INTECHN platform for grain sample quality assessment

University of Rousse, Department of Automatics, Information and control engineering, BG - 7017 Rousse, Bulgaria


MLADENOV, M. and T. DRAGANOVA, 2011. INTECHN platform for grain sample quality assessment. Bulg. J. Agric. Sci., 17: 567-578

This paper presents the approaches, methods and tools for assessment of main quality features of grain samples using spectra analysis of the sample elements. They are developed within the frames of the research project “development of Intelligent Technologies for assessment of Quality and Safety of Food agricultural Products”, founded by the Bulgarian national Science Fund. The sample elements are divided in nine quality groups according to their surface features, which are related to the color characteristics and surface texture. Three different approaches are used for feature extraction from spectra and for data dimensionality reduction: principal component analysis and combinations of two kinds of wavelet analyses and principal component analysis. Three classifiers, based on radial basis elements, are used for object classification in the quality groups. The validation, training and testing errors of the classification procedures are evaluated. The results obtained are compared with the results obtained by the Unscrambler referent platform.

Key words: grain sample quality assessment, feature extraction, spectra analysis, classification
Abbreviations: INTECHN - Abbreviation of the research project; NIR - Near infrared reflectance; LDA - linear discriminat analysis; SvM - Support vector Machines; SIMca - Soft Independent Modeling of class analogy (SIMca); Pca - Principal component analysis

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