Classification of tomatoes with different genotypes by visible and short-wave near-infrared spectroscopy with least-squares support vector machines and other chemometrics

Journal of Food Engineering

Volumn 94, Issue 1, 2009, Pages 34-39

  Xie, Lijuan ^a   Ying, Yibin ^a   Ying, Tiejin ^a  

^a ZHEJIANG UNIVERSITY   (China)

Author keywords

Classification; Tomato; Visible and short wave near infrared spectroscopy; Wavelength selection

Indexed keywords

CHEMOMETRICS; CLASSIFICATION; CLASSIFICATION RATES; DIFFUSE REFLECTANCES; IDENTIFICATION RATES; LEAST-SQUARES SUPPORT VECTOR MACHINES; PARTIAL-LEAST-SQUARES; PORTABLE APPLICATIONS; SOFT INDEPENDENT MODELING OF CLASS ANALOGIES; SPECTROSCOPIC TECHNIQUES; TOMATO; VISIBLE AND SHORT-WAVE NEAR-INFRARED SPECTROSCOPY; WAVELENGTH SELECTION;

COMPUTATIONAL FLUID DYNAMICS; DISCRIMINANT ANALYSIS; FRUITS; IMAGE RETRIEVAL; INFRARED DEVICES; RESEARCH AIRCRAFT; SPECTROSCOPIC ANALYSIS; SPECTRUM ANALYSIS; SUPPORT VECTOR MACHINES; TEXTILES;

INFRARED SPECTROSCOPY;

LYCOPERSICON ESCULENTUM;

EID: 67349136419     PISSN: 02608774     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jfoodeng.2009.02.023     Document Type: Article

References (28)

