Establishment of a Hyperspectral Spectroscopy-Based Biochemical Component Detection Model for Green Tea Processing Materials
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摘要: 目的：建立高光谱技术快速检测绿茶加工原料生化成分的方法。方法：用高光谱相机对加工过程中的茶叶原料进行实时拍摄，获取茶叶原料的光谱数据；对样本的含水率、游离氨基酸、茶多酚以及咖啡碱的含量进行检测；光谱数据预处理后，利用无信息变量消除法（uninformative variable elimination，UVE）、竞争性自适应重加权法（competitive adaptive reweighted sampling，CARS）、连续投影算法（successive projections algorithm，SPA）三种特征提取方法与偏最小二乘（partial least-squares，PLS）、支持向量机（support vector machine，SVM）和随机森林（random forest，RF）三种机器学习模型分别组合进行建模分析，预测茶叶原料中的含水率、游离氨基酸、茶多酚和咖啡碱的含量。结果：茶叶原料的含水率、游离氨基酸、茶多酚和咖啡碱最佳组合模型分别为UVE-RF、CARS-SVM、UVE-SVM、UVE-PLS，决定系数（coefficient of determination，R2）分别为0.99、0.92、0.97、0.87，交互验证均方根误差（root mean square error of cross validation，RMSECV）分别为0.7615%、0.723 μg·g−1、0.3701%、0.1197%，相对分析误差（relative percent difference，RPD）分别为10.2093%、25.446 μg·g−1、3.5851%、2.5284%。结论：相关性高，建模误差合理，模型效果优秀，可以有效检测加工过程中茶叶原料的生化成分。该方法不仅无损，而且快速准确，有望在茶叶加工中得到广泛应用。Abstract: Objective: To establish a method for rapid detection of biochemical components of green tea processing materials by hyperspectral technique. Methods: The hyperspectral camera was employed to capture real-time images of the tea raw materials during the processing procedure in order to collect the spectral data of the tea raw materials. The samples' moisture content, free amino acids, tea polyphenols, and caffeine content were all found. After spectral data preprocessing, three feature extraction methods, uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA) and partial least-squares (PLS), support vector machine (SVM) and random forest (RF) were combined to predict the water content, free amino acids, polyphenols and caffeine content of tea raw materials. Result: The best combination models of water content, free amino acids, tea polyphenols and caffeine of tea raw materials were UVE-RF, CARS-SVM, UVE-SVM and UVE-PLS, with the coefficient of determination (R2) of 0.99, 0.92, 0.97 and 0.87, and the root mean square error of cross validation (RMSECV) of 0.7615%, 0.723 μg·g−1, 0.3701% and 0.1197%, respectively, the relative percent difference (RPD) was 10.2093%, 25.446 μg·g−1, 3.5851% and 2.5284%, respectively. Conclusion: High correlation, appropriate modeling error, outstanding model effect, and the ability to accurately identify the biochemical components of raw materials throughout processing are all characteristics of the model. This technique is not only quick and precise but also non-destructive. In the processing of tea, it is anticipated to be widely employed.
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图 5 经MSC+二阶导数微分法+S-G平滑处理后的光谱曲线
Figure 5. Spectral curve after MSC+second derivative differentiation+S-G smoothing
图 6 特征波段分布
注：A-含水率UVE；B-含水率CARS；C-含水率SPA；D-茶多酚UVE；E-茶多酚CARS；F-茶多酚SPA；G-氨基酸UVE；H-氨基酸CARS；I-氨基酸SPA；J-咖啡碱UVE；K-咖啡碱CARS；L-咖啡碱SPA。
Figure 6. Characteristic band distribution
图 7 各成分最佳模型测试数据集散点图
注：A-含水率UVE-PLS；B-含水率CARS-SVM；C-含水率SPA-RF；D-茶多酚SPA-PLS；E-茶多酚UVE-SVM；F-茶多酚UVE-RF；G-氨基酸CARS-PLS；H-氨基酸CARS-SVM；I-氨基酸SPA-RF；J-咖啡碱UVE-PLS；K-咖啡碱CARS-SVM；L-咖啡碱SPA-RF。
Figure 7. Distribution point diagram of best model test data of each component
