基于图像和光谱信息融合的红茶萎凋程度量化判别

为了实现对红茶萎凋程度量化判别,该研究提出了一种将图像和光谱信息融合后分别与线性判别分析法和偏最小二乘法结合的技术,进行工夫红茶萎凋程度定性判别及儿茶素与氨基酸比值定量预测研究。通过对图像进行主成分分析,筛选出5个特征波长和对应的光谱特征值,基于灰度共生矩阵提取5个特征波长图像的纹理特征值,并采用连续投影算法优选出14个纹理特征值,然后分别以光谱和纹理特征值融合数据建立红茶萎凋程度的线性判别模型和儿茶素与氨基酸比值的偏最小二乘预测模型。结果表明：采用所研究的方法和建立的模型对工夫红茶萎凋程度判别准确率达到94.64%,儿茶素与氨基酸比值预测相关系数为0.8765,预测均方根误差为0.434,预...

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Bibliographic Details
Published in农业工程学报 Vol. 32; no. 24; pp. 303 - 308
Main Author 宁井铭 孙京京 朱小元 李姝寰 张正竹 黄财旺
Format Journal Article
LanguageChinese
Published 安徽农业大学茶树生物学与资源利用国家重点实验室,合肥,230036%安徽祁门金东茶厂,祁门,245600 2016
Subjects
偏最小二乘法
儿茶素与氨基酸比值
判别分析方法
图像分析
数据融合
红茶
萎凋
data fusion
discriminant analysis
萎凋
image analysis
偏最小二乘法
withering
数据融合
红茶
儿茶素与氨基酸比值
判别分析方法
图像分析
partial least squares approximations
black tea
ratio of catechins to amino acids
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ISSN1002-6819
DOI10.11975/j.issn.1002-6819.2016.24.041

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Summary:为了实现对红茶萎凋程度量化判别,该研究提出了一种将图像和光谱信息融合后分别与线性判别分析法和偏最小二乘法结合的技术,进行工夫红茶萎凋程度定性判别及儿茶素与氨基酸比值定量预测研究。通过对图像进行主成分分析,筛选出5个特征波长和对应的光谱特征值,基于灰度共生矩阵提取5个特征波长图像的纹理特征值,并采用连续投影算法优选出14个纹理特征值,然后分别以光谱和纹理特征值融合数据建立红茶萎凋程度的线性判别模型和儿茶素与氨基酸比值的偏最小二乘预测模型。结果表明：采用所研究的方法和建立的模型对工夫红茶萎凋程度判别准确率达到94.64%,儿茶素与氨基酸比值预测相关系数为0.8765,预测均方根误差为0.434,预测结果较好。证明应用这两种方法能实现对红茶萎凋程度量化判别。
Bibliography:11-2047/S
Withering is the first procedure and the key step in processing of black tea.It is crucial for the quality of black tea product.Usually,the judgment of the withering degree relies on the processor＇s judgment,rather than a quantitative analysis by fast evaluation method.In order to develop the digitized discrimination on withering degrees,different degrees of withering samples were collected in our research.In this study,168 samples provided by Jindong tea factory in Qimen County were investigated.All of the samples belonged to different withering degrees（55 samples of mild withering,61 samples of moderate withering and 52 samples of excessive withering）.The samples were randomly divided into two subsets at the ratio of 2：1.112 samples were chosen as the calibration set and the remaining 56 samples were prediction set.The calibration set was used to develop the model,while the prediction set was applied to test the robustness of the model.The withering degree was nondestructively evaluated by hyperspe
ISSN:1002-6819
DOI:10.11975/j.issn.1002-6819.2016.24.041