1. Acevedo F.J., Jiménez J., Maldonado S., Domínguez E., and Narváez A. Classification of wines produced in specific regions by UV-visible spectroscopy combined with support vector machines. Journal of Agricultural and Food Chemistry 55 (2007) 6842-6849
2. Blanco M., Coello J., Iturriaga H., Maspoch S., and Pages J. NIR calibration in non-linear systems: different PLS approaches and artificial neural networks. Chemometrics and Intelligent Laboratory Systems 50 1 (2000) 75-82
3. Borin A., Ferrão M.F., Mello C., Maretto D.A., and Poppi R.J. Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk. Analytica Chimica Acta 579 (2006) 25-32
4. Casale M., Sáiz Abajo M.J., González Sáiz J.M., Pizarro C., and Forina M. Study of the aging and oxidation processes of vinegar samples from different origins during storage by near-infrared spectroscopy. Analytica Chimica Acta 557 (2006) 360-366
5. Chauchard F., Cogdill R., Roussel S., Roger J.M., and Bellon-Maurel V. Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes. Chemometrics and Intelligent Laboratory Systems 71 (2004) 141-150
6. Chen J., Arnold M.A., and Small G.W. Comparison of combination and first overtone spectral regions of near-infrared calibration models for glucose and other biomolecules in aqueous solutions. Analytical Chemistry 76 18 (2004) 5405-5413
7. Cozzolino D., Kwiatkowski M.J., Parker M., Cynkar W.U., Dambergs R.G., Gishen M., and Herderich M.J. Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy. Analytical Chimica Acta 513 (2004) 73-80
8. Cozzolino D., Murray I., Chree A., and Scaife J.R. Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near-infrared spectroscopy. LWT - Food Science and Technology 38 (2005) 821-828
9. Gayo J., and Hale S.A. Detection and quantification of species authenticity and adulteration in crabmeat using visible and near-infrared spectroscopy. Journal of Agricultural and Food Chemistry 55 (2007) 585-592
10. Gestal M., Gómez-Carracedo M.P., Andrade J.M., Dorado J., Fernández E., Prada D., and Pazos A. Classification of apple beverages using artificial neural networks with previous variable selection. Analytica Chimica Acta 524 (2004) 225-234
11. Han Q.J., Wu H.L., Cai C.B., Xu L., and Yu R.Q. An ensemble of Monte Carlo uninformative variable elimination for wavelength selection. Analytica Chimica Acta 612 2 (2008) 121-125
12. Huang Y., Tang J., Swanson B.G., Cavinato A.G., Lin M., and Rasco B.A. Near infrared spectroscopy: a new tool for studying physical and chemical properties of polysaccharide gels. Carbohydrate Polymers 53 (2003) 281-288
13. Huck C.W., Guggenbichler W., and Bonn G.K. Analysis of caffeine, theobromine and theophylline in coffee by near infrared spectroscopy (NIRS) compared to high-performance liquid chromatography (HPLC) coupled to mass spectrometry. Analytica Chimica Acta 538 (2005) 195-203
14. Kovalenko I.V., Rippke G.R., and Hurburgh C.R. Determination of amino acid composition of soybeans (Glycine max) by near-infrared spectroscopy. Journal of Agricultural and Food Chemistry 54 (2006) 3485-3491
15. Langeron Y., Doussot M., Hewson D.J., and Duchêne J. Classifying NIR spectra of textile products with kernel methods. Engineering Applications of Artificial Intelligence 20 (2007) 415-427
16. Lin M., Cavinato A.G., Mayes D.M., Smiley S., Huang Y., Al-holy M., and Rasco B.A. Bruise detection in Pacific pink salmon (Oncorhynchus gorbuscha) by visible and short-wavelength near-infrared (SW-NIR) spectroscopy (600-1100 nm). Journal of Agricultural and Food Chemistry 51 (2003) 6404-6408
17. Liu L., Cozzolino D., Cynkar W.U., Gishen M., and Colby C.B. Geographic classification of Spanish and Australian Tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis. Journal of Agricultural and Food Chemistry 54 (2006) 6754-6759
18. Liu Y.D., Ying Y.B., Yu H.Y., and Fu X.P. Comparison of the HPLC method and FT-NIR analysis for quantification of glucose, fructose, and sucrose in intact apple fruits. Journal of Agricultural and Food Chemistry 54 (2006) 2810-2815
19. Pochet N., De Smet F., Suykens J.A.K., and De Moor B.L.R. Systematic benchmarking of microarray data classification: assessing the role of nonlinearity and dimensionality reduction. Bioinformatics 20 (2004) 3185-3195
20. Schölkopf B., Burges C., and Smola A. Three Remarks on the Support Vector Method of Function Estimation in Advanced in Kernel Methods: Support Vector Learning (1999), The MIT Press, Cambridge, MA
21. Suykens J.A.K., and Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters 9 (1999) 293-300
22. Suykens J.A.K., Van Gestel T., De Brabanter J., De Moor B., and Vandewalle J. Least Squares Support Vector Machines (2002), World Scientific, Singapore
23. Thissen U., Ustun B., Melssen W.J., and Buydens L.M.C. Multivariate calibration with least-squares support vector machines. Analytical Chemistry 76 (2004) 3099-3105
24. Yu H.Y., Ying Y.B., Fu X.P., and Lu H.S. Quality determination of Chinese rice wine based on Fourier transform near infrared spectroscopy. Journal of Near Infrared Spectroscopy 14 (2006) 37-44
25. Yu H.Y., Lin H.J., Ying Y.B., Li B.B., and Pan X.X. Prediction of enological parameters and discrimination of rice wine age using least-squares support vector machines and near infrared spectroscopy. Journal of Agricultural and Food Chemistry 56 (2008) 307-313
26. Van Gestel T., Suykens J., Baesens B., Viaene S., Vanthienen J., Dedene G., De Moor B., and Vandewalle J. Benchmarking least squares support vector machine classifiers. Machine Learning 54 (2004) 5-32
27. Vapnik V.N. The Nature of Statistical Learning Theory (1995), Springer-Verlag, New York, USA
28. Zhao J.W., Chen Q.S., Huang X.Y., and Fang C.H. Qualitative identification of tea categories by near infrared spectroscopy and support vector machine. Journal of the Pharmaceutical and Biomedical Analysis 41 (2006) 1198-1204