Abstract

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

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  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]

2008

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).

2007

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]

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]

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]

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]

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]

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]

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]

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]

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]

2006

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]

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]

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]

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]

P. C. Shih and C. J. Liu, “Face detection using discriminating feature analysis and support vector machine,” Pattern Recognition 39, 260-276 (2006).
[CrossRef]

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]

2005

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]

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]

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]

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]

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]

J. Xing and J. D. Baerdemaeker, “Bruise detection on 'Jonagold' apples using hyperspectral imaging,” Postharvest Biol. Technol. 37, 152-162 (2005).
[CrossRef]

2004

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]

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]

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]

P. Bajcsy and P. Groves, “Methodology for hyperspectral band selection,” Photogrammetric Eng. Remote Sens. J. 70, 793-802 (2004).

2003

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]

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]

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]

2002

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).

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]

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).

2001

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]

1998

C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining Knowledge Discovery 2, 121-167 (1998).
[CrossRef]

1997

S. R. Gunn, M. Brown, and K. M. Bossley, “Network performance assessment for neurofuzzy data modelling,” Intell. Data Anal. 1208, 313-323 (1997).

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]

1996

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]

1995

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]

1985

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]

Aishima, T.

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]

Angeles Herrador, M.

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]

Ariana, D. P.

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]

Arora, S.

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]

Arzamastsev, A.

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]

Baerdemaeker, J. D.

J. Xing and J. D. Baerdemaeker, “Bruise detection on 'Jonagold' apples using hyperspectral imaging,” Postharvest Biol. Technol. 37, 152-162 (2005).
[CrossRef]

Bajcsy, P.

P. Bajcsy and P. Groves, “Methodology for hyperspectral band selection,” Photogrammetric Eng. Remote Sens. J. 70, 793-802 (2004).

Basty, S.

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]

Berney, G.

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]

Bhuyan, M.

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]

Borah, S.

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]

Bossley, K. M.

S. R. Gunn, M. Brown, and K. M. Bossley, “Network performance assessment for neurofuzzy data modelling,” Intell. Data Anal. 1208, 313-323 (1997).

Brown, M.

S. R. Gunn, M. Brown, and K. M. Bossley, “Network performance assessment for neurofuzzy data modelling,” Intell. Data Anal. 1208, 313-323 (1997).

Burger, J.

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]

Burges, C. J. C.

C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining Knowledge Discovery 2, 121-167 (1998).
[CrossRef]

Cai, J. R.

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).

Chalus, P.

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]

Chan, D. E.

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]

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]

Chance, B.

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]

Chao, K.

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).

Chen, H.

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]

Chen, Q. S.

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).

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]

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]

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]

Chen, Y.

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]

Chen, Y. R.

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]

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]

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).

De Baerdemaeker, J.

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]

Deng, Y. W.

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]

Dereniak, E.

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]

Dorofeyev, V.

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]

Duan, K.

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]

Edmond, A.

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]

Fang, C. H.

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]

Fang, M.

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]

Freeman, J.

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]

Fritz, A.

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]

Fritz, G. G.

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).

Gariépy, C.

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]

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]

Geladi, P.

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]

Glickson, J. D.

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]

Goel, P.

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]

Goetz, A. F. H.

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]

Gonzalez, A. G.

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]

Groves, P.

P. Bajcsy and P. Groves, “Methodology for hyperspectral band selection,” Photogrammetric Eng. Remote Sens. J. 70, 793-802 (2004).

Gulian, A. M.

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).

Gunenc, A.

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]

Gunn, S. R.

S. R. Gunn, M. Brown, and K. M. Bossley, “Network performance assessment for neurofuzzy data modelling,” Intell. Data Anal. 1208, 313-323 (1997).

Gustavo Gonzalez, A.

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]

Guyer, D. E.

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]

Hege, E.

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]

Hideki, H.

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]

Hines, E. L.

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]

Houmøller, L.

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]

Intes, X.

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]

Jiang, L.

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]

Johnson, W.

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]

Karimi, Y.

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]

Katsunori, K.

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]

Keerthi, S. S.

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]

Kellicut, D.

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]

Kim, M.

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).

Kim, M. S.

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]

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]

Kobayashi, A.

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]

Lacroix, R.

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]

Lawrence, K. C.

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]

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]

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]

Lew, R.

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]

Liu, C. J.

P. C. Shih and C. J. Liu, “Face detection using discriminating feature analysis and support vector machine,” Pattern Recognition 39, 260-276 (2006).
[CrossRef]

Liu, M. H.

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]

Liu, Y. L.

Lötze, E.

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]

Lu, R. F.

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]

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]

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]

Mansfield, J.

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]

Mehl, P. M.

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]

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).

Monroy, M.

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]

Ngadi, M. O.

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]

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]

Nicolaï, B. M.

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]

Noh, H. K.

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]

O'Connell, D.

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]

Pablo, F.

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]

Park, B.

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]

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]

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]

Patel, R.

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]

Peirs, A.

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]

Pomerantsev, A.

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]

Poo, A. N.

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]

Prasher, S.

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]

Prasher, S. O.

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]

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]

Qiao, J.

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]

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]

Qin, J. W.

