Near-infrared hyperspectral imaging for non-destructive classification of commercial tea products. (December 2018)

• Record Type: Journal Article
• Authors: Mishra, Puneet; Nordon, Alison; Tschannerl, Julius; Lian, Guoping; Redfern, Sally; Marshall, Stephen
• Is Part Of: Journal of food engineering. Volume 238(2018)
• Page Start: 70
• Page End: 77
• Publication Date: 2018-12
• Subjects: Imaging spectroscopy -- Hypercube -- Multivariate -- Data visualisation -- Neighbourhood methods; Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Analyse -- Périodiques; Aliments -- Recherche -- Périodiques; 664.005
• DOI: 10.1016/j.jfoodeng.2018.06.015
• Languages: English
• ISSNs: 0260-8774

Abstract

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 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. Highlights: Non-destructive classification of tea products. Non-linear data visualisation of NIRS data. Support vector machine classification with ensemble method. Supports rapid assessment of commercial tea products.