Automated tea quality classification by hyperspectral imaging

Applied Optics

Volumn 48, Issue 19, 2009, Pages 3557-3564

  Zhao, Jiewen ^a   Chen, Quansheng ^a   Cai, Jianrong ^a   Ouyang, Qin ^a  

^a JIANGSU UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ACQUISITION; IMAGE ANALYSIS; IMAGING TECHNIQUES; PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS; SPECTROSCOPY; SUPPORT VECTOR MACHINES;

CLASSIFICATION MODELS; CLASSIFICATION RATES; CLASSIFICATION RESULTS; HYPERSPECTRAL DATA; QUALITY CLASSIFICATION; SVM ALGORITHM; SVM CLASSIFIERS; TEXTURE ANALYSIS;

HYPERSPECTRAL IMAGING;

EID: 67749093161     PISSN: 1559128X     EISSN: 15394522     Source Type: Journal    
DOI: 10.1364/AO.48.003557     Document Type: Article

References (40)

1. A. F. H. Goetz, G. Vane, T. E. Solomon, and B. N. Rock, "Imaging spectrometry for earth remote sensing," Science 228, 1147-1153 (1985).
2. K. S. Wood, A. M. Gulian, G. G. Fritz, and D. Van Vechten, "A QVD detector for focal plane hyperspectral imaging in astronomy," Bull. Am. Astron. Soc. 34, 1241(2002).
3. E. Hege, D. O'Connell, W. Johnson, S. Basty and E. Dereniak, "Hyperspectral imaging for astronomy and space surveillance," Proc. SPIE 5159, 380-391 (2003).
4. Y. Uno, S. Prasher, R. Lacroix, P. Goel, Y. Karimi, A. Viau, and R. Patel, "Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data," Comput. Electron. Agric. 47, 149-161 (2005).
5. V. Smail, A. Fritz, and D. Wetzel, "Chemical imaging of intact seeds with NIR focal plane array assists plant breeding," Vib. Spectrosc. 42, 215-221 (2006).
6. O. Rodionova, L. Houmøller, A. Pomerantsev, P. Geladi, J. Burger, V. Dorofeyev, and A. Arzamastsev, "NIR spectrometry for counterfeit drug detection: a feasibility study," Anal. Chim. Acta 549, 151-158 (2005).
7. Y. Roggo, A. Edmond, P. Chalus, and M. Ulmschneider, "Infrared hyperspectral imaging for qualitative analysis of pharmaceutical solid forms," Anal. Chim. Acta 535, 79-87 (2005).
8. D. Kellicut, J. Weiswasser, S. Arora, J. Freeman, R. Lew, C. Shuman, J. Mansfield, and A. Sidawy "Emerging technology: hyperspectral imaging," Perspect. Vasc. Surg. Endovasc. Ther. 16, 53-57 (2004).
9. G. Zheng, Y. Chen, X. Intes, B. Chance, and J. D. Glickson, "Contrast enhanced near-infrared (NIR) optical imaging for subsurface cancer detection," J. Porphyrins Phthalocyanines 8, 1106-1117 (2004).
10. J. Qiao, M. O. Ngadi, N. Wang, C. Gariépy, and S. O. Prasher, "Pork quality and marbling level assessment using a hyperspectral imaging system," J. Food Eng. 83, 10-16 (2007).
11. J. Qiao, N. Wang, M. O. Ngadi, A. Gunenc, M. Monroy C. Gariépy, and S. O. Prasher, "Prediction of drip-loss, pH, and color for pork using a hyperspectral imaging technique," Meat science 76, 1-8 (2007).
12. B. Park, W. R. Windham, K. C. Lawrence, and D. P. Smith, "Contaminant classification of poultry hyperspectral imagery using a spectral angle mapper algorithm," Biosyst. Eng. 96, 323-333 (2007).
13. B. Park, K. C. Lawrence, W. R. Windham, and D. P. Smith, "Performance of hyperspectral imaging system for poultry surface fecal contaminant detection," J. Food Eng. 75, 340-348(2006).
14. Y. L. Liu, W. R. Windham, K. C. Lawrence, and B. Park, "Simple algorithms for the classification of visible/nearinfrared and hyperspectral imaging spectra of chicken skins, feces, and fecal contaminated skins," Appl. Spectrosc. 57, 1609-1612 (2003).
15. L. Jiang, B. Zhu, X. Q. Rao, G. Berney and Y. Tao, "Discrimination of black walnut shell and pulp in hyperspectral fluorescence imagery using Gaussian kernel function approach," J. Food Eng. 81, 108-117 (2007).
16. J. Xing, W. Saeys, and J. De Baerdemaeker, "Combination of chemometric tools and image processing for bruise detection on apples," Comput. Electron. Agric. 56, 1-13 (2007).
17. H. K. Noh and R. F. Lu, "Hyperspectral laser-induced fluorescence imaging for assessing apple fruit quality," Postharvest Biol. Technol. 43, 193-201 (2007).
