OSA's Digital Library

Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 48, Iss. 19 — Jul. 1, 2009
  • pp: 3557–3564

Automated tea quality classification by hyperspectral imaging

Jiewen Zhao, Quansheng Chen, Jianrong Cai, and Qin Ouyang  »View Author Affiliations

Applied Optics, Vol. 48, Issue 19, pp. 3557-3564 (2009)

View Full Text Article

Enhanced HTML    Acrobat PDF (575 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



A hyperspectral imaging technique was attempted to classify green tea. Five grades of green tea samples were attempted. A hyperspectral imaging system was developed for data acquisition of tea samples. Principal component analysis was performed on the hyperspectral data to determine three optimal band images. Texture analysis was conducted on each optimal band image to extract characteristic variables. A support vector machine (SVM) was used to construct the classification model. The classification rates were 98% and 95% in the training and prediction sets, respectively. The SVM algorithm shows excellent performance in classification results in contrast with other pattern recognitions classifiers. Overall results show that the hyperspectral imaging technique coupled with a SVM classifier can be efficiently utilized to classify green tea.

© 2009 Optical Society of America

OCIS Codes
(110.0110) Imaging systems : Imaging systems
(110.2970) Imaging systems : Image detection systems

ToC Category:
Imaging Systems

Original Manuscript: March 9, 2009
Revised Manuscript: June 4, 2009
Manuscript Accepted: June 4, 2009
Published: June 22, 2009

Jiewen Zhao, Quansheng Chen, Jianrong Cai, and Qin Ouyang, "Automated tea quality classification by hyperspectral imaging," Appl. Opt. 48, 3557-3564 (2009)

Sort:  Author  |  Year  |  Journal  |  Reset  


  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). [CrossRef] [PubMed]
  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). [CrossRef]
  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). [CrossRef]
  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). [CrossRef]
  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). [CrossRef]
  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). [CrossRef]
  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). [CrossRef]
  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). [CrossRef]
  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). [CrossRef]
  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). [CrossRef] [PubMed]
  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). [CrossRef]
  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). [CrossRef]
  14. Y. L. Liu, W. R. Windham, K. C. Lawrence, and B. Park, “Simple algorithms for the classification of visible/near-infrared and hyperspectral imaging spectra of chicken skins, feces, and fecal contaminated skins,” Appl. Spectrosc. 57, 1609-1612 (2003). [CrossRef] [PubMed]
  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). [CrossRef]
  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). [CrossRef]
  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). [CrossRef]
  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). [CrossRef]
  19. J. Xing and J. D. Baerdemaeker, “Bruise detection on 'Jonagold' apples using hyperspectral imaging,” Postharvest Biol. Technol. 37, 152-162 (2005). [CrossRef]
  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). [CrossRef]
  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). [CrossRef] [PubMed]
  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). [CrossRef]
  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). [CrossRef] [PubMed]
  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). [CrossRef] [PubMed]
  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). [CrossRef]
  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). [CrossRef]
  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). [CrossRef]
  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). [CrossRef]
  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). [CrossRef]
  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). [CrossRef]
  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). [CrossRef]
  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). [CrossRef]
  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). [CrossRef]
  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). [CrossRef]
  38. P. M. Mehl, K. Chao, M. Kim, and Y. R. Chen, “Detection of defects on selected apple cultivars using hyperspectral 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). [CrossRef] [PubMed]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


Fig. 1 Fig. 2 Fig. 3
Fig. 4 Fig. 5

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited