Black tea stored for years might be adulterated as fresh tea for sell. Near-infrared hyperspectral imaging coupled with machine learning methods was applied for rapid detection of black tea storage years. Black tea samples produced in the years of 2016, 2017, 2018 and 2019 (storage for 3, 2, 1 and 0 years) were studied. Principal component analysis (PCA) was used to form score images to qualitatively visualize the differences of tea samples stored for different years. Loadings of each principal component were used to identify optimal wavelengths. Based on the full range spectra and the optimal wavelengths, conventional machine learning methods (logistic regression (LR), support vector machine (SVM)) and deep learning methods (convolutional neural network (CNN), long short-term memory (LSTM) and CNN-LSTM) were used to establish classification models. Classification models using full spectra and optimal wavelengths obtained close results. Deep learning methods obtain better results. Black tea samples stored for 1 and 2 years were more likely to be misclassified. Fresh tea samples can be well identified from the stored samples. The overall results illustrated the feasibility to identify the storage year of black tea with machine learning methods, proving an efficient alternative for black tea quality inspection.

储存多年的红茶可能会掺假成新鲜茶出售。近红外高光谱成像结合机器学习方法被用于快速检测红茶的储存年限。研究了2016年，2017年，2018年和2019年生产的红茶样品（存储3年，2年，1年和0年）。主成分分析（PCA）用于形成评分图像，以定性地可视化存储不同年份的茶样品的差异。每个主要成分的载荷用于确定最佳波长。基于全范围光谱和最佳波长，常规的机器学习方法（逻辑回归（LR），支持向量机（SVM））和深度学习方法（卷积神经网络（CNN），长短期记忆（LSTM）和CNN-LSTM）用于建立分类模型。使用全光谱和最佳波长的分类模型获得了接近的结果。深度学习方法可获得更好的结果。储存1年和2年的红茶样品更容易被误分类。可以从储存的样品中很好地识别新鲜茶样品。总体结果说明了使用机器学习方法确定红茶储存年限的可行性，证明了红茶质量检查的有效替代方法。