可见/短波与长波近红外光谱联用的茶树识别及茶鲜叶茶多酚含量快速检测方法

doi:10.12133/j.smartag.SA202505034

Smart Agriculture

• •

可见/短波与长波近红外光谱联用的茶树识别及茶鲜叶茶多酚含量快速检测方法

许金钗¹^,²^,³(), 李晓丽⁴, 翁海勇¹^,²^,³, 何勇⁴, 朱雪松⁵, 刘鸿飞⁶, 黄镇雄¹^,²^,³(), 叶大鹏¹^,²^,³

^1. 福建农林大学机电工程学院，福建福州 350002，中国
^2. 福建农林大学未来技术学院/海峡联合研究院，福建福州 350002，中国
^3. 福建省农业信息感知技术重点实验室，福建福州 350002，中国
^4. 浙江大学生物系统工程与食品科学学院，浙江杭州 310058，中国
^5. 轻工业杭州机电设计研究院有限公司，浙江杭州 311121，中国
^6. 奥谱天成（厦门）光电有限公司，福建厦门 361021，中国

收稿日期:2025-05-30 出版日期:2025-08-12
基金项目:
国家自然科学基金面上项目(31771676); 农机智能控制与制造技术福建省高校重点实验室（武夷学院）开放基金项目(AMICM202402)
作者简介:
许金钗，博士研究生，研究方向为农业人工智能。E-mail： xjc@fafu.edu.cn
通信作者:
黄镇雄，博士，副教授，研究方向为农业电气化与自动化。E-mail： hzxiong@fafu.edu.cn

Rapid Tea Identification and Polyphenol Detection in Fresh Tea Leaves Using Visible/Shortwave and Longwave Near-Infrared Spectroscopy

XU Jinchai¹^,²^,³(), LI Xiaoli⁴, WENG Haiyong¹^,²^,³, HE Yong⁴, ZHU Xuesong⁵, LIU Hongfei⁶, HUANG Zhenxiong¹^,²^,³(), YE Dapeng¹^,²^,³

^1. College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
^2. School of Future Technology Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China
^3. Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou 350002, China
^4. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
^5. HMEI Mechanery and Engineering Co. , Ltd. , Hangzhou 311121, China
^6. Optosky Co. , Ltd. , Xiamen 361021, China

Received:2025-05-30 Online:2025-08-12
Foundation items:National Natural Science Foundation of China (General Program)(31771676); Open Fund Project of Fujian Provincial University Key Laboratory of Agricultural Machinery Intelligent Control and Manufacturing Technology (Wuyi University)(AMICM202402)
About author:
XU Jinchai, E-mail: xjc@fafu.edu.cn
Corresponding author:
HUANG Zhenxiong, E-mail: hzxiong@fafu.edu.cn

摘要/Abstract

摘要：

【目的/意义】 茶多酚是衡量茶叶品质的关键指标，实现茶鲜叶茶多酚含量快速无损检测对于保障茶叶品质至关重要。同时，实现不同茶叶品种和叶位的快速识别，能够有效指导茶叶生产。 【方法】 本研究联用可见/短波近红外光谱（400~1 050 nm）与长波近红外光谱（1 051~1 650 nm）技术研制一台茶鲜叶品质成分快速无损检测装置，采用多源数据融合（数据级和特征级融合），以及机器学习算法，构建了不同茶叶品种、叶位和茶多酚含量快速检测模型。 【结果与讨论】 实验结果表明，不同茶树品种或不同叶位的茶多酚含量存在显著差异。相较于单一数据源，基于数据融合所建立的模型能有效提高预测性能，其中经过Savitzky-Golay卷积平滑预处理后结合特征级融合方法建立的偏最小二乘法判别分析模型（Partial Least Squares Discriminant Analysis, PLS-DA）对3个茶树品种和4个叶位识别的预测集准确率分别达到100%和87.93%。此外，基于数据级融合的一维卷积神经网络模型（One-Dimensional Convolutional Neural Network, 1D-CNN）对茶鲜叶茶多酚含量的预测决定系数（Predicted Coefficient of Determination, R²_P）、预测均方根误差（Root Mean Square Error of Prediction, RMSEP）和残差预测偏差（Residual Predictive Deviation, RPD）分别为0.802 0、0.636 8%和2.268 4，优于仅采用可见/短波近红外光谱和长波近红外光谱。 【结论】 该检测装置能够实现茶鲜叶茶多酚含量的快速检测，也能有效识别茶叶品种和叶位，为多源数据融合技术应用于指导茶叶生产加工提供新思路。

