红茶感官品质及成分近红外光谱快速检测模型建立
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摘要
以在发酵过程中小叶种工夫红茶为研究对象,分别建立了基于近红外光谱检测技术的感官品质评分和理化品质指标(茶黄素、茶红素、茶褐素、儿茶素和酚氨比)的定量分析模型。在模型建立过程中,探讨了特征变量优选方法对预测模型的影响。首先,对获取的近红外光谱数据进行标准正态变量变换法(standard normal Z transformation,SNV)预处理,进而采用联合区间偏最小二乘回归(synergy interval PLS,Si-PLS)、随机蛙跳算法(shuffled frog leaping algorithm,SFLA)、竞争性自适应权重取样法(competitiveadaptivereweightedsampling,CARS)和连续投影(successive projections algorithm,SPA),筛选出各品质指标的最优特征波长变量;最后基于优选波长分别建立各发酵品质指标的偏最小二乘法(partial least squares regression,PLS)线性预测模型和支持向量机(support vector regression,SVR)非线性预测模型。模型结果比较表明,Si、CARS、SFLA和SPA等变量筛选方法可有效压缩变量,以及进一步提高模型精度。非线性模型的预测均方根误差值(root-mean-square error of prediction,RMSEP)均明显小于PLS模型,相关性系数(correlation coefficient,R)和相对分析误差(relative percent deviation,RPD)均高于PLS模型。对于红茶发酵品质的检测上,非线性模型性能优于线性模型。感官品质、茶褐素和儿茶素的最优变量SVR预测模型的RPD值分别为3.923、3.234和5.462,酚氨比和茶红素模型的RPD值分别为2.815和2.223。除茶黄素的评价模型外(RPD为1.77),基于最优特征波长的各品质指标SVR模型的RPD值均大于2,表明模型具有极好的预测性能。研究结果为实现工夫红茶发酵品质的近红外光谱快速检测的实际应用奠定理论基础。
Due to the defect of judging fermentation quality by human observation and difficulty in detecting biochemical components quickly Taking WIP(work in progress) of the Congou black tea in the fermentation process as the research object, in this study, we established quantitative analysis models of sensory quality scores and physical and chemical quality indicators(theaflavin, thearubigin, theaflavin, catechin and phenol to ammonia ratio) based on near-infrared spectroscopy(NIRS). In the process of model establishment, the influence of the feature variable selection method on the prediction model was discussed. Firstly, the acquired data of near-infrared spectral was preprocessed by SNV(standard normal Z transformation), and then combined Si-PLS(synergy interval partial least squares), SFLA(shuffled frog leaping algorithm), CARS(competitive adaptive reweighted sampling) and SPA(successive projections algorithm), the optimal characteristic wavelength variable of each quality indicators was then selected. Finally, the PLS(partial least squares) linear prediction model and the SVR(support vector machine regression) nonlinear prediction model of each fermentation quality indicators were established based on the preferred wavelengths. The comparison of model results showed that the variable screening methods such as Si, CARS, SFLA and SPA can effectively compress the variables compared with the full-band variable model and the number of wavelength variables was greatly reduced under the premise of stabilizing the performance of the model. Among them, nine characteristic wavelength variables closely related to the sensory score were optimized by the SPA method, from which the variable compression rate was as high as 98.88%. The best selection methods for the correlation characteristic wavelength variation and the numbers of sensitive characteristic wavelengths screened of TFs(theaflavins), TRs(thearubigins), TBs(teabrownine), catechin and TP/FAA(Ratio of tea polyphenol and three amino acids) were SPA-PLS and 16, SPA-PLS and 10, CARS-PLS and 33, SPA-PLS and 9, SFLA-PLS and 12, respectively and sensitive features wavelengths were screened. The best screening method for the correlation characteristic wavelength variation of total amount of catechin was SPA-PLS, and a total of nine extremely sensitive characteristic wavelengths were screened. The best screening method for the correlation characteristic wavelength variable of TP/FAA(Ratio of tea polyphenol and three amino acids) was SFLA-PLS, and a total of 12 extremely sensitive characteristic wavelengths were screened. The RPD values of SVR prediction models that were established by the optimal variable of sensory quality, TBs and catechin were all greater than 3, which were 3.923, 3.234, and 5.462, respectively. The RPD values indicated that the results of NIR quantitative analysis were accurate and reliable, and the model prediction performance was extremely high, which can be used for quality control. The RPD value of TP/FAA model was 2.815(>2) indicating that the model has a good predictive performance and can be used for quantitative analysis. The RPD value of TRs was 2.223(>2) indicating that the model had good predictive performance. The RPD value of TFs was 1.770(between 1.4 and 1.8), indicating that the model has poor predictive performance and can be used to make rough prediction and correlation assessment for examples. The predictive performance of SVR model of TFs was poor, because the content of TFs was lower(<1%) and the components was extremely complex, which affected the difficulty and precision of the model construction. The study established a quantitative rapid detection method for key chemical constituents in black tea fermentation, and completed dynamic monitoring of fermentation quality status. The research results set a theoretical foundation for the practical application of the near-infrared spectroscopy rapid detection of the fermentation quality of black tea.
