柿饼干燥中单宁变化规律及BP神经网络预测模型的建立
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摘要
研究不同温度、不同初始单宁含量、不同水分含量对柿饼干制过程中可溶性单宁变化规律的影响,并建立BP神经网络预测模型。结果表明:在35～55℃范围内,温度越高,可溶性单宁脱涩时间越短,且每个温度下均出现返涩现象;初始单宁含量越高,脱涩时间越长,但初始单宁含量在低浓度范围内,脱涩时间不受单宁浓度的影响;水分含量影响脱涩速率,水分含量越低,脱涩越困难。通过建立的BP神经模型可知,BP网络结构为4-6-1,BP预测模型的相关系数为0.966,验证集模型的相关系数为0.93,证明BP神经网络可以对干燥过程中的可溶性单宁含量进行预测。
The effects of temperature, initial tannin content and moisture content on the variation of soluble tannin in dried persimmon are studied, and a BP neural network prediction model is established. The results show that in the temperature range of 35～55 ℃,the higher the temperature, the shorter the deastringent time of soluble tannin, and there is a back-astringency phenomenon at each temperature,the higher the initial tannin content, the longer the deastringent time, but the low the initial tannin content, the deastringent time is not affected by tannin content,the water content affects the speed of deastringent, and the lower the water content, the more difficult the deastringent. The BP neural network structure is 4-6-1, the correlation coefficient of BP prediction model is 0.966, and the correlation coefficient of validation set model is 0.93. It proves that BP neural network can predict the content of soluble tannin in drying process.
The effects of temperature, initial tannin content and moisture content on the variation of soluble tannin in dried persimmon are studied, and a BP neural network prediction model is established. The results show that in the temperature range of 35～55 ℃,the higher the temperature, the shorter the deastringent time of soluble tannin, and there is a back-astringency phenomenon at each temperature,the higher the initial tannin content, the longer the deastringent time, but the low the initial tannin content, the deastringent time is not affected by tannin content,the water content affects the speed of deastringent, and the lower the water content, the more difficult the deastringent. The BP neural network structure is 4-6-1, the correlation coefficient of BP prediction model is 0.966, and the correlation coefficient of validation set model is 0.93. It proves that BP neural network can predict the content of soluble tannin in drying process.
引文
[1]张宝善,伍晓红,陈锦屏.柿单宁研究进展[J].陕西师范大学学报(自然科学版),2008(1):99-105.
[2]徐君驰,张青林,徐莉清,等.柿果脱涩机理研究新进展[J].园艺学报,2016,43(9):1653-1664.
[3]张海生,陈锦屏.柿饼加工中脱涩和反涩机理的研究[J].食品工业科技,2003(12):39-40.
[4]杜建明.柿果实的脱涩机理[J].食品科学,1993(4):17-19.
[5]Guiné R P F,Barroca M J,Gon?alves F J,et al.Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments[J].Food Chemistry,2015,168:454-459.
[6]Aghbashlo M,Hosseinpour S,Ghasemi M.Computer vision technology for real-time food quality assurance during drying process[J].Trends in Food Science & Technology,2014,39(1):76-84.
[7]GB 5009.3-2016,食品中水分的测定[S].
[8]Tessmer M A,Kluge R A,Appezzato-da-Glória B.The accumulation of tannins during the development of "Giombo" and "Fuyu" persimmon fruits[J].Scientia Horticulturae,2014,172:292-299.
[9]Benarie R,Sonego L.Temperature affects astringency removal and recurrence in persimmon[J].Journal of Food Science,1993,58(6):1397-1400.
[10]董文宾,周悦,修秀红.柿子脱涩新工艺及其影响因素研究[J].陕西科技大学学报(自然科学版),2014,32(2):109-112.
[11]Fukushima T,Kitamura T,Murayama H,et al.Mechanisms of astringency removal by ethanol treatment in "Hiratanenashi" kaki fruits[J].Journal of the Japanese Society for Horticultural Science,1991,60(3):685-694.
