INTECHN PLATFORM FOR GRAIN SAMPLE QUALITY ASSESSMENT
Keywords:
grain sample quality assessment, feature extraction, spectra analysis, classificationAbstract
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.
References
Baye, T. M., T. C. Pearson and A. M. Settles, 2006. development of a calibration to predict maize seed composition using single kernel near infrared spectroscopy. Journal of cereal Science, 43 (2): 236-243.
Brosnan, T. and D. W. Sun, 2003. Inspection and grading of agricultural and food products by computer vision systems - a review. computers and electronics in Agriculture, 36: 193 – 213.
Dowell, F. E., B. Maghirang, F. Xie, G. L. Lookhart, R. O. Pierce, B. W. S. Seabourn, R. Bean, J. D. Wilson and O. K. Chung, 2006. Predicting Wheat Quality characteristics and Functionality Using near-Infrared Spectroscopy. cereal chemistry, 83 (5): 529–536.
Dowell, F. E., T. C. Pearson, E. B. Maghirang, F. Xie and D. T. Wicklow, 2002. Reflectance and transmittance spectroscopy applied to detecting fumonisin in single corn kernels infected with Fusarium verticillioides, cereal chemistry, 79: 222–226.
Girolamo, A. D., V. Lippolis, E. Nordkvist and A. Visconti, 2009. rapid and non invasive analysis of deoxinivalenol in durum and common wheat by Fourier – Transform infrared spectroscopy, Food Additives and contaminants, 26 (06): 907-917.
Howlett, R. and J. Jain, 2001. radial basis function networks 1: recent developments in theory and applications. Springer.
Huang, H., H. Yu, H. Xu and Y. Ying, 2008. near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: a review. Journal of Food engineering, 87 (3): 303-313.
Huang, D. S. and W. B. Zhao, 2005. determining the centers of radial basis probabilistic neural networks by recursive orthogonal least square algorithms. Applied Mathematics and computation, 162 (1): 461-473.
Kos, G., H. Lohninger and R. Krska, 2003. development of a Method for the determination of Fusarium Fungi on corn Using Mid-Infrared Spectroscopy with attenuated Total Reflection and Chemometrics, Anal. chem., 75 (5): 1211–1217.
Liu, J. and M. R. Paulsen, 2000. corn whiteness measurement and classification using machine vision. transactions of the ASAe, 43 (3): 757-763.
Luo, X., D. S. Jayas and S. Symons, 1999. comparison of statistical and neural network methods for classifying cereal grains using machine vision. transactions of the ASAe, 42 (2): 413-419.
Mahesh, S., D. S. Jayas, J. Paliwal and N. D. G. White, 2010. Identification of wheat classes at different moisture levels using near-infrared hyperspectral images of bulk samples, Sensing and instrumentation for food quality and safety, 10.1007.
Majumdar, S. and D. S. Jayas, 2000a. Classification of cereal grains using machine vision: color Models II, transaction of ASAe, 43 (6): 1677-1680.
Majumdar, S. and D. S. Jayas, 2000b. Classification of cereal grains using machine vision: Morphology models I, transaction of ASAe, 43 (6): 1669-1675.
Miralbés, C., 2003. Prediction chemical composition and alveograph Parameters on Wheat by near-Infrared Transmittance Spectroscopy. J. Agric. Food chem., 51 (21): 6335–6339.
Mladenov, M. and M. Dejanov, 2008. application of neural networks for seed germination assessment. Proceedings of the 9th WSeAS international conference on neural networks cnn’08, Sofia, Bulgaria, pp. 67-72.
Mladenov, M., S. Penchev, B. Borisov, K. Arvanitis and N. Sigrimis, 2004. evaluation of some properties for purity assessment of seeds using computer vision system. Proceedings of Ageng, 2004, leuven, Belgium.
Mladenov, M., S. Penchev, M. Dejanov and M. Mustafa, 2011. Quality assessment of grain samples using color image analysis. Proc. оf тhe 8th IASTED International conference on Signal Processing, Pattern Recognition and Applications.
Ning, S., R. Ruan, L. Luo, X. Chen, P.L. Chen and R.K. Jones, 1988. automation of a machine vision and neural network based system for determination of scabby rate of wheat samples. ASAe Annual International Meeting, orlando, Florida.
Paliwal, J., N. S. Visen and D. S. Jayas, 2001. evaluation of neural network architectures for cereal grain classification using morphological features. Journal of Agr. engineering Research. 79 (4): 361-370.
Paliwal, J., N. S. Visen, D. S. Jayas and N. D. G. White, 2003a. Cereal grain and dockage identification using machine vision. Biosystems engineering. 85 (1): 51-57.
Paliwal, J., N. S. Visen and D. S. Jayas, 2003b. comparison of a neural network and non-parametric classifier for grain kernel identification. Biosystems engineering. 85(4): 405-413.
Paulsen, M. R., L. O. Pordesimo, M. Singh, S. W. Mbuvi and B. Ye, 2003. Maize starch yield calibrations with near-infrared reflectance. Biosystems engineering, 85 (4): 455-460.
Paulsen, M. R. and M. Singh, 2004. calibration of a nearinfrared transmission grain analyzer for extractable starch in maize. Biosystems engineering. 89 (1): 79-83.
Pearson, T. C., D. T. Wicklow, E. B. Maghirang, F. Xie and F. E. Dowell, 2001. Detecting aflatoxin in single corn kernels by transmittance and reflectance spectroscopy, transactions of the American Society of Agricultural engineers, 44: 1247–1254.
Peiris, K. H. S., M. O. Pumphrey, Y. Dong, E. B. Maghirang, W. Berzonsky and F. E. Dowell, 2010. Near-Infrared Spectroscopic Method for Identification of Fusarium head Blight damage and Prediction of deoxynivalenol in Single Wheat Kernels, cereal chem., 87 (6): 511–517.
Ruan, R., Y. Li, X. Lin and P. Chen, 2002. non-destructive determination of deoxynivalenol levels in barley using nea-infrared spectroscopy, Applied engineering in Agriculture, 18 (50): 549-553.
Schneider, H. and H. Kutzbach, 1999. determination of broken kernels in threshing process using image analysing and machine vision. (Proceedings of Symposium “actual Tasks on agricultural engineering”, croatia).
Wesley, I. J., O. Larroque, B. G. Osborne, N. Azudin, H. Allen and J. H. Skerritt, 2001. Measurement of gliadin and glutenin content of flour by NIR spectroscopy. Journal of cereal Science, 34:125–133.
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