Classification of contaminants from wheat using near-infrared hyperspectral imaging

Biosystems Engineering

Volumn 135, Issue , 2015, Pages 73-86

  Ravikanth, Lankapalli ^a   Singh, Chandra B ^a   Jayas, Digvir S ^a   White, Noel D G ^b  

^a UNIVERSITY OF MANITOBA   (Canada)

^b Cereal Research Centre   (Canada)

Author keywords

Animal excreta; Dockage; Foreign material; Near infrared hyperspectral imaging; Spectral pre processing; Statistical classification

Indexed keywords

ANIMALS; CLASSIFICATION (OF INFORMATION); CONTAMINATION; DIAGNOSTIC RADIOGRAPHY; IMPURITIES; INFRARED DEVICES; NEAREST NEIGHBOR SEARCH; SPECTROSCOPY; SUPPORT VECTOR MACHINES;

DOCKAGE; FOREIGN MATERIALS; NEAR-INFRARED HYPERSPECTRAL IMAGING; PRE-PROCESSING; STATISTICAL CLASSIFICATION;

GRAIN (AGRICULTURAL PRODUCT);

ANIMALIA; AVENA; AVENA FATUA; BRASSICA NAPUS VAR. NAPUS; CERVIDAE; GLYCINE MAX; HORDEUM; ORYCTOLAGUS CUNICULUS; SECALE CEREALE; TRITICUM AESTIVUM; ZEA MAYS;

EID: 84930210544     PISSN: 15375110     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.biosystemseng.2015.04.007     Document Type: Article

References (42)

