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Fusing spectral and textural information in near-infrared hyperspectral imaging to improve green tea classification modelling

Mishra, Puneet, Nordon, A, Mohd Asaari, Mohd Shahrimie, Lian, Guoping and Redfern, Sally (2019) Fusing spectral and textural information in near-infrared hyperspectral imaging to improve green tea classification modelling Journal of Food Engineering, 249. pp. 40-47.

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Hyperspectral imaging (HSI) can acquire data in two modes: imaging and spectroscopy, revealing the spatially-resolved spectral properties of materials. Traditional HSI processing in the close-range domain primarily focuses on the spectral information with minimal utilisation of the spatial information present in the data. The present work describes a methodology for utilising the spatial information present in HSI data to improve classification modelling over that achievable with spectral information alone. The methodology has been evaluated using near infrared (NIR) HSI data of sixteen green tea products from seven different countries. The methodology involves selecting and sharpening an image plane to enhance the textural details. The textural information is then extracted from the statistical properties of the grey level co-occurrence matrix (GLCM) of the sharpened image plane using a moving window operation. Finally, the textural properties are combined with the spectral information using one of the three different levels of data fusion, i.e. raw data level, feature level and decision level. Raw data-level fusion involved concatenating the spectral and textural data before performing the classification task. The feature-level fusion involved performing principal component analysis (PCA) on spectral and textural information and combining the PC scores obtained prior to performing classification. Decision-level fusion involved a majority voting scheme to enhance the final classification maps. All the classification tasks were performed using multi-class support vector machine (SVM) models. The results showed that combining the textural and spectral information during modelling resulted in improved classification of the sixteen green tea products compared to models built using spectral or textural information alone.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Chemical and Process Engineering
Authors :
Mishra, Puneet
Nordon, A
Mohd Asaari, Mohd Shahrimie
Redfern, Sally
Date : May 2019
Funders : European Union’s Horizon 2020 research and innovation programme
DOI : 10.1016/j.jfoodeng.2019.01.009
Grant Title : The Marie Sklodowska-Curie grant
Copyright Disclaimer : © 2019 Elsevier Ltd. All rights reserved.
Uncontrolled Keywords : Chemical imaging; Texture; Support vector machine (SVM); Grey level co-occurrence matrix (GLCM); Data fusion; Green tea
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. The authors also acknowledge the Centre for Hyperspectral Imaging at the University of Strathclyde, particularly Julius Tschannerl and Prof. Stephen Marshall for their kind help in performing the HSI experiments. The data related to the publication can be accessed in supplementary file. Appendix A. Supplementary data Supplementary data to this article can be found online at
Depositing User : Diane Maxfield
Date Deposited : 09 Sep 2019 10:56
Last Modified : 09 Sep 2019 10:56

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