Application of Hyperspectral Imaging for Cultivar Discrimination of Malting Barley Grains

Piotr Zapotoczny 1  and Ewa Ropelewska 1
  • 1 Department of Systems Engineering, Faculty of Engineering, University of Warmia and Mazury in Olsztyn

Abstract

The aim of this study was to perform and evaluate the accuracy of classification of grains of different cultivars of malting barley. The grains of eight cultivars: Blask, Bor do, Con chita, Kormoran, Mercada, Serwal, Signora, Victoriana, with three moisture content: 12, 14, 16% were examined. The selected parameters of the surface texture of grain mass obtained from images taken using the techniques of hyperspectral imaging were determined. The accuracy of grains discrimination carried out using different methods of selection and classification of data was compared. The pairwise comparison and comparison of three, four and eight cultivars of malting barley were carried out. The most accurate discrimination was determined in the case of the pairwise comparison. Victoriana cultivar was the most different from the others. The most similar texture of grain mass was found in the comparison of cultivars: Blask and Mercada. In the case of eight examined cultivars of malting barley, the most accurate discrimination (classification error – 55%) was obtained for images taken at the moisture content of 14% and at a wavelength of 750 nm, for the attributes selection performed with the use of probability of error and average correlation coefficient (POE+ACC) method and the discrimination carried out using the linear discriminant analysis (LDA).

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  • Gowen, A.A., O’Donnella, C.P., Cullen, P.J., Downey, G., Frias, J.M. (2007). Hyperspectral imaging - an emerging process analytical tool for food quality and safety control. Trends in Food Science and Technology, 18, 590-598.

  • Huang, H., Liu, L., Ngadi, M.O. (2014). Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety. Sensors, 14, 7248-7276.

  • Jakubczyk, T., Haber, T. (1981). Analiza zbóż i przetworów zbożowych. SGGW-AR, Warszawa.

  • Mahesh, S., Manickavasagan, A., Jayas, D.S., Paliwal, J., White, N.D.G. (2008). Feasibility of nearinfrared hyperspectral imaging to differentiate Canadian wheat classes. Biosystems Engineering, 10, 50-57.

  • Pierna, J.A.F., Vermeulen, P., Amand, O., Tossens, A., Dardenne, P., Baeten, V. (2012). NIR hyperspectral imaging spectroscopy and chemometrics for the detection of undesirable substances in food and feed. Chemometrics and Intelligent Laboratory Systems, 117, 233-239.

  • PN-EN ISO 712:2012. Ziarno zbóż i przetwory zbożowe - Oznaczanie wilgotności - Metoda odwoławcza.

  • Shahin, M.A., Symons, S.J. (2011). Detection of Fusarium damaged kernels in Canada Western Red Spring wheat using visible/near-infrared hyperspectral imaging and principal component analysis. Computers and Electronics in Agriculture, 75, 107-112

  • Sun, D.W. (2010). Hyperspectral Imaging for Food Quality Analysis and Control. Academic Press/Elsevier, San Diego, California, USA.

  • Wallays C., Missotten B., De Baerdemaeker J., Saeys W. (2009). Hyperspectral waveband selection for on-line measurement of grain cleanness. Biosystems Engineering, 104, 1-7.

  • Williams, P.J., Geladi, P., Britz, T.J., Manley, M. (2012). Investigation of fungal development in maize kernels using NIR hyperspectral imaging and multivariate data analysis. Journal of Cereal Science, 55, 272-278.

  • Zapotoczny, P. (2009). Dyskryminacja odmian ziarna pszenicy na podstawie cech geometrycznych. Agricultural Engineering, 5(114), 319-328.

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