Rao, X. Q.

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]

Rock, B. N.

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]

Rodionova, O.

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]

Roggo, Y.

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]

Saeys, W.

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]

Scheerlinck, N.

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]

Shih, P. C.

P. C. Shih and C. J. Liu, “Face detection using discriminating feature analysis and support vector machine,” Pattern Recognition 39, 260-276 (2006).
[CrossRef]

Shuman, C.

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]

Sidawy, A.

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]

Smail, V.

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]

Smith, D. P.

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]

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]

Solomon, T. E.

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]

Tao, Y.

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]

Theron, K. I.

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]

Togari, N.

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]

Toshihiro, M.

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]

Ulmschneider, M.

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]

Uno, Y.

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]

Valera, P.

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]

Van Vechten, D.

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).

Vane, G.

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]

Vapnik, V. N.

V. N. Vapnik, “The Nature of Statistical Learning Theory (Springer-Verlag, 1995).

Viau, A.

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]

Wang, C. Y.

Wang, D. M.

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]

Wang, N.

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]

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]

Wang, X. Y.

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]

Weiswasser, J.

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]

Wetzel, D.

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]

Windham, W. R.

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]

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]

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]

Wood, K. S.

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).

Xing, J.

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]

J. Xing and J. D. Baerdemaeker, “Bruise detection on 'Jonagold' apples using hyperspectral imaging,” Postharvest Biol. Technol. 37, 152-162 (2005).
[CrossRef]

Zhang, H. D.

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]

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]

Zhao, J. W.

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).

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]

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]

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]

Zheng, G.

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]

Zhu, B.

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]

Zuo, Y. G.

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]

Anal. Chim. Acta

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]

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]

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]

Appl. Eng. Agric,

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).

Appl. Spectrosc.

Biosyst. Eng.

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]

Bull. Am. Astron. Soc.

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).

Comput. Electron. Agric.

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]

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]

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]

Data Mining Knowledge Discovery

C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining Knowledge Discovery 2, 121-167 (1998).
[CrossRef]

Food Res. Int.

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]

Intell. Data Anal.

S. R. Gunn, M. Brown, and K. M. Bossley, “Network performance assessment for neurofuzzy data modelling,” Intell. Data Anal. 1208, 313-323 (1997).

J. Chromatogr. A

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]

J. Food Eng.

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]

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]

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]

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]

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]

J. Near Infrared Spectrosc.

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]

J. Porphyrins Phthalocyanines

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]

Meat science

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]

Neurocomputing;Variable Star Bulletin

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]

Pattern Recognition

P. C. Shih and C. J. Liu, “Face detection using discriminating feature analysis and support vector machine,” Pattern Recognition 39, 260-276 (2006).
[CrossRef]

Perspect. Vasc. Surg. Endovasc. Ther.

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]

Photogrammetric Eng. Remote Sens. J.

P. Bajcsy and P. Groves, “Methodology for hyperspectral band selection,” Photogrammetric Eng. Remote Sens. J. 70, 793-802 (2004).

Postharvest Biol. Technol.

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]

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]

J. Xing and J. D. Baerdemaeker, “Bruise detection on 'Jonagold' apples using hyperspectral imaging,” Postharvest Biol. Technol. 37, 152-162 (2005).
[CrossRef]

Proc. SPIE

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]

Science

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]

Spectrochim. Acta Part A

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]

Talanta

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]

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]

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]

Trans. Am. Soc. Agric. Biol. Eng.

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).

Vib. Spectrosc.

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]

Other

V. N. Vapnik, “The Nature of Statistical Learning Theory (Springer-Verlag, 1995).

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Figures (5)

Fig. 1
Fig. 1

Hyperspectral imaging system developed at the Agricultural Product Processing and Storage Lab, Jiangsu University, in Zhenjiang, China.

Fig. 2
Fig. 2

Spectral profiles of five grades of tea samples from 500 nm to 900 nm .

Fig. 3
Fig. 3

Nonlinear separation of input and linear separation feature space. Squares and circles denote the negative and the positive training samples, respectively. These points have nonlinear separation in input space and linear separation in feature space.

Fig. 4
Fig. 4

Top three principal components (PC1, PC2, and PC3) images; three images at three dominant bands ( 762 nm , 793 nm , and 838 nm ).

Fig. 5
Fig. 5

Contour plot of the optimization the parameters γ and σ 2 by cross validation.

Tables (2)

Tables Icon

Table 1 Cross Matrix for the Classification Results in Training and Prediction Sets

Tables Icon

Table 2 Comparison of Classification Results from SVM, BP-ANN, and LDA Models

Equations (8)

Equations on this page are rendered with MathJax. Learn more.

R = I B W B ,
s = Φ ( x ) ,
K ( x i , x j ) = Φ ( x i ) · Φ ( x j ) .
K ( x i , x j ) = exp ( x i x j 2 2 σ 2 ) ,
Mean = i = 0 L 1 z i p ( z i ) ,
SD = i = 0 L 1 ( z i Mean ) 2 p 2 ( z i ) ,
Energy = i = 0 L 1 p 2 ( z i ) ,
Entropy = i = 0 L 1 p ( z i ) log 2 p ( z i ) .

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