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基于光谱-空间特征的黄茶多酚含量估算模型
引用本文:杨宝华,高远,王梦玄,齐麟,宁井铭.基于光谱-空间特征的黄茶多酚含量估算模型[J].光谱学与光谱分析,2021,41(3):936-942.
作者姓名:杨宝华  高远  王梦玄  齐麟  宁井铭
作者单位:1. 安徽农业大学信息与计算机学院,安徽 合肥 230036
2. 安徽农业大学茶树生物与利用国家重点实验室,安徽 合肥 230036
基金项目:安徽省自然科学基金项目(1808085MF195);安徽省高校自然科学研究重点项目(KJ2016A837);安徽省科技重大专项(1803071149);农业部农业物联网技术集成与应用重点实验室开放基金项目(2016KL02)资助。
摘    要:茶多酚是黄茶中的重要成分之一,具有保健和药用功效。准确估测茶多酚含量对茶叶品质鉴定和定量分析具有重要的意义。学者们已经利用电子鼻、电子舌、高光谱和近红外技术开展了茶多酚的估测研究,取得了良好的效果。然而,由于缺乏空间特征,难以满足黄茶内外品质综合判断的要求。随着高光谱成像系统的发展,尽管基于灰度共生矩阵的茶叶成分估测已经被证实取得较好的效果,但在实际应用中仍然存在一些障碍。一方面,分辨率较低时,图像的纹理特征不会有显著差异,并且少数特征无法充分地解译高光谱图像,从而导致模型估测效果较差。另一方面,分辨率较高时,特征的增加会导致模型更复杂。因此,在保留高光谱图像原始信息的前提下,有必要进一步挖掘高光谱图像的潜在特征,尤其是纹理的细节部分。因此,提出了一种融合光谱和空间特征的模型来提高茶多酚估测的准确性。首先,利用连续小波变换提取光谱信息的小波系数;其次,根据不同尺度的小波系数能量优选小波系数特征,分别是第4尺度的959和1 561 nm,第5尺度的1 321,1 520和1 540 nm,以及第6尺度的1 202和1 228 nm;再者,基于小波系数能量之和优选2个特征波长,分别是1 102和1 309 nm;然后,根据特征波长对应的高光谱图像分别提取灰度共生矩阵和小波纹理。最后,分别利用小波系数特征、灰度共生矩阵、小波纹理和他们的组合构建黄茶多酚含量的估测模型。通过对五种黄茶的分析和验证,比较基于不同特征的不同模型估测效果,包括偏最小二乘回归、支持向量回归和随机森林方法。结果表明,融合小波系数特征,共生矩阵和小波纹理的支持向量回归模型效果最佳,校正集的R2为0.933 0,验证集的R2为0.823 8。因此,所提出的模型能有效的提高茶多酚含量的预测精度,为预测茶叶的其他成分提供了技术基础。

关 键 词:茶多酚  小波变换  小波纹理  高光谱图像  
收稿时间:2020-02-21

Estimation Model of Polyphenols Content in Yellow Tea Based on Spectral-Spatial Features
YANG Bao-hua,GAO Yuan,WANG Meng-xuan,QI Lin,NING Jing-ming.Estimation Model of Polyphenols Content in Yellow Tea Based on Spectral-Spatial Features[J].Spectroscopy and Spectral Analysis,2021,41(3):936-942.
Authors:YANG Bao-hua  GAO Yuan  WANG Meng-xuan  QI Lin  NING Jing-ming
Institution:1. School of Information and Computer, Anhui Agricultural University, Hefei 230036, China 2. State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
Abstract:Tea polyphenols(TP)is one of the important ingredients of yellow tea,which has health and medicinal effects.Moreover,accurate estimation of tea polyphenol content is of great significance for tea quality identification and quantitative analysis.Previous scholars have used E-nose,E-tongue,hyperspectral and near-infrared techniques to conduct research on the estimation of tea polyphenols,and they have achieved good results.However,due to the lack of spatial characteristics,it is difficult to meet the accuracy requirements for the comprehensive judgment of the internal and external quality of tea.With the development of hyperspectral imaging system(HIS),although the estimation of tea texture based on the gray level co-occurrence matrix(GLCM)has made progress,there are still some obstacles in practical application.On the one hand,if the resolution is low,there will be no significant difference in the texture features of the image,and fewer features will not be able to fully interpret the image,resulting in lower model accuracy.On the other hand,if the resolution is high,the increase of features will make the model more complicated.Therefore,on the premise of retaining the original information of the hyperspectral image,it is necessary to explore further the potential features of hyperspectral images,especially the details of the texture.Consequently,a method of combining spectral and spatial features is proposed to improve the accuracy of tea polyphenol estimation.First,the wavelet coefficients are extracted using continuous wavelet transform based on the spectral information obtained from the hyperspectral image.Second,the wavelet coefficient features are extracted based on the wavelet coefficients,including 959 and 1561 nm at the 4 th scale,1321,1520 and 1540 nm at the 5 th scale,and 1202 and 1228 nm at the 6 th scale.Furthermore,two characteristic wavelengths are preferred based on the sum of the energy of wavelet coefficients,which are 1102 and 1309 nm,respectively.Then,the gray level co-occurrence matrix and wavelet texture are extracted according to the hyperspectral image corresponding to the characteristic wavelength.Finally,the wavelet coefficient features,co-occurrence matrix,wavelet texture,and their combinations were used to construct an estimation model for the content of polyphenols in yellow tea.By comparing different regression methods based on different characteristics,including partial least squares regression(PLSR),support vector regression(SVR),and random forest(RF),five types of yellow tea were analyzed and verified.The experimental results show that SVR model based on the fusion of wavelet coefficient features,co-occurrence matrix texture,and wavelet texture achieves the best results with R2 of 0.9330 for calibration set and 0.8238 for the validation set.Therefore,the proposed model can effectively improve the prediction accuracy of tea polyphenol content,which also provide a technical basis for predicting other components of tea.
Keywords:Tea polyphenols  Wavelet transform  Wavelet texture  Hyperspectral image
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