School of Engineering, Anhui Agricultural University, Hefei 230036, China.
State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 May 5;252:119522. doi: 10.1016/j.saa.2021.119522. Epub 2021 Feb 2.
Keemun black tea is classified into 7 grades according to the difference in its quality. The appearance and flavour are crucial indicators of its quality. This research demonstrates a rapid grading method of jointly using near-infrared reflectance spectroscopy (NIRS) and computer vision systems (CVS) to evaluate the flavour and appearance quality of tea. A Bruker MPA Fourier Transform near-infrared spectrometer was used to record the spectrum of samples. A computer vision system was used to capture the image of tea leaves in an unobstructed manner. 80 tea samples for each grade were analyzed. The performance of four NIRS feature extraction methods (principal component analysis, local linear embedding, isometric feature mapping, and convolutional neural network (CNN)) was compared in this study. Histograms of six geometric features (leaf width, leaf length, leaf area, leaf perimeter, aspect ratio, and rectangularity) of different tea samples were used to describe their appearance. A feature-level fusion strategy was used to combine softmax and artificial neural networks (ANN) to classify NIRS and CVS features. The results indicated that for an individual NIRS signal, CNN achieved the highest classification accuracy with the softmax classification model. The histograms of the combined shape features indicated that when the softmax classification model was used, the classification accuracy was also higher than ANN. The fusion of NIRS and CVS features proved to be the optimal combination; the accuracy of calibration, validation and testing sets increased from 99.29%, 96.67% and 98.57% (when the optimal features from a single-sensor were used) to 100.00%, 99.29% and 100.00% (when features from multiple-sensors were used). This study revealed that the combination of NIRS and CVS features can be a useful strategy for classifying black tea samples of different grades.
祁门红茶按品质分为 7 个等级，其外观和滋味是衡量其品质的重要指标。本研究采用近红外漫反射光谱（NIRS）和计算机视觉系统（CVS）联合快速分级方法，评估茶叶的滋味和外观品质。采用 Bruker MPA 傅里叶变换近红外光谱仪记录样品光谱，计算机视觉系统无阻碍地获取茶叶图像。对每个等级的 80 个茶样进行分析。对比了主成分分析、局部线性嵌入、等度量特征映射和卷积神经网络（CNN）等 4 种 NIRS 特征提取方法的性能。使用不同茶样的 6 个几何特征（叶宽、叶长、叶面积、叶周长、长宽比和矩形度）的直方图来描述其外观。采用特征级融合策略，将 softmax 和人工神经网络（ANN）结合对 NIRS 和 CVS 特征进行分类。结果表明，对于单个 NIRS 信号，CNN 与 softmax 分类模型结合时分类准确率最高。组合形状特征的直方图表明，使用 softmax 分类模型时，分类准确率也高于 ANN。NIRS 和 CVS 特征的融合是最佳组合；校准集、验证集和测试集的准确率从使用单个传感器的最佳特征时的 99.29%、96.67%和 98.57%提高到使用多个传感器的特征时的 100.00%、99.29%和 100.00%。该研究表明，NIRS 和 CVS 特征的组合可以成为一种有用的策略，用于对不同等级的红茶样品进行分类。
