基于电子鼻多传感器融合的茶叶存储时间识别
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摘要
借助电子鼻检测存储60、120、180、240、300、360 d的黄山毛峰茶香气信息,根据电子鼻各传感器响应曲线变化特点,选取出1组能够表征不同香气信息的基本特征变量,分别采用主成分回归(PCR)、偏最小二乘回归(PLS)和BP神经网络(BPNN)方法,建立茶叶存储时间的预测模型。测试样本集对3种预测模型的检验结果表明:PCR、PLS、BPNN模型的预测标准误差分别为10.05、6.04、3.21d;最大预测相对误差分别为11.03%、7.02%、5.89%;平均预测相对误差分别为6.73%、4.74%、3.62%;预测值与实际值之间的决定系数R2分别为0.862、0.896、0.987。3种模型都能较好地对茶叶存储时间进行预测,相比较而言,BPNN模型性能最优,PLS模型性能优于PCR模型。
A recognition methods for storage time of tea was set up based on the Huangshanmaofeng tea under storage time of 60,120,180,240,300 and 360 d detected by electronic nose.According to response curves of electronic nose,a set of essential characteristic variables were selected.On the basis of these variables,principle component regression(PCR),partial least squares regression(PLS) and back propagation neural network(BPNN) was applied to build the prediction model for storage time of tea,respectively.Three prediction models were validated by test sample set.The results indicated that standard error of prediction of PCR,PLS and BPNN models were 10.05,6.04 and 3.21 d,respectively;the maximum relative error 11.03%,7.02% and 5.89%,respectively;the mean relative error 6.73%,4.74%,and 3.62%,respectively;determination coefficient between predicted value and real value 0.862,0.896 and 0.987,respectively.All of the models could predict storage time of tea well.BPNN was the model with the best performance and PLS is better than PCR.
A recognition methods for storage time of tea was set up based on the Huangshanmaofeng tea under storage time of 60,120,180,240,300 and 360 d detected by electronic nose.According to response curves of electronic nose,a set of essential characteristic variables were selected.On the basis of these variables,principle component regression(PCR),partial least squares regression(PLS) and back propagation neural network(BPNN) was applied to build the prediction model for storage time of tea,respectively.Three prediction models were validated by test sample set.The results indicated that standard error of prediction of PCR,PLS and BPNN models were 10.05,6.04 and 3.21 d,respectively;the maximum relative error 11.03%,7.02% and 5.89%,respectively;the mean relative error 6.73%,4.74%,and 3.62%,respectively;determination coefficient between predicted value and real value 0.862,0.896 and 0.987,respectively.All of the models could predict storage time of tea well.BPNN was the model with the best performance and PLS is better than PCR.
引文
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[22]AY?EGüL U,RECEP?.Efficient android electronic nose design for recognition and perception of fruit odors using Kernel Extreme Learning Machines[J].Chemometrics and Intelligent Laboratory Systems,2017,166:69-80.
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[25]张红梅,田辉,何玉静,等.茶叶中茶多酚含量电子鼻技术检测模型研究[J].河南农业大学学报,2012,46(3):302-306.
[26]张红梅,王俊,余泳昌,等.基于电子鼻技术的信阳毛尖茶咖啡碱检测方法[J].传感技术学报,2011,24(8):1223-1227.
[27]BHATTACHARYA N,TUDU B,JANA A,et al.Preemptive identification of optimum fermentation time for black tea using electronic nose[J].Sensors and Actuators B,2008,131:110-116.
[28]YANG Z Y,DONG F,KAZUO S,et al.Identification of coumarin enriched Japanese green teas and their particular flavor using electronic nose[J].Journal of Food Engineering,2009,92:312-316.
[29]严正红,周俊,毛家敏.基于GA-BP神经网络的番茄应力松弛参数的估计[J].湖南农业大学学报(自然科学版),2018,44(5):565-569.
[2]杨春兰,薛大为,鲍俊宏.黄山毛峰茶贮藏时间电子鼻检测方法研究[J].浙江农业学报,2016,28(4):676-681.
[3]周波,钱堃,马旭东,等.基于集员估计的室内移动机器人多传感器融合定位[J].控制理论与应用,2017,34(4):541-550.
[4]徐礼胜,靳雁冰,王琦文,等.多传感器融合的穿戴式心率监测系统[J].哈尔滨工业大学学报,2015,47(5):97-103.
[5]蒙万隆,郑丼敏,杨璐,等.电子鼻技术对猪肉挥収性盐基氮的预测研究[J].食品工业科技,2018,39(7):243-248.
