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Optimization of a Process for the Enzymatic Extraction of Nutrient Enriched Bael Fruit Juice Using Artificial Neural Network (ANN) and Response Surface Methodology (RSM)
International Journal of Fruit Science ( IF 2.4 ) Pub Date : 2020-09-21 , DOI: 10.1080/15538362.2020.1834898
Akshay Sonawane 1 , Sumit Sudhir Pathak 1 , Rama Chandra Pradhan 1
Affiliation  

ABSTRACT Bael fruit pulp was treated with pectinase enzyme for the easy extraction and high recovery of juice with nutritive properties. The response surface methodology (RSM) and artificial neural network (ANN) modeling were carried out on runs of Box-Behnken design of three variables for the optimization process and identification of the best modeling method. Pectinase concentration of 0.22 g/100 g of pulp, the temperature of 46.20°C, and time of 6.35 hours was found out to be RSM optimized value of variables whereas pectinase concentration of 0.18 g/100 g of pulp, the temperature of 46.81°C and time of 6.09 hours was obtained to be ANN-GA optimized value of variables. From the values of the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE), ANN was found out to be better than RSM. This research would help in the commercial production of bael fruit juice on an industrial scale.

中文翻译:

使用人工神经网络 (ANN) 和响应面方法 (RSM) 优化酶法提取富含营养的 Bael 果汁的工艺

摘要 Bael 果肉用果胶酶处理,易于提取和高回收率的具有营养特性的果汁。响应面方法 (RSM) 和人工神经网络 (ANN) 建模对三个变量的 Box-Behnken 设计运行进行了优化过程和最佳建模方法的识别。果胶酶浓度为 0.22 g/100 g 果肉,温度为 46.20°C,时间为 6.35 小时被发现是 RSM 优化变量值,而果胶酶浓度为 0.18 g/100 g 果肉,温度为 46.81° C 和 6.09 小时的时间得到 ANN-GA 优化的变量值。从决定系数 (R2)、均方根误差 (RMSE) 和平均绝对误差 (MAE) 的值来看,ANN 优于 RSM。
更新日期:2020-09-21
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