[12]张家年,刘冬,谭军,等.柿饼加工过程中乙烯利对脱涩和干燥的影响[J].中国果树,1997(4):24-25.
[13]张桂霞,王英超,魏欣,等.柿果软化过程中单宁和可溶性固形物含量的变化[J].安徽农业科学,2009(14):6599-6600.
[14]张宝善,张有林.涩柿生长、脱涩及返涩单宁细胞形态的变化[J].西北植物学报,2000(2):303-308.
[15]刘兴华,王启.柿饼加工技术[J].食品科学,1990(3):46-51.
[16]Nadian M H,Rafiee S,Aghbashlo M,et al.Continuous real-time monitoring and neural network modeling of apple slices color changes during hot air drying[J].Food and Bioproducts Processing,2015,94:263-274.
[17]Hecht-Nielsen R.Theory of the backpropagation neural network[J].Neural Networks,1988(1):445.
[18]王继龙,刘晓霞,魏舒畅,等.基于BP神经网络的纤维性根茎药材超滤总皂苷保留率预测模型[J].食品工业科技,2016,37(12):85-88.
[2]徐君驰,张青林,徐莉清,等.柿果脱涩机理研究新进展[J].园艺学报,2016,43(9):1653-1664.
[3]张海生,陈锦屏.柿饼加工中脱涩和反涩机理的研究[J].食品工业科技,2003(12):39-40.
[4]杜建明.柿果实的脱涩机理[J].食品科学,1993(4):17-19.
[5]Guiné R P F,Barroca M J,Gon?alves F J,et al.Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments[J].Food Chemistry,2015,168:454-459.
[6]Aghbashlo M,Hosseinpour S,Ghasemi M.Computer vision technology for real-time food quality assurance during drying process[J].Trends in Food Science & Technology,2014,39(1):76-84.
[7]GB 5009.3-2016,食品中水分的测定[S].
[8]Tessmer M A,Kluge R A,Appezzato-da-Glória B.The accumulation of tannins during the development of "Giombo" and "Fuyu" persimmon fruits[J].Scientia Horticulturae,2014,172:292-299.
[9]Benarie R,Sonego L.Temperature affects astringency removal and recurrence in persimmon[J].Journal of Food Science,1993,58(6):1397-1400.
[10]董文宾,周悦,修秀红.柿子脱涩新工艺及其影响因素研究[J].陕西科技大学学报(自然科学版),2014,32(2):109-112.
[11]Fukushima T,Kitamura T,Murayama H,et al.Mechanisms of astringency removal by ethanol treatment in "Hiratanenashi" kaki fruits[J].Journal of the Japanese Society for Horticultural Science,1991,60(3):685-694.
[12]张家年,刘冬,谭军,等.柿饼加工过程中乙烯利对脱涩和干燥的影响[J].中国果树,1997(4):24-25.
[13]张桂霞,王英超,魏欣,等.柿果软化过程中单宁和可溶性固形物含量的变化[J].安徽农业科学,2009(14):6599-6600.
[14]张宝善,张有林.涩柿生长、脱涩及返涩单宁细胞形态的变化[J].西北植物学报,2000(2):303-308.
[15]刘兴华,王启.柿饼加工技术[J].食品科学,1990(3):46-51.
[16]Nadian M H,Rafiee S,Aghbashlo M,et al.Continuous real-time monitoring and neural network modeling of apple slices color changes during hot air drying[J].Food and Bioproducts Processing,2015,94:263-274.
[17]Hecht-Nielsen R.Theory of the backpropagation neural network[J].Neural Networks,1988(1):445.
[18]王继龙,刘晓霞,魏舒畅,等.基于BP神经网络的纤维性根茎药材超滤总皂苷保留率预测模型[J].食品工业科技,2016,37(12):85-88.