18. B. M. Nicolaï, E. Lötze, A. Peirs, N. Scheerlinck, and K. I. Theron, "Nondestructive measurement of bitter pit in apple fruit using NIR hyperspectral imaging," Postharvest Biol. Technol. 40, 1-6 (2006).
19. J. Xing and J. D. Baerdemaeker, "Bruise detection on 'Jonagold' apples using hyperspectral imaging," Postharvest Biol. Technol. 37, 152-162 (2005).
20. P. M. Mehl, Y. R. Chen, M. S. Kim, and D. E. Chan, "Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations," J. Food Eng. 61, 67-81 (2004).
21. Y. L. Liu, Y. R. Chen, C. Y. Wang, D. E. Chan, and M. S. Kim, "Development of a simple algorithm for the detection of chilling injury in cucumbers from visible/near-infrared hyperspectral imaging," Appl. Spectrosc. 59, 78-85 (2005).
22. D. P. Ariana, R. F. Lu, and D. E. Guyer, "Near-infrared hyperspectral reflectance imaging for detection of bruises on pickling cucumbers," Comput. Electron. Agric. 53, 60-70 (2006).
23. J. W. Qin, and R. F. Lu, "Measurement of the absorption and scattering properties of turbid liquid foods using hyperspectral imaging," Appl. Spectrosc. 61, 388-396 (2007).
24. P. Valera, F. Pablo, and A. G. Gonzalez, "Classification of tea samples by their chemical composition using discriminant analysis," Talanta 43, 415-419 (1996).
25. Y. G. Zuo, H. Chen, and Y. W. Deng, "Simultaneous determination of catechins, caffeine and gallic acids in green, Oolong, black and pu-erh teas using HPLC with a photodiode array detector," Talanta 57, 307-316 (2002).
26. N. Togari, A. Kobayashi, and T. Aishima, "Pattern recognition applied to gas chromatographic profiles of volatile component in three tea categories," Food Res. Int. 28, 495-502 (1995).
27. H. Hideki, M. Toshihiro, and K. Katsunori, "Simultaneous determination of qualitatively important components in green tea infusions using capillary electrophoresis," J. Chromatogr. A 758, 332-335 (1997).
28. M. Angeles Herrador and A. Gustavo Gonzalez, "Pattern recognition procedures for differentiation of Green, Black and Oolong teas according to their metal content from inductively coupled plasma atomic emission spectrometry," Talanta 53, 1249-1257 (2001).
29. Q. S. Chen, J. W. Zhao, C. H. Fang, and D. M. Wang," Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM)," Spectrochim. Acta Part A 66, 568-574 (2007).
30. Q. S. Chen, J. W. Zhao, H. D. Zhang, and X. Y. Wang, "Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration," Anal. Chim. Acta 572, 77-84 (2006).
31. Q. S. Chen, J. W. Zhao, H. D. Zhang, M. H. Liu, and M. Fang, "Qualitative identification of tea by infrared spectroscopy based on soft independent modeling of class analogy pattern recognition," J. Near Infrared Spectrosc. 13, 327-332 (2005).
32. S. Borah, E. L. Hines, and M. Bhuyan, "Wavelet transform based image texture analysis for size estimation applied to the sorting of tea granules," J. Food Eng. 79, 629-639 (2007).
33. Q. S. Chen, J. W. Zhao, and J. R. Cai, "Identification of tea varieties using computer vision," Trans. Am. Soc. Agric. Biol. Eng. 51, 615-621 (2008).
34. S. R. Gunn, M. Brown, and K. M. Bossley "Network performance assessment for neurofuzzy data modelling," Intell. Data Anal. 1208, 313-323 (1997).
35. P. C. Shih and C. J. Liu, "Face detection using discriminating feature analysis and support vector machine," Pattern Recognition 39, 260-276 (2006).
36. V. N. Vapnik, "The Nature of Statistical Learning Theory (Springer-Verlag, 1995).
37. C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining Knowledge Discovery 2, 121-167 (1998).
38. P. M. Mehl, K. Chao, M. Kim, and Y. R. Chen, "Detection of defects on selected apple cultivars usinghyperspectral and multispectral image analysis," Appl. Eng. Agric, 18, 219-226 (2002).
39. P. Bajcsy and P. Groves, "Methodology for hyperspectral band selection," Photogrammetric Eng. Remote Sens. J. 70, 793-802(2004).
40. K. Duan, S. S. Keerthi, and A. N. Poo, "Evaluation of simple performance measures for tuning SVM hyperparameters," Neurocomputing; Variable Star Bulletin 51, 41-59 (2003).