Feasibility Study on Rapid Determination of Tea Polyphenols in Black Tea by Near Infrared Spectroscopy
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摘要: 茶多酚作为茶叶品质检测的重要指标之一，利用近红外光谱分析技术对茶多酚含量进行快速检测具有重要意义。本文以144个红茶样品作为研究对象，采取近红外光谱法结合偏最小二乘法（Partial Least Squares, PLS），分别建立粉末状茶叶样品和完整茶叶样品的茶多酚含量的近红外快速分析模型。结果表明，选用SNV+一阶导数+Savitzky-Golay平滑的预处理方法结合PLS建立的预测模型效果最佳，粉末状茶叶样品所建立模型训练集相关系数（Correlation Coefficient，r）为0.9990，训练集均方根误差（Root Mean Square Error of Calibration, RMSEC）为0.165%，预测集的r为0.9243，预测集均方根误差（Root Mean Square Error of Prediction, RMSEP）为0.972%；完整茶叶样品训练集r为0.9967，RMSEC为0.310%，预测集的r为0.9541，RMSEP为0.870%。结果表明，完整茶叶样品所建立的PLS定量分析模型要优于粉末状茶叶所建立的模型。因此，利用近红外光谱技术可实现对红茶中茶多酚含量的快速、无损检测。
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关键词:
- 红茶 /
- 茶多酚 /
- 近红外光谱 /
- 偏最小二乘法(PLS)
Abstract: Tea polyphenol, a vital indicator used for the detection of tea quality, is of great significance to quickly detect the tea polyphenol content via near infrared spectroscopy. In this paper, the near infrared spectroscopy in combination with partial least squares (PLS) was adopted to establish the rapid analysis models by near infrared for tea polyphenol content of powdered and complete tea samples respectively, using 144 black tea samples as the study objects, revealing that the prediction model established by SNV+first derivative+Savitzky-Golay smoothing combined with PLS had the optimal effect in the results. The correlation coefficient (r) was 0.9990 and the root mean square error of calibration (RMSEC) was 0.165% of the training set, while the r was 0.9243 and the root mean square error of prediction (RMSEP) was 0.972% of the prediction set in powered tea samples. At the same time, the r was 0.9967 and the RMSEC was 0.310% of the training set, while the r was 0.9541 and the RMSEP was 0.870% of the prediction set in complete tea samples. The results showed that the PLS model for complete tea samples was better than that for powdered tea. Therefore, rapid and nondestructive detection of tea polyphenols in black tea can be achieved by near infrared spectroscopy.-
Key words:
- black tea /
- tea polyphenols /
- near infrared spectroscopy /
- partial least squares(PLS)
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图 1 傅里叶变换红外光谱仪原理图
Figure 1. Schematic diagram of Fourier transform infrared spectrometer
图 4 经过导数处理的近红外光谱吸收图
Figure 4. Derivative processed near-infrared spectral absorption patterns
图 5 经过S-G平滑+一阶导数处理后的近红外光谱吸收图
Figure 5. Near-infrared spectral absorption map after smoothing and first derivative processing
图 6 基于PLS模型的不同主成分数对预测结果的影响
Figure 6. The influence of different principal components on the prediction results based on PLS model
图 7 不同形态茶叶样本PLS模型的预测集回归曲线
Figure 7. Prediction set regression curve of PLS model for tea samples with different morphology
图 8 不同主成分数下ANN模型对应的预测集相关系数（r）与均方根误差（RMSEP）
Figure 8. Correlation coefficient (r) and root mean square error (RMSEP) of prediction set corresponding to ANN model under different principal components number
表 1 20组不同浓度的茶多酚含量（%）
Table 1. 20 groups of tea polyphenols at different concentrations (%)
红茶样本编号 茶多酚含量 红茶样本编号 茶多酚含量 H-001 12.89 H-045 11.25 H-010 19.71 H-048 13.84 H-015 14.97 H-053 22.38 H-016 14.35 H-057 19.22 H-021 13.93 H-059 20.66 H-023 25.79 H-062 11.51 H-031 20.17 H-070 18.64 H-034 9.67 H-076 8.78 H-036 12.80 H-083 18.96 H-043 10.02 H-088 14.64 
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表 2 PLS和PCR建模结果
Table 2. PLS and PCR modeling results
样本类别 方法 主因子数 训练集 预测集 RMSEC（%） 相关系数r RMSEP（%） 相关系数r 粉末状样品 PLS+SNV+无 6 1.55 0.9020 1.46 0.8468 PLS+SNV+一阶导数+S-G平滑 6 0.165 0.9990 0.972 0.9243 PLS+SNV+二阶导数+S-G平滑 6 0.118 0.9995 1.65 0.7549 PCR+SNV+无 无 1.57 0.8994 1.42 0.8540 PCR+SNV+一阶导数+S-G平滑 无 1.97 0.8369 1.15 0.9017 PCR+SNV+二阶导数+S-G平滑 无 2.37 0.7517 1.78 0.7371 完整样品 PLS+SNV+无 4 2.27 0.8068 1.88 0.8590 PLS+SNV+一阶导数+S-G平滑 4 0.310 0.9967 0.870 0.9541 PLS+SNV+二阶导数+S-G平滑 4 0.166 0.9991 2.52 0.7875 PCR+SNV+无 无 2.33 0.7971 1.55 0.8899 PCR+SNV+一阶导数+S-G平滑 无 2.51 0.7578 1.38 0.9085 PCR+SNV+二阶导数+S-G平滑 无 3.18 0.5633 2.50 0.6719
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表 3 基于LS-SVM对茶叶样本茶多酚含量的预测结果
Table 3. Prediction results of tea polyphenol content in tea samples based on LS-SVM
样本类别 训练集 预测集 相关系数r RMSEC（%） 相关系数r RMSEP（%） 完整茶叶样本 0.7440 0.9832 0.6000 1.0136 粉末状茶叶样本 0.7936 0.8875 0.6280 1.0143
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