关键词: 茶鲜叶, 茶多酚, 无损检测, 数据融合, 一维卷积神经网络（1D-CNN）

Abstract:

[Objective] Tea polyphenols, as a key indicator for evaluating tea quality, possess significant health benefits. Traditional detection methods are limited by poor timeliness, high cost, and destructive sampling, making them difficult to meet the demands of tea cultivar breeding and real-time monitoring of tea quality. Meanwhile, rapid identification of tea cultivars and leaf positions is critical for guiding tea production. Therefore, this study aims to develop a non-destructive detection device for quality components of fresh tea leaves based on the combined technology of visible/short-wave near-infrared and long-wave near-infrared spectroscopy, to realize rapid non-destructive detection of tea polyphenol content and rapid identification of tea cultivars and leaf positions. [Methods] A rapid non-destructive detection device for quality components of fresh tea leaves was developed by combining visible/short-wave near-infrared spectroscopy (400-1 050 nm) and long-wave near-infrared spectroscopy (1 051-1 650 nm). The Savitzky-Golay (SG) convolution smoothing method was used for preprocessing the spectral data. The Folin-Ciocalteu method was employed to determine the tea polyphenol content, and abnormal samples were eliminated using the interquartile range (IQR) method. Data-level and feature-level fusion methods were adopted, with the competitive adaptive reweighted sampling (CARS) algorithm used to extract characteristic wavelengths. Prior to modeling, the Kennard-Stone algorithm was applied to partition the dataset into a training set and a prediction set at a ratio of 4:1. Models such as principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), least squares support vector machine (LS-SVM), extreme learning machine (ELM), and 1D convolutional neural network (1D-CNN) were constructed for the identification of 3 cultivars (Huangdan, Tieguanyin, and Benshan) and 4 leaf positions. For predicting tea polyphenol content, models including partial least squares regression (PLSR), least squares support vector regression (LS-SVR), ELM, and 1D-CNN were established for predicting the tea polyphenol content in fresh tea leaves. [Results and Discussions] The results showed that there were significant differences in tea polyphenol contents among different cultivars and leaf positions (P<0.05). Specifically, the tea polyphenol content of Huangdan was (17.54±1.82%), which was 1.16 times and 1.04 times that of Tieguanyin (15.04±1.22%) and Benshan (16.81±1.24%), respectively. For each cultivar, the tea polyphenol content generally showed a decreasing trend from the 1st to 4th leaf position, with the highest content in the 1st leaf position. Principal component analysis (PCA) revealed that for cultivar identification, the scatter distribution of the principal components of Huangdan, Tieguanyin, and Benshan, as well as their projections in the directions of PC1 and PC2, shows a clear trend of clustering into three groups, indicating a good classification effect, although there was still some overlap among individual samples. For leaf position identification, the scatter distributions of the principal components of the 1st, 2nd, 3rd, and 4th leaf positions overlapped with each other, with no obvious clustering among leaf positions. Compared with single-source data, models based on data fusion effectively improved prediction performance. Among them, the PLS-DA model established by combining SG preprocessing with feature-level fusion achieved prediction accuracies of 100% and 87.93% for the identification of 3 tea cultivars and 4 leaf positions, respectively. Furthermore, the 1D-CNN model based on data-level fusion exhibited superior performance in predicting tea polyphenol content, with a coefficient of determination (R²_P), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) of 0.802 0, 0.636 8%, and 2.268 4, respectively, which outperformed models using only visible/short-wave near-infrared spectroscopy or long-wave near-infrared spectroscopy. [Conclusions] The developed detection device combining visible/short-wave near-infrared and long-wave near-infrared spectroscopy, mainly composed of spectrometers, Y-type optical fibers, plant probes, polymer lithium batteries, DC uninterruptible power supplies, voltage conversion modules, and aluminum alloy casings, could synchronously collect multi-source spectral data of visible/short-wave near-infrared and long-wave near-infrared from fresh tea leaves. Combined with data fusion methods and machine learning algorithms, it enabled rapid detection of tea polyphenol content and efficient identification of cultivars and leaf positions in fresh tea leaves, providing new insights for the application of multi-source data fusion technology in elite tea cultivar breeding and non-destructive detection of fresh tea leaf quality.

