Identification of egg's freshness using NIR and support vector data description

Journal of Food Engineering

Volumn 98, Issue 4, 2010, Pages 408-414

  Zhao, Jiewen ^a   Lin, Hao ^a   Chen, Quansheng ^a   Huang, Xingyi ^a   Sun, Zongbao ^a   Zhou, Fang ^a  

^a JIANGSU UNIVERSITY   (China)

Author keywords

Egg; Freshness; Identification; Near infrared spectroscopy; Support vector data description

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CLASSIFICATION METHODS; CLASSIFICATION MODELS; DISCRIMINATION ANALYSIS; DISCRIMINATION MODEL; FRESHNESS; IDENTIFICATION RATES; K-NEAREST NEIGHBORS; NIR SPECTROSCOPY; PARTIAL LEAST SQUARES; SUPPORT VECTOR DATA DESCRIPTION; TRAINING SAMPLE; WAVELENGTH RANGES;

DATA DESCRIPTION; INFRARED DEVICES; NEAR INFRARED SPECTROSCOPY; NEURAL NETWORKS; PATTERN RECOGNITION; SUPPORT VECTOR MACHINES; VECTORS;

INFRARED SPECTROSCOPY;

EID: 77549086672     PISSN: 02608774     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jfoodeng.2010.01.018     Document Type: Article

References (36)

