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

Mishra, Puneet, Nordon, Alison, Tschannerl, Julius, Lian, Guoping, Redfern, Sally and Marshall, Stephen (2018) Near-infrared hyperspectral imaging for non-destructive classification of commercial tea products Journal of Food Engineering, 238. pp. 70-77.

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Abstract

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 multiclass 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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Chemical and Process Engineering
Authors :
NameEmailORCID
Mishra, Puneet
Nordon, Alison
Tschannerl, Julius
Lian, Guopingg.lian@surrey.ac.uk
Redfern, Sally
Marshall, Stephen
Date : December 2018
Funders : The European Union's Horizon 2020 research and innovation programme
DOI : 10.1016/j.jfoodeng.2018.06.015
Grant Title : The Marie Sklodowska-Curie grant
Copyright Disclaimer : © 2018 Elsevier Ltd. All rights reserved.
Uncontrolled Keywords : Imaging spectroscopy; Hypercube; Multivariate; Data visualisation; Neighbourhood methods
Additional Information : This work has received funding from the European Union's Horizon 2020 research and innovation programme named ’MODLIFE’(Advancing Modelling for Process-Product Innovation, Optimization,Monitoring and Control in Life Science Industries) under the Marie Sklodowska-Curie grant agreement number 675251.
Depositing User : Diane Maxfield
Date Deposited : 09 Sep 2019 11:08
Last Modified : 09 Sep 2019 11:09
URI: http://epubs.surrey.ac.uk/id/eprint/852565

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