Maryam Ebrahimi; Rouhollah Karimi; Amir Daraei Garmakhany; Narjes Aghajani; Alireza Shayganfar
Abstract
Artificial neural networks (ANN) are a nondestructive method for estimating fruit and vegetable shelf life and quality attributes. This research used artificial neural networks to model a storage process for fruit grapes (Vitis vinifera cv. Rishbaba) coated with maltodextrin, including different levels ...
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Artificial neural networks (ANN) are a nondestructive method for estimating fruit and vegetable shelf life and quality attributes. This research used artificial neural networks to model a storage process for fruit grapes (Vitis vinifera cv. Rishbaba) coated with maltodextrin, including different levels of potassium nanocarbonate (0-2%) and pyracantha extract (0-1.5%). After applying these coatings, the fruits were stored for 60 days in cold storage (-1 °C), with a relative humidity of 90%. Measurements considered weight loss percentage, titrable acidity (TA), pH, texture firmness, color index (a), and general fruit acceptance. Artificial neural networks predicted changes in fruits during the storage process. By examining different networks, the feedforward backpropagation network had 3-10-6 topologies with a coefficient of determination (R2) greater than 0.988 and a mean square error (MSE) less than 0.005. With a hyperbolic sigmoid tangent activation function, a resilient learning pattern and 1000 learning process were determined as the best neural method. On the other hand, the results of the optimized models showed that this model had the highest and lowest accuracy for predicting the weight loss percentage (R2 = 0.9975) and a (R2 = 0.5671) of the samples, respectively.
Maryam Rahimi; Narges Pakravan; Rouhollah Karimi
Abstract
Grapes are a rich source of phenolic compounds with high antioxidant, antibacterial, and nutritional properties among fruits. The aim of this study was to investigate different classes of phenolic compounds in the berry skin of eleven Vitis vinifera cultivars. The phenolic compounds were flavonols, flavanols, ...
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Grapes are a rich source of phenolic compounds with high antioxidant, antibacterial, and nutritional properties among fruits. The aim of this study was to investigate different classes of phenolic compounds in the berry skin of eleven Vitis vinifera cultivars. The phenolic compounds were flavonols, flavanols, flavone, anthocyanins, stilbenes, and phenolic acids. The highest amounts of catechin, epicatechin, ferulic acid, and chlorogenic acid were observed in ‘Yaghooti’ grape cultivar (P≤0.05). However, the amounts of catechin gallat, kaempferol, myricetin, and pcoumaric acid in ‘Bidaneh Ghermez’ berries were higher (P≤0.05) compared to other cultivars. Quercetin was the main flavonol and was highest (9.48 μg g-1; P≤0.05) in ‘Yaghooti’ berries. Luteolin content, as a flavone, ranged from 0.49 μmol g-1 in ‘Rishbaba’ berry skin to 0.88 μmol g-1 in ‘Bidaneh Ghermez’. Delphinidin-3-glucoside and malvidin-3- glucoside were highest in ‘Yaghooti’. Cyanidin-3-glucoside and peonidin -3-glucoside were highest in ‘Angoor Siah.’ Petunidin 3-glucoside was highest in ‘Bidaneh Ghermez’ (P≤0.05). Berry skin resveratrol varied from 22.7 μg g-1 in ‘Monaqa and Fakhri’ cultivars to 54.8 μg g-1 FW in ‘Bidaneh Ghermez,’ with an overall average of 36.9 μg g-1 FW. Among different cultivars, the antioxidant capacity of ‘Angoor Siah’ was highest (71.3%; P≤0.05) and ‘Monaqa’ was lowest. The ‘Angoor Siah’ cultivar had more antibacterial activity compared to other cultivars. In sum, the berry skin of ‘Yaghooti,’ ‘Angoor Siah,’ and ‘Bidaneh Ghermez’ showed the highest health-promoting bioactive compounds, potentially important for future studies.
Rouhollah Karimi; Seyed Mehdi Mirbagheri; Maryam Davtalab
Abstract
Raisins are good sources of bioactive compounds with beneficial effects on human health. Mineral nutrition is one of the main viticultural practices affecting grape and raisin phytochemical compositions. In this study, the effect of preharvest foliar application of potassium (K2SO4; 0, 1.5 and 3%) and ...
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Raisins are good sources of bioactive compounds with beneficial effects on human health. Mineral nutrition is one of the main viticultural practices affecting grape and raisin phytochemical compositions. In this study, the effect of preharvest foliar application of potassium (K2SO4; 0, 1.5 and 3%) and iron (Fe-EDDHA; 0, 0.5 and 1%) was evaluated on some nutritional and biochemical properties of raisins produced from ripped ‘Red Sultana’ grape (Vitis vinifera L.). The experiment laid on a factorial arrangement of variables using a completely randomized block design. The highest soluble sugars of fructose and glucose were related to raisin produced from the vines treated with 3% potassium in combination with 0.5% iron fertilizers. However, 3% K2SO4-treated vines in combination with 1% Fe- EDDHA showed a considerable increase in raisin sucrose and also putrescine concentration. The raisin organic acids of succinic acid, fumaric acid, citric acid, and malic acid increased significantly in treated vines with both fertilizers at final doses; however, tartaric acid showed the highest amount in 3% potassium in combination with 0.5% iron treatments. The vines treated with a high level of potassium in combination with moderate level of iron produced raisin with the highest phenolic acids of kaempferol, quercetin, chlorogenic acid and resveratrol and also showed the lowest polyphenol oxidase activity. Furthermore, raisin cinnamic acid, rutin and catechin concentration showed a peak in vines sprayed with a high level of potassium and iron and also most anthocyanidins such as petunidin-3-glucoside, peonidin-3-glucoside, cyanidin-3-glucoside and delphinidin-3-glucoside reached their highest concentration by this treatment. Likewise, the highest antioxidant capacities (measured by FRAP, DPPH and ABTS methods) were achieved in 3% potassium–treated vines in combined with iron at a moderate level. In conclusion, results indicated that preharvest application of potassium and iron are highly effective to improve the Red Sultana raisin bioactive compositions.