Due to the defect of judging fermentation quality by human observation and difficulty in detecting biochemical components quickly Taking WIP(work in progress) of the Congou black tea in the fermentation process as the research object, in this study, we established quantitative analysis models of sensory quality scores and physical and chemical quality indicators(theaflavin, thearubigin, theaflavin, catechin and phenol to ammonia ratio) based on near-infrared spectroscopy(NIRS). In the process of model establishment, the influence of the feature variable selection method on the prediction model was discussed. Firstly, the acquired data of near-infrared spectral was preprocessed by SNV(standard normal Z transformation), and then combined Si-PLS(synergy interval partial least squares), SFLA(shuffled frog leaping algorithm), CARS(competitive adaptive reweighted sampling) and SPA(successive projections algorithm), the optimal characteristic wavelength variable of each quality indicators was then selected. Finally, the PLS(partial least squares) linear prediction model and the SVR(support vector machine regression) nonlinear prediction model of each fermentation quality indicators were established based on the preferred wavelengths. The comparison of model results showed that the variable screening methods such as Si, CARS, SFLA and SPA can effectively compress the variables compared with the full-band variable model and the number of wavelength variables was greatly reduced under the premise of stabilizing the performance of the model. Among them, nine characteristic wavelength variables closely related to the sensory score were optimized by the SPA method, from which the variable compression rate was as high as 98.88%. The best selection methods for the correlation characteristic wavelength variation and the numbers of sensitive characteristic wavelengths screened of TFs(theaflavins), TRs(thearubigins), TBs(teabrownine), catechin and TP/FAA(Ratio of tea polyphenol and three amino acids) were SPA-PLS and 16, SPA-PLS and 10, CARS-PLS and 33, SPA-PLS and 9, SFLA-PLS and 12, respectively and sensitive features wavelengths were screened. The best screening method for the correlation characteristic wavelength variation of total amount of catechin was SPA-PLS, and a total of nine extremely sensitive characteristic wavelengths were screened. The best screening method for the correlation characteristic wavelength variable of TP/FAA(Ratio of tea polyphenol and three amino acids) was SFLA-PLS, and a total of 12 extremely sensitive characteristic wavelengths were screened. The RPD values of SVR prediction models that were established by the optimal variable of sensory quality, TBs and catechin were all greater than 3, which were 3.923, 3.234, and 5.462, respectively. The RPD values indicated that the results of NIR quantitative analysis were accurate and reliable, and the model prediction performance was extremely high, which can be used for quality control. The RPD value of TP/FAA model was 2.815(>2) indicating that the model has a good predictive performance and can be used for quantitative analysis. The RPD value of TRs was 2.223(>2) indicating that the model had good predictive performance. The RPD value of TFs was 1.770(between 1.4 and 1.8), indicating that the model has poor predictive performance and can be used to make rough prediction and correlation assessment for examples. The predictive performance of SVR model of TFs was poor, because the content of TFs was lower(<1%) and the components was extremely complex, which affected the difficulty and precision of the model construction. The study established a quantitative rapid detection method for key chemical constituents in black tea fermentation, and completed dynamic monitoring of fermentation quality status. The research results set a theoretical foundation for the practical application of the near-infrared spectroscopy rapid detection of the fermentation quality of black tea.
