State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, PR China.
State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, Anhui, PR China.
Spectrochim Acta A Mol Biomol Spectrosc. 2020 Apr 5;230:118079. doi: 10.1016/j.saa.2020.118079. Epub 2020 Jan 17.
From the perspective of combating fraud issues and examining keemun black tea properties, there was a contemporary urgent demand for a keemun black tea rankings identification system. Current rapid evaluation systems had been mainly developed for green tea grade evaluation, but there was space for improvement to establish a highly robust model. The present study proposed cognitive spectroscopy that combined near infrared spectroscopy (NIRS) with multivariate calibration and feature variable selection methods. We defined "cognitive spectroscopy" as a protocol that selects characteristic information from complex spectral data and showed optimal results without human intervention. 700 samples representing keemun black tea from seven quality levels were scanned applying an NIR sensor. To differentiate which wavelength variables of the acquired NIRS data carry key and feature information regarding keemun black tea grades, there were four different variables screening approaches, namely genetic algorithm (GA), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and shuffled frog leaping algorithm (SFLA), were compared in this study. Cognitive models were developed using least squares support vector machine (LSSVM), back propagation neural network (BPNN) and random forest (RF) methods combined with the optimized characteristic variables from the above variables selection algorithms for the identification of keemun black tea rank quality. Experimental results showed that all cognitive models utilizing the SFLA approach achieved steady predictive results based on eight latent variables and selected thirteen characteristic wavelength variables. The CARS-LSSVM model with the best predictive performance was proposed based on selecting ten characteristic latent variables, and the best performance indicators of the model were as follows: the root mean square error of prediction (RMSEP) was 0.0413, the correlation coefficients of prediction set (R) was 0.9884, and the correct discriminant rate (CDR) was 99.01% in the validation process. This study demonstrated that cognitive spectroscopy represented a proper strategy for the highly identification of quality rankings of keemun black tea.
从打击欺诈问题和检验祁门红茶特性的角度来看，迫切需要建立一个祁门红茶等级鉴定系统。目前的快速评价体系主要是为绿茶等级评价而开发的，但仍有改进空间，以建立一个高度稳健的模型。本研究提出了认知光谱学，它将近红外光谱（NIRS）与多元校准和特征变量选择方法相结合。我们将“认知光谱学”定义为一种从复杂光谱数据中选择特征信息的协议，无需人工干预即可获得最佳结果。应用 NIR 传感器扫描了 700 个代表七个质量水平的祁门红茶样品。为了区分获得的 NIRS 数据中的哪些波长变量携带有关祁门红茶等级的关键和特征信息，本研究比较了四种不同的变量筛选方法，即遗传算法（GA）、连续投影算法（SPA）、竞争自适应重加权采样（CARS）和蛙跳算法（SFLA）。使用最小二乘支持向量机（LSSVM）、反向传播神经网络（BPNN）和随机森林（RF）方法结合上述变量选择算法优化的特征变量，建立了认知模型，用于鉴定祁门红茶等级质量。实验结果表明，所有利用 SFLA 方法的认知模型基于 8 个潜在变量和选择的 13 个特征波长变量都取得了稳定的预测结果。基于选择 10 个特征潜在变量，提出了 CARS-LSSVM 模型，该模型具有最佳的预测性能，其模型的最佳性能指标如下：预测集的均方根误差（RMSEP）为 0.0413，预测集的相关系数（R）为 0.9884，验证过程中的正确判别率（CDR）为 99.01%。本研究表明，认知光谱学代表了一种对祁门红茶质量等级进行高度识别的合适策略。
