Spectral data compression and analyses techniques to discriminate wheat classes

Transactions of the ASABE

Volumn 49, Issue 5, 2006, Pages 1607-1612

  Wang, Wenbo ^a   Paliwal, Jitendra ^a  

^a UNIVERSITY OF MANITOBA   (Canada)

Author keywords

Discrete Fourier transform; Discrete wavelet transform; Least squares support vector machines; Linear classifier; Near infrared spectroscopy; Principal component analysis; Probabilistic neural network

Indexed keywords

DISCRETE WAVELET TRANSFORM; LINEAR CLASSIFIERS; NEAR INFRARED SPECTROSCOPY; PROBABILISTIC NEURAL NETWORK; SUPPORT VECTOR MACHINES;

DATA COMPRESSION; DATA REDUCTION; DISCRETE FOURIER TRANSFORMS; PRINCIPAL COMPONENT ANALYSIS; WAVELET TRANSFORMS;

CROPS;

COMPRESSION; DATA SET; DISCRETE ELEMENT METHOD; FOURIER TRANSFORM; GROWTH RATE; NEAR INFRARED; PRINCIPAL COMPONENT ANALYSIS; SPECTRAL ANALYSIS; SPECTROSCOPY; WHEAT;

CLASSIFICATION; DISCRIMINANT ANALYSIS; NEURAL NETWORKS; WHEAT;

CANADA; NORTH AMERICA;

TRITICUM AESTIVUM;

EID: 33845667882     PISSN: 21510032     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (29)

1. AACC. 2000. Method 39-11. In Approved Methods of the AACC. St. Paul, Minn.: American Association of Cereal Chemists.
2. Candolfi, A., W. Wu, D. L. Massart, and S. Heuerding. 1998. Comparison of classification approaches applied to NIR spectra of clinical study lots. J. Pharmaceutical and Biomedical Analysis 16(8): 1329-1347.
3. Cocchi, M., M. Corbellini, G. Foca, M. Lucisano, M. A. Pagani, L. Tassi, and A. Ulrici. 2005. Classification of bread wheat flours in different quality categories by a wavelet-based feature selection/classification algorithm on NIR spectra. Analytica Chimica Acta 544(1-2): 100-107.
4. Cogdill, R. P., and P. Dardenne. 2004. Least-squares support vector machines for chemometrics: An introduction and evaluation. J. Near Infrared Spectroscopy 12(2): 93-100.
5. Draper, N., and H. Smith. 1986. Selecting the "best" regression equation. In Applied Regression Analysis, 294-352. N. Draper and H. Smith, eds. New York, N.Y.: John Wiley and Sons.
6. Fearn, T., and A. M. C. Davies. 2003. A comparison of Fourier and wavelet transforms in the processing of near infrared spectroscopic data: Part 1. Data compression. J. Near Infrared Spectroscopy 11(1): 3-15.
7. Fernandez Pierna, J. A., P. Volery, R. Besson, V. Baeten, and P. Dardenne. 2005. Classification of modified starches by Fourier transform infrared spectroscopy using support vector machines. J. Agric. Food Chem. 53(17): 6581-6585.
8. Geladi, P., D. Macdougall, and H. Martens. 1985. Linearization and scatter-correction for near-infrared reflectance spectra of meat. Applied Spectroscopy 39(3): 491-500.
9. Goicoechea, H. C., and A. C. Olivieri. 2002. Wavelength selection for multivariate calibration using a genetic algorithm: A novel initialization strategy. J. Chem. Info. Computing Sci. 42(5): 1146-1153.
10. Indahl, U. G., N. S. Sahni, B. Kirkhus, and T. Næs. 1999. Multivariate strategies for classification based on NIR-spectra - with application to mayonnaise. Chemometrics and Intelligent Laboratory Systems 49(1): 19-31.
11. Jackson, E. J. 1991. A User's Guide to Principal Components. New York, N.Y.: John Wiley and Sons.
12. Jolliffe, I. T. 1986. Principal Component Analysis. New York, N.Y.: Springer-Verlag.
13. Lásztity, R., and A. Salgó. 2002. Quality assurance of cereals: Past, present, future. Periodica Polytechnica Ser. Chem. Eng. 46(1-2): 5-13.
14. Leung, A. K., F. Chau, and J. Gao. 1998. A review on applications of wavelet transform techniques in chemical analysis: 1989-1997. Chemometrics and Intelligent Laboratory Systems 43: 165-184.
15. Mallet, Y., D. Coomans, and O. de Vel. 1996. Recent developments in discriminant analysis on high dimensional spectral data. Chemometrics and Intelligent Laboratory Systems 35(2): 157-173.
16. Mark, H. L., and D. Tunnell. 1985. Qualitative near-infrared reflectance analysis using Mahalanohis distances. Analytical Chem. 57(7): 1449-1456.
17. Osborne, B. G., T. Fearn, and P. H. Hindle. 1993. Practical NIR Spectroscopy: With Applications in Food and Beverage Analysis. Essex, U.K.: Longman Scientific and Technical.
18. Pal, M., and P. M. Mather. 2005. Support vector machines for classification in remote sensing. International J. Remote Sensing 26(5): 1007-1011.
19. Specht, D. F. 1990. Probabilistic neural networks. Neural Networks 3(1): 109-118.
20. Strang G., and T. Nguyen. 1996. Wavelets and Filter Banks. Wellesley, Mass.: Wellesley-Cambridge Press.
21. Suykens, J. A. K., T. Van Gestel, J. De Brabanter, B. De Moor, and J. Vandewalle. 2002. Least Squares Support Vector Machines. Singapore: World Scientific.
22. Swierenga, H., F. Wülfert, O. E. de Noord, A. P. de Weijer, A. K. Smilde, and L. M. C. Buydens. 2000. Development of robust calibration models in near-infrared spectrometric applications. Analytica Chimica Acta 411: 121-135.
23. Tate, R., D. Watson, and S. Eglen. 1995. Using wavelets for classifying human in vivo magnetic resonance spectra. In Wavelets and Statistics Lecture Notes in Statistics 103: 377-383. A. Antoniadis and G. Oppenheim, eds.New York, N.Y.: Springer-Verlag.
24. Trygg, J., and S. Wold. 1998. PLS regression on wavelet compressed NIR spectra. Chemometrics and Intelligent Laboratory Systems 42: 209-220.
25. Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. New York, N.Y.: Springer-Verlag.
26. Wang, H., S. D. Babacan, and K. Sayood. 2005. Lossless hyperspectral image compression using context-based conditional averages. In Proc. Data Compression Conference. Piscataway, N.J.: IEEE.
27. Wolff, D. D., and M. L. Parsons. 1983. Pattern Recognition Approach to Data Interpretation. New York, N.Y.: Plenum Press.
28. Wu, W., B. Walczak, D. L. Massart, K. A. Prebble, and I. R. Last. 1995. Spectral transformation and wavelength selection in near-infrared spectra classification. Analytica Chimica Acta 315(3): 243-255.
29. Wu, W., B. Walczak, W. Penninckx, and D. L. Massart. 1996. Feature reduction by Fourier transform in pattern recognition of NIR data. Analytica Chimica Acta 331(1-2): 75-83.