Key words: Fresh tea leaves, tea polyphenols, non-destructive detection, data fusion, one-dimensional convolutional neural network (1D-CNN)

中图分类号:

许金钗, 李晓丽, 翁海勇, 何勇, 朱雪松, 刘鸿飞, 黄镇雄, 叶大鹏. 可见/短波与长波近红外光谱联用的茶树识别及茶鲜叶茶多酚含量快速检测方法[J]. 智慧农业(中英文), doi: 10.12133/j.smartag.SA202505034.

XU Jinchai, LI Xiaoli, WENG Haiyong, HE Yong, ZHU Xuesong, LIU Hongfei, HUANG Zhenxiong, YE Dapeng. Rapid Tea Identification and Polyphenol Detection in Fresh Tea Leaves Using Visible/Shortwave and Longwave Near-Infrared Spectroscopy[J]. Smart Agriculture, doi: 10.12133/j.smartag.SA202505034.

图/表 13

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表1

单一数据源和多源数据融合的茶树品种、叶位识别模型性能比较

类别	识别模型	输入变量	变量数	训练集				预测集
类别	识别模型	输入变量	变量数	准确率/%	精确率%	召回率/%	F ₁分数	准确率/%	精确率%	召回率/%	F ₁分数
3个品种	PLS-DA	可见/短波近红外	651	100.00	100.00	100.00	1.000 0	100.00	100.00	100.00	1.000 0
		长波近红外	600	87.83	87.94	88.17	0.879 9	94.83	94.11	93.70	0.938 5
		数据级数据融合	1 251	100.00	100.00	100.00	1.000 0	100.00	100.00	100.00	1.000 0
		特征级数据融合	171	99.13	99.12	99.10	0.991 1	100.00	100.00	100.00	1.000 0
	LS-SVM	可见/短波近红外	651	99.57	99.55	99.55	0.995 5	100.00	100.00	100.00	1.000 0
		长波近红外	600	96.52	96.66	96.60	0.966 1	100.00	100.00	100.00	1.000 0
		数据级数据融合	1 251	99.57	99.58	99.54	0.995 6	100.00	100.00	100.00	1.000 0
		特征级数据融合	171	98.26	98.29	98.22	0.982 1	100.00	100.00	100.00	1.000 0
	ELM	可见/短波近红外	651	100.00	100.00	100.00	1.000 0	100.00	100.00	100.00	1.000 0
		长波近红外	600	100.00	100.00	100.00	1.000 0	100.00	100.00	100.00	1.000 0
		数据级数据融合	1 251	100.00	100.00	100.00	1.000 0	100.00	100.00	100.00	1.000 0
		特征级数据融合	171	98.70	98.69	98.69	0.986 9	100.00	100.00	100.00	1.000 0
	1D-CNN	可见/短波近红外	651	100.00	100.00	100.00	1.000 0	100.00	100.00	100.00	1.000 0
		长波近红外	600	93.91	93.99	94.02	0.939 8	98.28	98.15	97.92	0.979 7
		数据级数据融合	1 251	100.00	100.00	100.00	1.000 0	100.00	100.00	100.00	1.000 0
		特征级数据融合	171	100.00	100.00	100.00	1.000 0	100.00	100.00	100.00	1.000 0
4个叶位	PLS-DA	可见/短波近红外	651	90.87	91.24	90.93	0.909 8	79.31	82.39	79.76	0.804 2
		长波近红外	600	64.35	64.92	64.39	0.644 8	68.97	69.02	69.74	0.689 7
		数据级数据融合	1 251	94.35	94.45	94.40	0.943 9	84.48	86.06	86.87	0.858 2
		特征级数据融合	90	85.65	85.45	85.23	0.853 2	87.93	91.18	88.51	0.889 3
	LS-SVM	可见/短波近红外	651	73.91	74.08	74.31	0.734 1	58.62	58.41	61.62	0.577 0
		长波近红外	600	56.09	56.00	56.57	0.560 9	58.62	58.86	59.93	0.592 1
		数据级数据融合	1 251	80.87	81.00	81.33	0.805 2	74.14	73.42	72.00	0.722 7
		特征级数据融合	90	96.52	96.38	96.46	0.964 1	79.31	81.55	78.57	0.789 3
	ELM	可见/短波近红外	651	95.22	95.24	95.48	0.953 6	75.86	75.45	76.43	0.759 3
		长波近红外	600	65.65	65.57	65.07	0.653 2	67.24	68.20	71.36	0.697 5
		数据级数据融合	1 251	79.57	79.49	79.43	0.794 6	79.31	80.33	78.99	0.796 5
		特征级数据融合	90	86.09	85.64	85.97	0.858 1	74.14	79.29	76.95	0.781 0
	1D-CNN	可见/短波近红外	651	79.57	79.64	79.47	0.794 5	68.97	68.23	69.10	0.684 6
		长波近红外	600	62.17	62.29	63.57	0.630 0	63.79	63.29	64.64	0.638 2
		数据级数据融合	1 251	87.83	87.96	88.28	0.876 2	74.14	75.09	73.62	0.735 8
		特征级数据融合	90	97.39	97.37	97.34	0.973 3	82.76	84.32	86.06	0.842 1

