Oxalic acid (OA), as a natural antioxidant, delays the ripening and senescence of fruits. In this research, the effect of different concentration of OA (0, 2, 4, 6, and 8 mM) investigated on storage life and postharvest quality of loquat fruit at 5°C and 90±5% relative humidity for 28 days. Traits such as browning index, biochemical and qualitative characteristics and activity of polyphenol oxidase (PPO) and peroxidase (POD) enzymes of fruit at harvest (0), 7, 14, 21 and 28 days after storage were analyzed. With increasing storage time, the level of browning index, total soluble solids (TSS), ratio of TSS/titratable acidity (TA) and the activity of PPO and POD enzymes increased in all treatments, while firmness, ascorbic acid, total phenol and flavonoids content and antioxidant activity decreased (p˂0.05). OA application significantly reduced the level of browning index and the activity of enzymes responsible for tissue browning (PPO and POD) compared to control (p˂0.05). At the end of storage, fruits treated with different concentration of OA had a more firmness tissue and higher level of ascorbic acid, total phenol and flavonoids content and antioxidant activity in comparison to the control fruits (p˂0.05). Overall, OA application effectively reduced fruit softening and browning index with diminished the activity of PPO and POD enzymes during cold storage. Thus, OA treatment (6 mM) can be used as an effective method for cold storage of loquat fruit.

Nowadays, citrus fruits, especially oranges, is very important in the human nutrition regime, and its quality characteristics assessment is very important. This study aimed to predict some biochemical characteristics of blood orange, using machine vision and artificial neural networks. In this experiment, the amount of vitamin C content, sugar content, and acidity (pH) were obtained using destructive laboratory methods. Images of blood orange samples were captured and 108 texture features and 57 color features were extracted on CIElab, RGB, HSV, and HIS color spaces and finally, the artificial neural networks method has been used to estimate the desired properties. To evaluate and select the most optimal artificial neural network, a feed-forward neural networks with Levenberg-Marquardt learning algorithm, the different number of neurons, and different transfer functions in the hidden and output layers was used. Finally, using the best neural network and 165 textural-color features, the amount of vitamin C content, sugar content, and pH were estimated with a correlation coefficient of 0.950, 0.968, and 0.884, respectively. Therefore, considering the appropriate correlation coefficient, machine vision and image processing technology can estimate some biochemical characteristics of blood oranges accurately.