn. Citrullus vulgaris Schad) contain high concentrations of cucurbitacin-E-glycoside (CEG), a compound that acts as an arrestant and feeding stimulant for diabroticine leaf beetles that are corn (maize) and cucurbit pests. Juice from BHW is used as feedstock to produce an insecticide synergist for improved chemical control of pests in cucurbit cropping systems. A positive linear relationship was observed between the CEG concentration of parent and offspring grown in open-pollinated field plots. However, subsequent experiments that explored the influence of parent and fruit maturity on CEG concentration did not confirm a relationship between accumulation patterns among offspring of half-sibling families. An effect of maturity was observed in that earlier harvested fruit had greater CEG concentrations than ripe or overripe fruit. In a field study, CIDETRAK L (active ingredient is BHW juice) was mixed with commonly used insecticides to enhance behavioral control of striped cucumber beetle Acalymma vittatum (F.) and squash vine borer Melittia cucurbitae (Harris). Equivalent control of A. vittatum and M. cucurbitae was observed on zucchini when treated with foliar applications of spinosad, acetamiprid, or lambda-cyhalothrin versus ground applications of the same products mixed with CIDETRAK L. Full article

In the last decade, significant efforts have been made to predict sensory characteristics using electronic senses, such as the electronic nose (e-nose) and the electronic tongue (e-tongue), and discuss their relationship to the eating quality evaluated by human panels. This study was conducted (1) to characterize the aroma and taste profiles of strawberries over a 5-day storage period (4 °C) using both electronic senses and human panels and (2) to correlate the electronic sense data with human panel data. A total of 10 sensory attributes of strawberries, including 7 aroma and 3 taste attributes, were analyzed by a descriptive sensory panel (n = 16) over the five days. Although the human panel did not find significant differences in the intensities of the strawberry attributes over the five days, the intensity ratings showed an increasing or decreasing trend over the storage period. However, the e-nose and the e-tongue discriminated each of the storage days of the strawberry samples. Furthermore, the partial least square regression coefficients of determination (R²) indicated that the e-nose and the e-tongue were highly predictive in their evaluation of the intensities of all the descriptive sensory attributes. Lastly, the concentrations of furaneol, one of the key volatiles imparting a distinct ripe strawberry aroma, were determined using an e-nose to correlate with the intensities of aroma attributes evaluated by the panel. A significant positive Pearson’s correlation coefficient was found with the intensities of overripe aroma. The findings indicate the potential of electronic senses to determine sensory characteristics and their excellent capability to predict the eating quality of strawberries. Full article

Thailand is a prominent global producer of mangoes, providing a wide range of mango cultivars and dealing with the challenge of managing biomass. Thus, biorefining mango peel to extract valuable components has the potential to reduce organic waste and create a new revenue source for the mango processing sector. This study aims to examine the physiology, physiochemical, and chemical characteristics in peel of nine Thai mango cultivars, along with the relationship between their characteristics. The Thai mango cultivars Mahachanok, Chok anan, and Rad exhibited a yellow appearance, while the other six cultivars appeared yellow-green. However, the firmness of the fruit was directly correlated with the firmness of the pulp. A proximate composition study revealed that the predominant constituent of mango peel was carbohydrates, comprising up to 75% of its composition. This was followed by fibre, which accounted for up to 13%. The Nga mango had the highest levels of total phenolic content (220 mgGAE/g) and total flavonoid content (5.5 mgCE/g). The primary phenolic acids identified in Thai mango peel were epicatechin, caffeic acid, catechin, and gallic acid. The Mahachanok cultivar exhibited the highest antioxidant activity, as determined by the ABTS and DPPH assays, with values of 85.67% and 85.78%, respectively. This study demonstrated the connections between the physiochemical characteristics of mangoes and their chemical compositions in different cultivars, indicating the possibility of choosing particular cultivars for extracting targeted bioactive compounds. The multivariate analyses revealed that there was no correlation between the physiochemical and chemical profiles of mangoes. This study highlights the significance of mango peel as a valuable by-product that has significant environmental and economic ramifications for the mango processing industry. Full article

The promotion of food from underutilized plants can help combat biodiversity loss, foster cultural preservation, and empower farmers in the face of market pressures and sustainable production conditions. The nutritional and aromatic characterization of two undervalued types of Sorbus domestica fruits, differentiated by their apple and pear shapes, has been carried out. Official Association of Analytical Communities methods have been used for proximate composition and mineral analysis determinations, and gas chromatography was used for the analysis of volatile components in three states of ripeness and compared with the aromas of fresh apple and quince jam. S. domestica fruits are a good source of K, Ca, Fe, and fiber and are an important source of antioxidants in the human diet. S. domestica fruits have proven to be very distinctive in the aromatic fraction. 1-hexanol, hexyl 1,3-octanediol, phenylacetaldehyde, nonanal, hexanal, and α-farnesene are the most potent odor compounds in the overripening stage of the fruits. The aroma profiles of immature S. domestica fruits were dominated by aldehydes, while in the overripe stage, the fruit accumulated abundant esters, alcohols, and sesquiterpenoids. S. domestica fruits could be introduced as an alternative to seasonal fruit consumption and could generate sustainable production and consumption alternatives while recovering cultural and food heritage. Full article

Discarded unripe kiwifruits (DUKs) are regarded as the major agro-byproducts in the production of kiwifruits, which have abundantly valuable secondary metabolites. Nevertheless, owing to the limited knowledge about the differences in phytochemicals and bioactivity between DUKs and mature kiwifruits, the utilization of DUKs in the food industry remains scarce. Hence, to promote their food applications, the phenolic compounds and bioactivity of discarded unripe, mature, and overripe fruits from three red-fleshed kiwifruit cultivars were studied and compared. The results revealed that the levels of total phenolics, total flavonoids, and total procyanidins in kiwifruits varied significantly by maturity stage. In addition, our findings demonstrated that DUKs possessed much higher contents of valuable phenolic compounds (e.g., chlorogenic acid (CHA), neochlorogenic acid (NCHA), gallic acid (GA), protocatechuic acid (PA), procyanidin B1 (ProcB1), procyanidin B2 (ProcB2), procyanidin C1 (ProcC1), quercetin 3-O-glucoside (QueG), and quercetin 3-O-rhamnoside (QueR)) than mature and overripe kiwifruits. Furthermore, DUKs exerted much stronger in vitro antioxidant capacity, inhibitory effects on α-glucosidase, and anti-inflammatory activity than mature and overripe kiwifruits, which were mainly attributed to their higher contents of total polyphenols and individual phenolic components, such as GA, CHA, NCHA, PA, ProcB1, ProcB2, ProcC1, and QueR. Overall, these findings provide sufficient evidence for the development and utilization of DUKs in the food/functional food industry. Full article