1. ASABE ASAE S352.2: Moisture measurement-unground grain and seeds 2012, ASAE, St. Joseph, Michigan.
2. Bauriegel E., Giebel A., Geyer M., Schmidt U., Herppich W.B. Early detection of Fusarium infection in wheat using hyperspectral imaging. Computers and Electronics in Agriculture 2011, 75(2):304-312.
3. Buddenbaum H., Steffens M. The effect of spectral pretreatments on chemometrics analyses of soil profile using laboratory imaging spectroscopy. Applied and Environmental Soil Science 2012, 1-12. Article ID 274903.
4. Centner V., Massart D.L., de Noord O.E. Detection of inhomogeneities in sets of NIR spectra. Analytica Chimica Acta 1996, 330(1):1-17.
5. CGC Official grain grading guide-Wheat: August 1, 2013 2013, Canadian Grain Commission Industry services, Canadian Grain Commission, Available from:, Accessed 11.11.13. https://www.grainscanada.gc.ca/oggg-gocg/2013/04-wheat-2013-eng.pdf.
6. Chang C., Lin C. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2011, 2. 27:1-27:27. Available from:, Accessed 07.03.13. http://www.csie.ntu.edu.tw/%7Ecjlin/libsvm.
7. Choudhary R., Mahesh S., Paliwal J., Jayas D.S. Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples. Biosystems Engineering 2009, 102(2):115-127.
8. Churchill D.B., Bilsland D.M., Cooper T.M. Separation of mixed lots of tall fescue and ryegrass seed using machine vision. Transactions of the ASAE 1993, 36(5):1383-1386.
9. Delwiche S.R., Hareland G.A. Detection of scab-damaged hard red spring wheat kernels by near-infrared reflectance. Cereal Chemistry 2004, 81(5):643-649.
10. Dogan H., Subramanyam B., Pedersen J.R. Analysis for extraneous matter. Food analysis 2010, 351-365. Springer, New York. S.S. Nielsen (Ed.).
11. Duda R.O., Peter E.H., Stork D.G. Nonmetric methods. Pattern classification 2001, Wiley-Interscience, New York City, NY. 2nd ed.
12. Geladi P., MacDougall D., Martens H. Linearization and scatter-corrections for near-infrared reflectance spectra of meat. Applied Spectroscopy 1985, 39(3):491-500.
13. Granitto P.M., Navone H.D., Verdes P.F., Ceccatto H.A. Weed seeds identification by machine vision. Computer and Electronics in Agriculture 2002, 33(2):91-103.
14. Guevara-Hernandez F., Gomez-Gil J. A machine vision system for classification of wheat and barley grain kernels. Spanish Journal of Agricultural Research 2011, 9(3):672-680.
15. Jayas D.S., Paliwal J., Visen N.S. Multi-layer neural networks for image analysis of agricultural products. Journal of Agricultural Engineering Research 2000, 77:119-128.
16. Kaliramesh S., Chelladurai V., Jayas D.S., Alagusundaram K., White N., Fields P. Detection of infestation by Callosobruchus maculatus in mungbean using near-infrared hyperspectral imaging. Journal of Stored Product Research 2013, 52:107-111.
17. Karunakaran C., Jayas D.S., White N.D.G. Soft X-ray inspection of wheat kernels infested by Sitophilus oryzae. Transactions of the ASAE 2003, 46(3):739-745.
18. Mahesh S., Jayas D.S., Paliwal J., White N.D.G. Near-infrared (NIR) hyperspectral imaging- An emerging analytical tool for classification of western Canadian wheat classes from different locations and crop years. North Central CSBE/ASABE Inter-sectional Conference ASABE conference paper (Paper No. MB-SK 10-302). Saskatoon, Saskatchewan. October 7-9, 2010 2010, ASABE, St. Joseph, Michigan, M.I.
19. Mahesh S., Jayas D.S., Paliwal J., White N.D.G. Identification of wheat classes at different moisture levels using near-infrared hyperspectral images of bulk samples. Sensing and Instrumentation for Food Quality and Safety 2011, 5(1):1-9.
20. Mahesh S., Manickavasagan A., Jayas D.S., Paliwal J., White N.D.G. Feasibility of near-infrared hyperspectral imaging to differentiate Canadian wheat classes. Biosystems Engineering 2008, 101(1):50-57.
21. Majumdar S., Jayas D.S. Classification of cereal grains using machine vision: I. Morphology models. Transactions of the ASAE 2000, 43(6):1669-1675.
22. Majumdar S., Jayas D.S. Classification of cereal grains using machine vision: II. Colour models. Transactions of the ASAE 2000, 43(6):1677-1680.
23. Majumdar S., Jayas D.S. Classification of cereal grains using machine vision: III. Texture models. Transactions of the ASAE 2000, 43(6):1681-1687.
24. Majumdar S., Jayas D.S. Classification of cereal grains using machine vision: IV. Combined morphology, colour, and texture models. Transactions of the ASAE 2000, 43(6):1689-1694.
25. Martens H., Jensen S.A., Geladi P. Multivariate linearity transformation for near infrared reflectance spectra of meat. Application spectroscopy. Proceedings of the Nordic Symposium, Applied Statistics 1983, 235-267. Stokkand Forlag Publication, Stavanger, Norway, June 12-14, 1983.
26. Martens H., Naes T. Multivariate calibration 1992, John Wiley & Sons Ltd, Chichester.
27. Melgani F., Bruzzone L. Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing 2004, 42(8):1778-1790.
28. Nair M., Jayas D.S. Dockage identification in wheat using machine vision. Canadian Agricultural Engineering 1998, 40(4):293-298.
29. NATO Survey of hyperspectral and multispectral imaging technologies 2007, RTO Technical Report, North Atlantic Treaty Organization, Springfield, Virginia, TR-SET-065-P3, AC/323 (SET-065) TP/44. Government Reports Announcements & Index (GRA&I).
30. Paliwal J., Shashidhar N.S., Jayas D.S. Grain kernel identification using kernel signature. Transactions of the ASAE 1999, 42(6):1921-1924.
31. Paliwal J., Visen N.S., Jayas D.S., White N.D.G. Quantification of variations in machine-vision-computed morphological features of cereal grains 2002, Paper No. 023131, ASAE, St. Joseph.
32. Paliwal J., Visen N.S., Jayas D.S., White N.D.G. Cereal Grain and Dockage Identification using Machine Vision. Biosystems Engineering 2003, 85(1):51-57.
33. Pierna J.A.F., Vermeulen P., Amand O., Tossens A., Dardenne P., Baeten V. NIR hyperspectral imaging spectroscopy and chemometrics for the detection of undesirable substances in food and feed. Chemometrics and Intelligent Laboratory Systems 2012, 117:233-239.
34. Savitzky A., Golay M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 1964, 36(8):1627-1639.
35. Singh C.B., Jayas D.S., Paliwal J., White N.D.G. Fungal detection in wheat using near-infrared hyperspectral imaging. Transactions of the ASABE 2007, 50(6):2171-2176.
36. Singh C.B., Jayas D.S., Paliwal J., White N.D.G. Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging. Journal of Stored Product Research 2009, 45(3):151-158.
37. Singh C.B., Jayas D.S., Paliwal J., White N.D.G. Detection of midge-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging. Biosystems Engineering 2010, 105(3):380-387.
38. Statistics Canada Estimated areas, yield, production and average farm price of principal field crops, in metric units- Table 001-0010. Statistics Canada 2014, Available from:, Accessed 07.01.14. http://www5.statcan.gc.ca/cansim/a26?lang=eng%26retrLang=eng%26id=0010010%26paSer=%26pattern=%26stByVal=1%26p1=1%26p2=-1%26tabMode=dataTable%26csid.
39. Stordrange L., Libnau F.O., Malthe-Sørenssen D., Kvalheim O.M. Feasibility study of NIR for surveillance of a pharmaceutical process, including a study of different preprocessing techniques. Journal of Chemometrics 2002, 16(8-10):529-541.
40. Vermeulen Ph, Pierna J.A.F., Van Egmond H.P., Dardenne P., Baeten V. Online detection and quantification of ergot bodies in cereals using infrared hyperspectral imaging. Food Additives and Contaminants. Part A, Chemistry, Analysis, Control, Exposure & Risk Assessment 2011, 29(2):232-240.
41. Zhang G., Jayas D.S., White N.D.G. Separation of touching grain kernels in an image by ellipse fitting algorithm. Biosystems Engineering 2005, 92(2):135-142.
42. Zhang H., Paliwal J., Jayas D.S., White N.D.G. Classification of fungal infected wheat kernels using near-infrared reflectance hyperspectral imaging and support vector machine. Transactions of the ASABE 2007, 50(5):1779-1785.