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| 基于光谱反射率的茶染宣纸茶水浓度预测 |
| Spectral reflectivity based tea concentration prediction for tea dyeing of rice paper |
| 投稿时间：2022-11-28 |
| DOI：10.3969/j.issn.1005-5630.2023.004.010 |
| 中文关键词: 光谱反射率 茶染 偏最小二乘法 BP神经网络 连续投影算法 |
| 英文关键词:spectral reflectivity tea dyeing partial least squares BP neural networks continuous projection algorithm |
| 基金项目:高等学校教学研究项目(DWJZW202142xn，DWJZW202235xn)；国家级大学生创新训练计划（202210681019） |
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| 中文摘要: |
| 茶染作为植物染色的一大门类，同时具有良好的环保性能和深厚的文化底蕴。为了能够准确描述茶叶染色的光谱变化，本文研究茶染后宣纸的光谱反射率与茶叶浓度的关系。首先采用分光光度计测量400～700 nm波段被茶叶染色后宣纸的光谱反射率，分别基于偏最小二乘回归模型、BP神经网络和连续投影算法(SPA)选择特征波段建立光谱信息与茶叶浓度之间关系的预测模型。然后以光谱反射率作为输入变量，对茶叶浓度进行预测。结果表明：基于偏最小二乘法、BP神经网络和连续投影算法 选择特征波段建立模型，通过茶染宣纸的光谱反射率来预测茶叶浓度具有较高的稳健性和可信度，其中SPA-BP神经网络模型的效果最优，平均预测正确率为98.40%，决定系数为0.9910，均方根误差为0.8433。这说明通过茶染宣纸的光谱数据来预测茶叶浓度具有可行性。 |
| 英文摘要: |
| As a major category of plant dyeing, tea dyeing has a deep cultural heritage while having good environmental protection performance. In order to accurately describe the spectral changes of tea staining, this work studied the relationship between the spectral reflectance of rice paper dyed with tea and the tea concentration. First, a spectrophotometer was used to measure the spectral reflectance of rice paper in the 400 to 700 nm band which was stained by tea leaves. Prediction models were constructed by the spectral reflectance of rice paper and tea concentration based on the partial least squares regression model, BP neural network and continuous projection algorithm(SPA) selected feature band, respectively. Then the spectral reflectance was used as an input variable to predict the tea concentration. The results show that the partial least squares method, BP neural network and continuous projection algorithm select characteristic bands to establish a model to predict tea concentration through the spectral reflectance of tea dyed rice paper, which has high robustness and reliability. SPA-BP neural network model has the best performance: the average prediction accuracy rate is 98.40%, the coefficient of determination is 0.9910, and the root mean square error is 0.843 3. This shows that it is feasible to predict tea concentration through spectral data of tea dyed rice paper. |
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