1. Berardinelli A., Giunchi A., Guarnieri A., Pezzi F., and Ragni L. Shell egg albumen height assessment by FT-NIR spectroscopy. Transactions of the ASAE 48 4 (2005) 1426-1428
2. Berrueta L.A., Alonso-Salces R.M., and Héberger K. Supervised pattern recognition in food analysis. Journal Chromatography A 1158 (2007) 196-214
3. Bu H.G., Wang J., and Huang X.B. Fabric defect detection based on multiple fractal features and support vector data description. Engineering Applications Artificial Intelligence 22 (2009) 224-235
4. Caglar N., Elmas M., Yaman Z.D., and Saribiyik M. Neural networks in 3-dimensional dynamic analysis of reinforced concrete buildings. Construction and Building Materials 22 (2008) 788-800
5. Chen Q.S., Zhao J.W., Fang C.H., and Wang D.M. Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM). Spectrochimica Acta Part A - Molecular and Biomolecular Spectroscopy 66 (2007) 568-574
6. Cho H.W. Data description and noise filtering based detection with its application and performance comparison. Expert Systems with Applications 36 (2009) 434-441
7. Devos O., Ruckebusch C., Durand A., Duponchel L., and Huvenne J.P. Support vector machines (SVM) in near infrared (NIR) spectroscopy: focus on parameters optimization and model interpretation. Chemometrics and Intelligent Laboratory Systems 96 (2009) 27-33
8. 2-photocatalytic degradation of nitrilotriacetic acid. Analytica Chimica Acta 595 (2007) 89-97
9. Fang S.F., Wang M.P., Qi Q.H., and Zheng F. Hybrid genetic algorithms and support vector regression in forecasting atmospheric corrosion of metallic materials. Computational Materials Science 44 (2008) 647-655
10. Fernández P.J.A., Baeren V., and Dardenne P. Screening of compound feeds using NIR hyperspectral data. Chemometrics and Intelligent Laboratory Systems 84 (2006) 114-118
11. Font R., Río-Celestino M.D., Cartea E., and Haro-Bailón A.D. Quantification of glucosinolates in leaves of leaf rape (Brassica napus ssp. Pabularia) by near-infrared spectroscopy. Phytochemistry 66 (2005) 175-185
12. Foody G.M., Mathur A., Sanchez-Hernandez C., and Boyd D.S. Training set size requirements for the classification of a specific class. Remote Sensing Environment 104 (2006) 1-14
13. Giunchi A., Berardinelli A., Ragni L., Fabbri A., and Silaghi F.A. Non-destructive freshness assessment of shell eggs using FT-NIR spectroscopy. Journal of Food Engineering 89 (2008) 142-148
14. Haugh R.R. A new method for determining the quality of an egg. US Egg Poultry 39 (1937) 27-49
15. Huang W.L., and Jiao L.C. Artificial immune kernel clustering network for unsupervised image segmentation. Progress in Natural Science 18 (2008) 455-461
16. Karoui R., Kemps B., Bamelis F., De Ketelaere B., Decuypere E., and De Baerdemaeker J. Methods to evaluate egg freshness in research and industry: a review. European Food Research and Technology 222 (2006) 727-732
17. Kestens V., Charoud-Got J., Bau A., Bernreuther A., and Emteborg H. Online measurement of water content in candidate reference materials by acousto-optical tuneable filter near-infrared spectrometry (AOTF-NIR) using pork meat calibrants controlled by Karl Fischer titration. Food Chemistry 106 (2008) 1359-1365
18. Lee S.W., Park J.Y., and Lee S.W. Low resolution face recognition based on support vector data description. Pattern Recognition 39 (2006) 1809-1812
19. Li-Chan E., and Nakai S. Biochemical basis for the properties of egg white. Poultry Biology 2 1 (1989) 21-50
20. Liu C.M., and Fu S.Y. Effective protocols for kNN search on broadcast multidimensional index trees. Information Systems Frontiers 33 (2008) 18-35
21. Liu X., Lu W.C., Jin S.L., Li Y.W., and Chen N.Y. Support vector regression applied to materials optimization of sialon ceramics. Chemometrics and Intelligent Laboratory Systems 82 (2006) 8-14
22. Liu Y.H., Lin S.H., Hsueh Y.L., and Lee M.J. Automatic target defect identification for TFT-LCD array process inspection using kernel FCM-based fuzzy SVDD ensemble. Expert Systems with Applications 36 (2009) 1978-1998
23. Pan Y., Chen J., and Guo L. Robust bearing performance degradation assessment method based on improved wavelet packet-support vector data description. Mechanical Systems Signal Processing 23 (2009) 669-681
24. Podsiadlo P., and Stachowiak G.W. Fast classification of engineering surfaces without surface parameters. Tribology International 39 (2006) 1624-1633
25. Ravna C., Skibsted E., and Bro R. Near-infrared chemical imaging (NIR-CI) on pharmaceutical solid dosage forms-comparing common calibration approaches. Journal of Pharmaceutical and Biomedical Analysis 48 (2008) 554-561
26. Sanchez-Hernandeza C., Boyd D.S., and Foody G.M. Mapping specific habitats from remotely sensed imagery: support vector machine and support vector data description based classification of coastal salt marsh habitats. Ecological Informatics 2 (2007) 83-88
27. Serudo R.L., De Oliveira L.C., Rocha J.C., Paterlini W.C., Rosa A.H., Da Silva H.C., and Botero W.G. Reduction capability of soil humic substances from the Rio Negro basin, Brazil, towards Hg(II) studied by a multi-method approach and principal component analysis (PCA). Geoderma 138 (2007) 229-236
28. Tao Q., Wu G.W., and Wang J. A new maximum margin algorithm for one-class problems and its boosting implementation. Pattern Recognition 38 (2005) 1071-1077
29. Tax D.M.J., and Duin R.P.W. Support vector domain description. Pattern Recognition Letters 20 (1999) 1191-1199
30. Tax D.M.J., and Duin R.P.W. Uniform object generation for optimizing one-class classifiers. Journal of Machine Learning Research 2 (2001) 155-173
31. Tax D.M.J., and Duin R.P.W. Support vector data description. Machine Learning 54 (2004) 45-66
32. Thissen U., Perers M., U{combining double acute accent}stǔn B., Melssen W.J., and Buydens L.M.C. Comparing support vector machines to PLS for spectral regression applications. Chemometrics and Intelligent Laboratory Systems 73 (2004) 169-179
33. United States Department of Agriculture, 2000. Agricultural Marketing Service. USDA Egg-Grading Manual. Agricultural Handbook No. 75. Washington.
34. Yetimezsoy K., and Demirel S. Artificial neural network (ANN) approach for modeling of Pb(II) adsorption from aqueous solution by Antep pistachio (Pistacia Vera L.) shells. Journal of Hazardous Materials 153 (2008) 1288-1300
35. Yu H.Y., Niu X.Y., Lin H.J., Ying Y.B., Li B.B., and Pan X.X. A feasibility study on on-line determination of rice wine composition by Vis-NIR spectroscopy and least-squares support vector machines. Food Chemistry 113 (2009) 291-296
36. Zou X.B., Zhao J.W., Huang X.Y., and Li Y.X. Use of FT-NIR spectrometry in non-invasive measurements of soluble solid contents (SSC) of 'Fuji' apple based on different PLS models. Chemometrics and Intelligent Laboratory Systems 87 (2007) 43-51