Near-infrared hyperspectral imaging for non-destructive classification of commercial tea products

Puneet Mishra, Alison Nordon, Julius Tschannerl, Guoping Lian, Sally Redfern, Stephen Marshall

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)
9 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)70-77
Number of pages8
JournalJournal of Food Engineering
Early online date13 Jun 2018
Publication statusPublished - 1 Dec 2018


  • imaging spectroscopy
  • hypercube
  • multivariate
  • data visualisation
  • neighbourhood methods

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