Search Results (5)

Kiwifruit consumption has increased due to its rich nutritional properties. Although ‘Hayward’ continues to be the main cultivar, others, such as yellow fleshed ‘Jintao’, are of increasing interest. The objective of this research was to evaluate the acceptability and storage performance of these two cultivars. Sensory evaluation of green ‘Hayward’ and yellow ‘Jintao’ kiwifruit were performed along cold storage for three seasons/years to follow the organoleptic characteristics through ripening, as well as the acquisition of their spectra by Vis-NIR. For ‘Jintao’ were performed two sensory evaluations per year at 2.5- and 4.5-months’ storage and for ‘Hayward’ at 2.5-, 4.5- and 5.5-months’ storage. The nonparametric Mann–Whitney test and Kruskal–Wallis ANOVA were performed to test the significant differences between the mean ranks among the storage time. A non-metric multidimensional scaling plot method using the ALSCAL algorithm in a seven-point Likert scale was applied to determine the relationships in the data, and a new approach using the receiver operating characteristic (ROC) analysis was tested. The last revealed that, for both cultivars, sweetness, acidity and texture were the variables with better scores for General flavor. Aroma was also important on ‘Jintao’. A strong correlation between soluble solids content (SSC) and reflectance was found for both cultivars, with the 635–780 nm range being the most important. Regarding firmness, a good correlation with reflectance spectra was observed, particularly in ‘Hayward’ kiwifruit. Based on these results, Vis-NIR can be an objective alternative to explore for determination of the optimum eating-ripe stage. Full article

Rapid ripeness assessment of fruit after harvest is important to reduce post-harvest losses by sorting fruit according to the duration until they become ready to eat. However, there has been little research on non-destructive estimation of the ripeness and ripening speed of avocado fruit. Unlike previous methods, which classify the ripeness of fruit into a few categories (e.g., unripe and ripe) or indirectly estimate ripeness from its firmness, we developed a method using hyperspectral imaging coupled with deep learning regression to directly estimate the duration until ripeness of Hass avocado fruit. A set of 44,096 sub-images of 551 Hass avocado fruit images was used to train, validate, and test a convolutional neural network (CNN) to predict the number of days until ripeness. Training, validation, and test samples were generated as sub-images of Hass fruit images and were used to train a spectral–spatial residual network to estimate the duration to ripeness. We achieved predictions of duration to ripeness with an average error of 1.17 days per fruit on the test set. A series of experiments demonstrated that our deep learning regression approach outperformed classification approaches that rely on dimensionality reduction techniques such as principal component analysis. Our results show the potential for combining hyperspectral imaging with deep learning to estimate the ripeness stage of fruit, which could help to fine-tune avocado fruit sorting and processing. Full article

The development of yellow color is an important aspect of fruit quality in yellow fleshed kiwifruit during fruit ripening, and it has a large influence on consumer preference. The yellow color is determined by carotenoid accumulation and chlorophyll degradation and is likely affected by ethylene production. This study investigates the expression of carotenoid, chlorophyll degradation, and ethylene response factors in ‘Qihong’ fruit, which had reached the near ripening stage (firmness ≈ 20 N) and were either left untreated (controls) or treated with 0.5 μL L⁻¹ of 1-MCP for 12 h. Both the accumulation of β-carotene (not lutein) and degradation of chlorophyll a and b increased in response to the 1-MCP treatment, resulting in more yellow colored flesh in the 1-MCP treated fruit with higher carotenoid and lower chlorophyll contents. 1-MCP up-regulated AcLCY-β, AcSGR1, and AcPAO2, but reduced the expression of AcCCD1. These four genes were correlated with the concentrations of β-carotene and the chlorophylls. The expression of three ethylene response factors, including Acc29730, Acc25620, and Acc23763 were delayed and down-regulated in 1-MCP treated fruit, showing the highest correlation with the expression of AcLCY-β, AcSGR1, AcPAO2, and AcCCD1. Dual-Luciferase assays showed that 1-MCP treatment not only eliminated the inhibition of Acc23763 on the promoters of both AcPAO2 and AcLCY-β, but also reduced the activation of Acc29730 and Acc25620 on the AcCCD1 promoter. Our findings indicate that Acc29730, Acc25620, and Acc23763 may play an important role in the response to 1-MCP treatment during the fruit eating ripe stage, which likely altered the promoter activities of carotenoid and chlorophyll-related genes (AcPAO2, AcLCY-β and AcCCD1) to regulate their transcripts, resulting in more yellow color in the fruit flesh of ‘Qihong’. Full article

Global demand for avocados has risen rapidly in recent years, yet supplying fruit that consistently meets consumer expectations for quality remains a challenge in the industry. Body rots in avocado fruit are a leading cause of consumer dissatisfaction. Anecdotal evidence suggests that body rot development may be promoted by mechanical injury at harvest and packing, despite the fruit being hard, green and mature (i.e., unripe) at these stages. Here, ‘Hass’ avocado fruit, harvested across multiple fruiting seasons from commercial orchards, were subjected to controlled impact from drop heights of 15–60 cm at the time of harvest or packing. With increasing drop height, body rot development at eating ripe stage generally occurred more frequently and produced larger lesions at the impact site and, in some experiments, elsewhere on the fruit. These findings refute a general belief that green mature avocado fruit can tolerate a degree of rough physical handling without ripe fruit quality being compromised. Ideally, best avocado harvesting and packing practice should recognize that unripe fruit must not experience drop heights of 30 cm or higher. Full article

Postharvest kiwifruit continues to ripen for a period until it reaches the optimal “eating ripe” stage. Without damaging the fruit, it is very difficult to identify the ripeness of postharvest kiwifruit by conventional means. In this study, an electronic nose (E-nose) with 10 metal oxide semiconductor (MOS) gas sensors was used to predict the ripeness of postharvest kiwifruit. Three different feature extraction methods (the max/min values, the difference values and the 70th s values) were employed to discriminate kiwifruit at different ripening times by linear discriminant analysis (LDA), and results showed that the 70th s values method had the best performance in discriminating kiwifruit at different ripening stages, obtaining a 100% original accuracy rate and a 99.4% cross-validation accuracy rate. Partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) were employed to build prediction models for overall ripeness, soluble solids content (SSC) and firmness. The regression results showed that the RF algorithm had the best performance in predicting the ripeness indexes of postharvest kiwifruit compared with PLSR and SVM, which illustrated that the E-nose data had high correlations with overall ripeness (training: R² = 0.9928; testing: R² = 0.9928), SSC (training: R² = 0.9749; testing: R² = 0.9143) and firmness (training: R² = 0.9814; testing: R² = 0.9290). This study demonstrated that E-nose could be a comprehensive approach to predict the ripeness of postharvest kiwifruit through aroma volatiles. Full article