表 1 各加工步骤的样品数据平均值
Table 1. Average value of sample data for each processing step
加工步骤 含水率（%） 茶多酚（%） 氨基酸（×10 μg·g−1） 咖啡碱（%） 茶鲜叶 79.28 23.16 1.86 3.45 摊晾1 h 73.17 24.85 2.12 3.97 摊晾2 h 72.34 26.06 2.49 4.21 摊晾3 h 73.19 24.17 2.16 4.21 摊晾4 h 71.83 22.18 2.19 3.80 杀青180 ℃ 59.23 25.58 1.22 3.55 杀青200 ℃ 57.38 26.80 1.50 4.07 杀青220 ℃ 53.31 24.54 1.39 3.53 杀青240 ℃ 52.78 24.56 1.47 3.49 杀青260 ℃ 48.12 24.24 1.81 3.69 杀青280 ℃ 44.16 25.34 2.00 3.70 揉捻20 min 51.01 22.39 2.45 3.75 揉捻40 min 50.77 21.66 2.33 3.40 揉捻60 min 51.63 22.24 2.50 3.59 滚筒做形 20.40 19.47 2.03 4.26 炒锅做形 15.45 25.15 2.19 3.24 干燥70 ℃ 23.14 2.08 3.57 干燥80 ℃ 21.81 2.08 3.62 干燥90 ℃ 22.88 2.14 3.54 干燥100 ℃ 22.53 2.12 3.38 
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表 2 训练集与测试集的生化成分统计分析
Table 2. Statistical Analysis of biochemical components of training set and test set
生化成分 光谱图片数量（张） 总样本（个） 训练样本（个） 预测样本（%） 最大值 最小值 均值 标准差 含水率 16 48 36 12 83.72 15 54.64 17.45 茶多酚 20 60 43 17 26.97 18.86 23.64 1.8 氨基酸 20 60 43 17 2.54 0.83 2.01 0.37 咖啡碱 20 65 50 15 5.01 2.84 3.7 0.34 注：咖啡碱测量时，为保障数据准确性，故多测量5组数据，一并用于建模。
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表 3 波段筛选结果
Table 3. Band screening results
生化成分 筛选方法 波长数目 波长范围 含水率 UVE 84 400~449,463~494,545~563,642~670,687~735,770~783,814~866,935~953,1001~1004 CARS 32 401,583~604,642~659,677~683,704~708,766~773,797~808,835~839,863~870,918 SPA 14 442,466,511,556,608,677,701,721,787,839,863,890,935,987 茶多酚 UVE 55 680~708,821~915,935~994 CARS 40 559,597~608,649~663,683~689,701~708,721~749,766~777,787~797,821~877,939~942,966~980,997~1004 SPA 16 404,494,532,639,690,711,725,746,770,801,856,873,894,921,949,973 咖啡碱 UVE 28 507,511,756~763,901~936,966~1004 CARS 18 432,452,459,521,590,621,628,783,787,821,835,842,887,894,973,984,987,1004 SPA 11 442,466,677,714,750,787,842,866,897,935,977 氨基酸 UVE 48 476~497,542~566,649~680,877~918,953~970 CARS 15 418~421,452~459,473,563,573,608~614,659~663,963,970 SPA 14 401,442,490,514,556,659,680,701,721,752,790,835,887,942
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表 4 模型结果汇总表
Table 4. Summary of model results
生化成分 模型 训练集 交互验证均方根误差
RMSECV预测集 均方根误差 RMSEC 相关系数 Rcal 均方根误差 RMSEP 相对分析误差 RPD 相关系数 Rp 决定系数 R2 含水率 UVE-PLS 0.0155 0.9983 0.5483 2.2716 7.3261 0.9914 0.98 CARS-SVM 2.0253 0.9941 1.0458 1.3819 11.4795 0.9959 0.99 UVE-RF 1.5934 0.9936 0.7615 1.4747 10.2093 0.9902 0.99 茶多酚 SPA-PLS 0.0929 0.9031 0.4427 0.6184 2.9274 0.9524 0.91 UVE-SVM 0.6206 0.9356 0.3701 0.5049 3.5851 0.9694 0.97 UVE-RF 0.6033 0.892 0.3598 0.7321 2.226 0.8814 0.88 氨基酸 CARS-PLS 0.0747 0.9405 0.0764 0.1382 2.6253 0.9264 0.86 CARS-SVM 0.1212 0.9478 0.0723 0.1347 2.5446 0.9247 0.92 SPA-RF 0.1148 0.9102 0.0684 0.1399 2.4942 0.8595 0.86 咖啡碱 UVE-PLS 0.0954 0.813 0.1197 0.1078 2.5284 0.9327 0.87 CARS-SVM 0.2389 0.7626 0.1379 0.1344 1.7255 0.9129 0.91 SPA-RF 0.2204 0.6448 0.1273 0.1522 1.3618 0.7826 0.78
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表 5 最佳建模结果
Table 5. The best results of modeling
生化成分 最优方法 R2 RMSECV RPD 含水率 UVE-RF 0.99 0.7615 10.2093 氨基酸 CARS-SVM 0.92 0.0723 2.5446 茶多酚 UVE-SVM 0.97 0.3701 3.5851 咖啡碱 UVE-PLS 0.87 0.1197 2.5284
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