表1

图9

表2

表3

图10

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输入变量	数据集	样本数/个	最小值/%	最大值/%	平均值/%	标准差/%	变异系数/%	偏度	峰度
可见/短波近红外（400~1 050 nm）	全集	276	12.71	20.60	16.41	1.60	9.80	0.019	-0.450
	训练集	221	12.71	20.60	16.44	1.62	9.86	0.036	-0.472
	预测集	55	13.13	19.58	16.25	1.55	9.59	-0.089	-0.352
长波近红外（1 051~1 650 nm）	全集	276	12.71	20.60	16.41	1.60	9.80	0.019	-0.450
	训练集	221	12.71	20.60	16.47	1.67	10.17	0.020	-0.571
	预测集	55	13.18	18.36	16.16	1.28	7.97	-0.402	-0.273
数据级数据融合	全集	276	12.71	20.60	16.41	1.60	9.80	0.019	-0.450
	训练集	221	12.71	20.60	16.48	1.64	9.95	0.037	-0.472
	预测集	55	13.18	18.72	16.09	1.44	8.97	-0.288	-0.748
特征级数据融合	全集	276	12.71	20.60	16.41	1.60	9.80	0.019	-0.450
	训练集	221	12.71	20.60	16.51	1.62	9.82	0.064	-0.484
	预测集	55	13.13	18.29	15.98	1.49	9.34	-0.372	-0.855

定量模型	输入变量	变量数/个	R ² _C	RMSEC/%	R ² _P	RMSEP/%	RPD
PLSR	可见/短波近红外	651	0.729 5	0.841 5	0.787 2	0.712 3	2.167 8
	长波近红外	600	0.461 4	1.227 0	0.465 6	0.933 1	1.368 0
	数据级数据融合	1 251	0.743 7	0.828 5	0.789 2	0.657 0	2.178 5
	特征级数据融合	106	0.700 1	0.886 2	0.770 2	0.708 9	2.086 3
LS-SVR	可见/短波近红外	651	0.650 4	0.956 7	0.738 3	0.789 8	1.955 0
	长波近红外	600	0.550 4	1.121 0	0.587 0	0.820 3	1.556 1
	数据级数据融合	1 251	0.741 2	0.832 5	0.751 3	0.713 7	2.005 5
	特征级数据融合	106	0.755 7	0.799 9	0.743 6	0.748 7	1.975 2
ELM	可见/短波近红外	651	0.637 7	0.973 9	0.745 6	0.778 7	1.982 8
	长波近红外	600	0.633 4	1.012 2	0.664 5	0.739 4	1.726 6
	数据级数据融合	1 251	0.647 3	0.971 9	0.753 2	0.710 9	2.013 3
	特征级数据融合	106	0.768 3	0.778 9	0.787 0	0.682 5	2.166 8
1D-CNN	可见/短波近红外	651	0.836 3	0.654 5	0.724 2	0.810 8	1.921 9
	长波近红外	600	0.534 0	1.141 2	0.622 0	0.784 4	1.642 4
	数据级数据融合	1 251	0.842 5	0.649 4	0.802 0	0.636 8	2.268 4
	特征级数据融合	106	0.819 6	0.687 3	0.733 4	0.763 5	1.954 8

可见/短波与长波近红外光谱联用的茶树识别及茶鲜叶茶多酚含量快速检测方法

Rapid Tea Identification and Polyphenol Detection in Fresh Tea Leaves Using Visible/Shortwave and Longwave Near-Infrared Spectroscopy

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