红茶感官品质及成分近红外光谱快速检测模型建立

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。

Near-infrared spectroscopy detection model for sensory quality and chemical constituents 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.