引文
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[15]董春旺,叶阳,江用文,等.工夫红茶可视化富氧发酵机设计及试验研究[J].茶叶科学,2015,35(4):370-376.Dong Chunwang,Ye Yang,Jiang Yongwen,et al.Design and experimental investigation of congou black tea visual aerobic fermentation machine[J].Journal of Tea Science,2015,35(4):370-376.(in Chinese with English abstract)
[16]Roberts E A H,Smith R F.Spectrophotometric measurements of theaflavins and thearubigins in black tea liquors in assessments of quality in teas[J].Analyst,1961,86(1019):94-98.
[17]Dong C W,Zhu H K,Zhao J W,et al.Sensory quality evaluation for appearance of needle-shaped green tea based on computer vision and nonlinear tools[J].Zhejiang Univ Sci B,2017,18(6):544-548.
[18]Chang C W,Laird D A,Hurburgh C R.Influence of soil moisture on near-infrared reflectance spectroscopic measurement of soil properties[J].Soil Science,2005,170(4):244-255.
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[20]陈全胜,赵杰文,蔡健荣,等.基于近红外光谱和机器视觉的多信息融合技术评判茶叶品质[J].农业工程学报,2008,24(3):5-10.Chen Quansheng,Zhao Jiewen,Cai Jianrong,et al.Inspection of tea quality by using multi-sensor information fusion based on NIR spectroscopy and machine vision[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2008,24(3):5-10.(in Chinese with English abstract)
[21]宁井铭,宛晓春,张正竹,等.近红外光谱技术结合人工神经网络判别普洱茶发酵程度[J].农业工程学报,2013,29(11):255-260.Ning Jingming,Wan Xiaochun,Zhang Zhengzhu,et al.Discriminating fermentation degree of Pu’er tea based on NIR spectroscopy and artificial neural network[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2013,29(11):255-260.(in Chinese with English abstract)
[22]宁井铭,颜玲,张正竹,等.祁门红茶加工中氨基酸和儿茶素快速检测模型建立[J].光谱学与光谱分析,2015,35(12):3422-3426.Ning Jingming,Yan Ling,Zhang Zhengzhu,et al.Rapid and dynamic determination models of amino acids and catechins concentrations during the processing procedures of keemun black tea[J].Spectroscopy and Spectral Analysis,2015,35(12):3422-3426.(in Chinese with English abstract)
[23]宁井铭,孙京京,朱小元,等.基于图像和光谱信息融合的红茶萎凋程度量化判别[J].农业工程学报,2016,32(24):303-308.Ning Jingming,Sun Jingjing,Zhu Xiaoyuan,et al.Discriminant of withering quality of Keemun black tea based on information fusion of image and spectrum[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2016,32(24):303-308.(in Chinese with English abstract)
[24]Paulo H G D D Marcelo F P,Mário César Ugulino de Araújo.Using SPA-PLS and NIR spectroscopy for the determination of total polyphenols and moisture in commercial tea samples[J].Analytical Methods,2015,7(8):3379-3384.
[25]Sinija V R,Mishra H N.FT-NIR spectroscopy for caffeine estimation in instant green tea powder and granules[J].LWT-Food Science and Technology,2009,42(5):998-1002.
[26]He Y,Li X,Deng X.Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model[J].Journal of Food Engineering,2007,79(4):1238-1242.
[27]Lee M,Hwang Y,Lee J,et al.The characterization of caffeine and nine individual catechins in the leaves of green tea(Camellia sinensis L.)by near-infrared reflectance spectroscopy[J].Food Chemistry,2014,158(3):51-57.
[28]Qi S,Ouyang Q,Chen Q,et al.Real-time monitoring of total polyphenols content in tea using a developed optical sensors system[J].Journal of Pharmaceutical and Biomedical Analysis,2014,97(1):16-22.
[29]Yu X,Liu K,Wu D,et al.Raisin quality classification using least squares support vector machine(LSSVM)based on combined color and texture features[J].Food and Bioprocess Technology,2012,5(5):1552-1563.
