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中国精品科技期刊2020
尚朝杰, 王维民, 谌素华, 张晓迪, 刘艳春. 应用神经网络优化芒果果汁的酶解工艺[J]. 食品工业科技, 2014, (19): 238-241. DOI: 10.13386/j.issn1002-0306.2014.19.042
引用本文: 尚朝杰, 王维民, 谌素华, 张晓迪, 刘艳春. 应用神经网络优化芒果果汁的酶解工艺[J]. 食品工业科技, 2014, (19): 238-241. DOI: 10.13386/j.issn1002-0306.2014.19.042
SHANG Chao-jie, WANG Wei-min, CHEN Su-hua, ZHANG Xiao-di, LIU Yan-chun. Optimization of mango juice enzymolysis process by neural network[J]. Science and Technology of Food Industry, 2014, (19): 238-241. DOI: 10.13386/j.issn1002-0306.2014.19.042
Citation: SHANG Chao-jie, WANG Wei-min, CHEN Su-hua, ZHANG Xiao-di, LIU Yan-chun. Optimization of mango juice enzymolysis process by neural network[J]. Science and Technology of Food Industry, 2014, (19): 238-241. DOI: 10.13386/j.issn1002-0306.2014.19.042

应用神经网络优化芒果果汁的酶解工艺

Optimization of mango juice enzymolysis process by neural network

  • 摘要: 为研究酶解条件对芒果果汁得率的影响,在单因素的基础上建立了芒果果汁酶解工艺的神经网络模型,并对其工艺参数进行了优化。研究结果表明:果胶酶用量0.007%、纤维素酶用量0.001%、酶解温度40.6℃、酶解时间60min为最佳酶解工艺参数。此时芒果带皮果汁得率为80.48%,去皮果汁得率为66.47%,提高14.01%,且带皮果汁品质变化较小,可以接受。本研究为芒果果汁生产工艺改进提供参考。 

     

    Abstract: In order to study the influence of enzymatic hydrolysis conditions on mango juice yield, the neural network model of mango juice enzymolysis process was established on the basis of single factors, and the process parameters were optimized.The results showed that the optimal enzymolysis process parameters were as follows, pectinase dosage 0.007%, cellulase dosage 0.001%, reaction temperature 40.6℃ and time 60 min. Under these conditions, the yield of mango pulp with peel was 80.48%, the yield of mango pulp without peel was 66.47%, increased by 14.01%, and the quality of juice can be accepted with small changes.This study provides a reference for mango juice production process improvement.

     

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