Near-infrared hyperspectral imaging for non-destructive classification of commercial tea products
Near-infrared hyperspectral imaging for non-destructive classification of commercial tea products
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DOI：
10.1016/j.jfoodeng.2018.06.015
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发表时间：
2018-12-01
影响因子：
5.5
通讯作者：
Marshall, Stephen
中科院分区：
文献类型：
--
作者：
Mishra, Puneet;Nordon, Alison;Marshall, Stephen
茶是世界上消费最多的饮料。近年来，高效液相色谱等各种高端分析技术被用于茶叶产品的分析。然而，这些技术需要复杂的样品制备，耗时，昂贵，并且需要熟练的分析人员进行实验。因此，为了支持茶叶产品的快速和无损评价，使用近红外（NIR） （950-1760 nm）高光谱成像（HSI）对乌龙茶、绿茶、黄茶、白茶、黑茶和普洱茶六种不同的商品茶产品进行分类。为了可视化HSI数据，比较了线性（主成分分析（PCA）和多维标度（MDS））和非线性（t分布随机邻居嵌入（t-SNE）和等距映射（ISOMAP））数据可视化方法。t-SNE根据加工程度将六种商业茶产品分为三组：最低加工、氧化和发酵。为了对不同茶叶产品进行分类，提出了一种包含支持向量机二值学习器的多类纠错输出码模型。利用分类模型对HSI超立方体中像素进行分类预测，得到分类图。SVM-ECOC模型对6种商品茶叶的分类准确率为97.41 +/- 0.16%。所开发的方法提供了一种对茶叶产品进行快速、无损的原位检测的手段，这将对过程监测、质量控制、真实性和掺假检测有相当大的好处。
Tea is the most consumed manufactured drink in the world. In recent years, various high end analytical techniques such as high-performance liquid chromatography have been used to analyse tea products. However, these techniques require complex sample preparation, are time consuming, expensive and require a skilled analyst to carry out the experiments. Therefore, to support rapid and non-destructive assessment of tea products, the use of near infrared (NIR) (950-1760 nm) hyperspectral imaging (HSI) for classification of six different commercial tea products (oolong, green, yellow, white, black and Pu-erh) is presented. To visualise the HSI data, linear (principal component analysis (PCA) and multidimensional scaling (MDS)) and non-linear (t-distributed stochastic neighbour embedding (t-SNE) and isometric mapping (ISOMAP)) data visualisation methods were compared. t-SNE provided separation of the six commercial tea products into three groups based on the extent of processing: minimally processed, oxidised and fermented. To perform the classification of different tea products, a multi-class error-correcting output code (ECOC) model containing support vector machine (SVM) binary learners was developed. The classification model was further used to predict classes for pixels in the HSI hypercube to obtain the classification maps. The SVM-ECOC model provided a classification accuracy of 97.41 +/- 0.16% for the six commercial tea products. The methodology developed provides a means for rapid, non-destructive, in situ testing of tea products, which would be of considerable benefit for process monitoring, quality control, authenticity and adulteration detection.