[30]Jankun J,Kondray V,Skrzypczakjankun E.Analysis of the inhibition of PAI-1 by metal theaflavin complexes and their degradation products[J].International Journal of Molecular Medicine,2013,31(5):1153-1158.
[31]桂安辉,朱宏凯,何华锋,等.基于动态聚类分析的工夫红茶发酵过程品质成分变化与发酵适度判别的研究[J].中国农学通报,2016,32(7):196-204.Gui Anhui,Zhu Hongkai,He Huafeng,et al.Quality components variation and fermenting degree discrimination during fermentation process of congou black tea based on dynamic cluster analysis[J].Chinese Agricultural Science Bulletin,2016,32(7):196-204.(in Chinese with English abstract)
[2]Pan H,Gao Y,Tu Y.Mechanisms of body weight reduction by black tea polyphenols[J].Molecules,2016,21(12):1659.
[3]Zhu F,Sakulnak R,Wang S.Effect of black tea on antioxidant,textural,and sensory properties of Chinese steamed bread[J].Food Chemistry,2016,194:1217-1223.
[4]Owuor P O,Obanda M,Nyirenda H E,et al.Influence of region of production on clonal black tea chemical characteristics[J].Food Chemistry,2008,108(1):263-271.
[5]Roberts E.The chemistry of tea manufacture[J].Journal of the Science of Food and Agriculture,1958,9(7):381-390.
[6]Muthumani T,Kumar R S.Influence of fermentation time on the development of compounds responsible for quality in black tea[J].Food Chemistry,2007,101(1):98-102.
[7]Ghosh A,Tamuly P,Bhattacharyya N,et al.Estimation of theaflavin content in black tea using electronic tongue[J].Journal of Food Engineering,2012,110(1):71-79.
[8]Sharma P,Ghosh A,Tudu B,et al.Monitoring the fermentation process of black tea using QCM sensor based electronic nose[J].Sensors and Actuators B:Chemical,2015,219:146-157.
[9]Gill G,Kumar A,Agarwal R.Monitoring and grading of tea by computer vision-A review[J].Journal of Food Engineering,2011,106(1):13-19.
[10]Dong C,Liang G,Hu B,et al.Prediction of congou black tea fermentation quality indices from color features using non-linear regression methods[J].Scientific Reports,doi:10.1038/s41598-018-28767-2.
[11]杨新河,李勤,黄建安,等.普洱茶茶色素提取工艺条件的响应面分析及其抗氧化性活性研究[J].食品科学,2011,32(6):1-6.Yang Xinhe,Li Qin,Huang Jian’an,et al.Optimization of process conditions for pu-erh tea pigment extraction by response surface analysis and its antioxidant activity[J].Food Science,2011 32(6):1-6.(in Chinese with English abstract)
[12]董春旺,赵杰文,朱宏凯,等.基于RSM和BP-AdaBoost-GA的红茶发酵性能参数优化[J].农业机械学报,2017,48(5):335-342.Dong Chunwang,Zhao Jiewen,Zhu Hongkai,et al.Parameter optimization of black tea fermentation machine Based on RSM and BP-Ada Boost-GA[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(5):335-342.(in Chinese with English abstract)
[13]Dong C,Zhu H,Wang J,et al.Prediction of black tea fermentation quality indices using NIRS and nonlinear tools[J].Food Science and Biotechnology,2017,26(4):1-8.
[14]Chen Q,Zhang D,Pan W,et al.Recent developments of green analytical techniques in analysis of tea’s quality and nutrition[J].Trends in Food Science and Technology,2015,43(1):63-82.
[15]董春旺,叶阳,江用文,等.工夫红茶可视化富氧发酵机设计及试验研究[J].茶叶科学,2015,35(4):370-376.Dong Chunwang,Ye Yang,Jiang Yongwen,et al.Design and experimental investigation of congou black tea visual aerobic fermentation machine[J].Journal of Tea Science,2015,35(4):370-376.(in Chinese with English abstract)
[16]Roberts E A H,Smith R F.Spectrophotometric measurements of theaflavins and thearubigins in black tea liquors in assessments of quality in teas[J].Analyst,1961,86(1019):94-98.
[17]Dong C W,Zhu H K,Zhao J W,et al.Sensory quality evaluation for appearance of needle-shaped green tea based on computer vision and nonlinear tools[J].Zhejiang Univ Sci B,2017,18(6):544-548.