[6]张娟,张申,张力,等.电子鼻结合统计学分析对牛肉中猪肉掺假的识别[J].食品科学,2018,39(4):296-300.
[7]SOARES S,AMARAL J S,MBPP O,et al.A SYBR green real-time PCR assay to detect and quantify pork meat in processed poultry meat products[J].Meat Science,2013,94(1):115-120.
[8]HADDI Z,BARBRI N E,TAHRI K,et al.Instrumental assessment of red meat origins and their storage time using electronic sensing systems[J].Analytical Methods,2015,7(12):5193-5203.
[9]NURJULIANA M,CHE M Y,MAT H D,et al.Rapid identification of pork for halal authentication using the electronic nose and gas chromatography mass spectrometer with headspace analyzer[J].Meat Science,2011,88(4):638-644.
[10]洪雪珍,韦真博,海铮,等.基于电子鼻和神经网络的牛肉新鲜度的检测[J].现代食品科技,2014,30(4):279-285.
[11]贾茹,张娟,王佳奕,等.电子鼻结合化学计量法对羊奶中蛋白质掺假的识别[J].食品科学,2017,38(8):308-312.
[12]CUI S,WANG J,YANG L,et al.Qualitative and quantitative analysis on aroma characteristics of ginseng at different ages using E-nose and GC-MS combined with chemometrics[J].Journal of Pharmaceutical and Biomedical Analysis,2015,102:64-77.
[13]SINGH H,RAJ V B,KUMAR J,et al.Metal oxide SAWE-nose employing PCA and ANN for the identification of binary mixture of DMMP and methanol[J].Sensors and Actuators B:Chemical,2014,200:147-156.
[14]贾茹,刘占东,马利杰,等.电子鼻对山羊奶中致膻游离脂肪酸的识别研究[J].中国乳品工业,2015,43(3):18-21.
[15]BOUGRINI M,TAHRI K,HADDI Z,et al.Aging time and brand determination of pasteurized milk using a multisensor E-nose combined with a voltammetric E-tongue[J].Materials Science and Engineering:C,2014,45:348-358.
[16]马利杰,贾茹,杨春杰,等.基于电子鼻技术对羊奶粉中掺假牛奶粉的快速检测[J].中国乳品工业,2014,42(11):47-50.
[17]HUI G H,WU Y L,YE D D,et al.Fuji apple storage time predictive method using electronic nose[J].Food Analytical Methods,2013,6(1):82-88.
[18]张鹏,李江阔,陈绍慧.基于电子鼻判别富士苹果货架期的研究[J].食品工业科技,2015,36(5):272-276.
[19]宋小青,仸亚梅,张艳宜,等.电子鼻对低温贮藏猕猴桃品质的预测[J].食品科学,2014,35(20):230-235.
[20]PATHANGE L P,MALLIKARJUNAN P,MARINI R P,et al.Non-destructive evaluation of apple maturity using an electronic nose system[J].Journal of Food Engineering,2006,77(4):1018-1023.
[21]李莹,仸亚梅,张爽,等.基于电子鼻的苹果低温贮藏时间及品质预测[J].西北农林科技大学学报(自然科学版),2015,43(5):183-191.
[22]AY?EGüL U,RECEP?.Efficient android electronic nose design for recognition and perception of fruit odors using Kernel Extreme Learning Machines[J].Chemometrics and Intelligent Laboratory Systems,2017,166:69-80.
[23]于慧春,王俊.电子鼻技术在茶叶品质检测中的应用研究[J].传感技术学报,2008,21(5):748-752.
[24]陈哲,赵杰文.基于电子鼻技术的碧螺春茶叶品质等级检测研究[J].农机化研究,2012,34(11):133-137.
[25]张红梅,田辉,何玉静,等.茶叶中茶多酚含量电子鼻技术检测模型研究[J].河南农业大学学报,2012,46(3):302-306.
[26]张红梅,王俊,余泳昌,等.基于电子鼻技术的信阳毛尖茶咖啡碱检测方法[J].传感技术学报,2011,24(8):1223-1227.
[27]BHATTACHARYA N,TUDU B,JANA A,et al.Preemptive identification of optimum fermentation time for black tea using electronic nose[J].Sensors and Actuators B,2008,131:110-116.
[28]YANG Z Y,DONG F,KAZUO S,et al.Identification of coumarin enriched Japanese green teas and their particular flavor using electronic nose[J].Journal of Food Engineering,2009,92:312-316.
[29]严正红,周俊,毛家敏.基于GA-BP神经网络的番茄应力松弛参数的估计[J].湖南农业大学学报(自然科学版),2018,44(5):565-569.