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首页> 外文期刊>Journal of Food Measurement and Characterization >Potential of hyperspectral imaging for rapid identification of true and false honeysuckle tea leaves
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Potential of hyperspectral imaging for rapid identification of true and false honeysuckle tea leaves

机译：高光谱成像的潜力，用于快速识别真假储藏茶叶

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摘要

Honeysuckle (Lonicerae japonicae Flos, LJF) tea is a favorite cool tea in China and Southeast Asia. However, some unscrupulous traders usually use Lonicera Flos (LF) as LJF to sell for earning high profit. In order to identify true and false honeysuckle tea leaves rapidly and precisely, hyperspectral imaging technology was applied to develop a nondestructive identification model for LJF and LF. Firstly, the original spectral data were analyzed by three pretreatment methods including Savitzky-Golay (SG) convolution smoothing, multiple scatter correct and standard normal variate transformation (SNV). Then, a full-band analysis model was established by using the partial least squares-discriminant analysis method. And after the selection of characteristic wavelengths by regression coefficients algorithm, the identification analysis models based on the back-propagation neural network and extreme learning machine (ELM) discriminant were established. The results showed that the BP neural network and ELM discriminant analysis model based on SNV denoising at 9 characteristic wavelengths could achieve the best identification results. The recognition rates of both modeling sets and forecasting sets could reach 100%. Therefore, the application of hyperspectral imaging technology can identify LJF and LF effectively and nondestructively, and has potential in the identification of true and false honeysuckle tea leaves.
机译：金银花（Liconeraeaponicae flos，LJF）茶是中国和东南亚最受欢迎的茶。然而，一些肆无忌惮的交易员通常使用Lonicera Flos（LF）作为LJF销售赚取高利润。为了迅速呈现真正和假金银花茶叶，应用了高光谱成像技术，为LJF和LF开发非破坏性识别模型。首先，通过三种预处理方法分析了原始光谱数据，包括Savitzky-Golay（SG）卷积平滑，多次散射正确和标准正常变换（SNV）。然后，通过使用局部最小二乘判别分析方法建立全带分析模型。并且在回归系数算法选择特征波长之后，建立了基于背部传播神经网络和极端学习机（ELM）判别的识别分析模型。结果表明，基于9个特征波长的SNV去噪的BP神经网络和榆树判别分析模型可以实现最佳识别结果。建模集和预测集的识别率可以达到100％。因此，高光谱成像技术的应用可以有效地识别LJF和LF，并且在识别真假和假金银花茶叶中具有潜力。

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