[18]Chang C W,Laird D A,Hurburgh C R.Influence of soil moisture on near-infrared reflectance spectroscopic measurement of soil properties[J].Soil Science,2005,170(4):244-255.
[19]Bellon V,Vigneau J L,Sévila F.Infrared and near-infrared technology for the food industry and agricultural uses:On-line applications[J].Food Control,1994,5(1):21-27.
[20]陈全胜,赵杰文,蔡健荣,等.基于近红外光谱和机器视觉的多信息融合技术评判茶叶品质[J].农业工程学报,2008,24(3):5-10.Chen Quansheng,Zhao Jiewen,Cai Jianrong,et al.Inspection of tea quality by using multi-sensor information fusion based on NIR spectroscopy and machine vision[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2008,24(3):5-10.(in Chinese with English abstract)
[21]宁井铭,宛晓春,张正竹,等.近红外光谱技术结合人工神经网络判别普洱茶发酵程度[J].农业工程学报,2013,29(11):255-260.Ning Jingming,Wan Xiaochun,Zhang Zhengzhu,et al.Discriminating fermentation degree of Pu’er tea based on NIR spectroscopy and artificial neural network[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2013,29(11):255-260.(in Chinese with English abstract)
[22]宁井铭,颜玲,张正竹,等.祁门红茶加工中氨基酸和儿茶素快速检测模型建立[J].光谱学与光谱分析,2015,35(12):3422-3426.Ning Jingming,Yan Ling,Zhang Zhengzhu,et al.Rapid and dynamic determination models of amino acids and catechins concentrations during the processing procedures of keemun black tea[J].Spectroscopy and Spectral Analysis,2015,35(12):3422-3426.(in Chinese with English abstract)
[23]宁井铭,孙京京,朱小元,等.基于图像和光谱信息融合的红茶萎凋程度量化判别[J].农业工程学报,2016,32(24):303-308.Ning Jingming,Sun Jingjing,Zhu Xiaoyuan,et al.Discriminant of withering quality of Keemun black tea based on information fusion of image and spectrum[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2016,32(24):303-308.(in Chinese with English abstract)
[24]Paulo H G D D Marcelo F P,Mário César Ugulino de Araújo.Using SPA-PLS and NIR spectroscopy for the determination of total polyphenols and moisture in commercial tea samples[J].Analytical Methods,2015,7(8):3379-3384.
[25]Sinija V R,Mishra H N.FT-NIR spectroscopy for caffeine estimation in instant green tea powder and granules[J].LWT-Food Science and Technology,2009,42(5):998-1002.
[26]He Y,Li X,Deng X.Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model[J].Journal of Food Engineering,2007,79(4):1238-1242.
[27]Lee M,Hwang Y,Lee J,et al.The characterization of caffeine and nine individual catechins in the leaves of green tea(Camellia sinensis L.)by near-infrared reflectance spectroscopy[J].Food Chemistry,2014,158(3):51-57.
[28]Qi S,Ouyang Q,Chen Q,et al.Real-time monitoring of total polyphenols content in tea using a developed optical sensors system[J].Journal of Pharmaceutical and Biomedical Analysis,2014,97(1):16-22.
[29]Yu X,Liu K,Wu D,et al.Raisin quality classification using least squares support vector machine(LSSVM)based on combined color and texture features[J].Food and Bioprocess Technology,2012,5(5):1552-1563.
[30]Jankun J,Kondray V,Skrzypczakjankun E.Analysis of the inhibition of PAI-1 by metal theaflavin complexes and their degradation products[J].International Journal of Molecular Medicine,2013,31(5):1153-1158.
[31]桂安辉,朱宏凯,何华锋,等.基于动态聚类分析的工夫红茶发酵过程品质成分变化与发酵适度判别的研究[J].中国农学通报,2016,32(7):196-204.Gui Anhui,Zhu Hongkai,He Huafeng,et al.Quality components variation and fermenting degree discrimination during fermentation process of congou black tea based on dynamic cluster analysis[J].Chinese Agricultural Science Bulletin,2016,32(7):196-204.(in Chinese with English abstract)