Quality evaluation of pickling cucumbers using hyperspectral reflectance and transmittance imaging: Part I. Development of a prototype

  • Diwan P. ArianaEmail author
  • Renfu Lu
Original Paper


This article reports on the development of a hyperspectral imaging prototype for online evaluation of external and internal quality of pickling cucumbers. The prototype consisted of a two-lane round belt conveyor, two illumination sources (one for reflectance and one for transmittance), and a hyperspectral imaging unit. It had a novel feature of simultaneous imaging under reflectance mode in the visible region (400–675 nm) and transmittance mode for the red and near-infrared region (Red-NIR) (675–1000 nm). Reflectance information from the visible region was intended for evaluating the external characteristics of cucumbers such as skin color, whereas transmittance information from Red-NIR was used for internal defect detection (i.e., hollow center). Additional features of the prototype included simultaneous acquisition of reflectance and transmittance from calibration references that were installed in the system, to provide real-time, continuous corrections of individual hyperspectral images from each sample. Methods and algorithms were developed of estimating cucumber fruit size and correcting the effect of fruit size on transmittance measurements. The system was calibrated and evaluated for detecting the color, size, and internal defect of pickling cucumbers.


Grading Hyperspectral imaging Pickling cucumbers External quality Internal quality Defect detection 



The authors appreciate Mr. Benjamin Bailey, Engineering Technician, for providing technical support to this research.


  1. 1.
    Y.R. Chen, K. Chao, M.S. Kim, Comput. Electron. Agric. 36, 173 (2002). doi: 10.1016/S0168-1699(02)00100-X CrossRefGoogle Scholar
  2. 2.
    B.G. Osborne, in Encyclopedia of Analytical Chemistry, ed. by R.A. Meyers (Wiley, New York, 2000)Google Scholar
  3. 3.
    J.A. Abbott, Postharv. Biol. Technol. 15, 207 (1999). doi: 10.1016/S0925-5214(98)00086-6 CrossRefGoogle Scholar
  4. 4.
    P. Butz, C. Hofmann, B. Tauscher, J. Food Sci. 70, R131 (2005)Google Scholar
  5. 5.
    S. Saranwong, S. Kawano, in Near-infrared Spectroscopy in Food Science and Technology, ed. by Y. Ozaki, W.F. McClure, A.A. Christy (Wiley, Hoboken, 2007), Chapter 7.2, pp. 219–245Google Scholar
  6. 6.
    D.C. Slaughter, J.A. Abbott, in Near-infrared Spectroscopy in Agriculture, ed. by C.A. Roberts, J. Jerry Workman, J.B.R. III (American Society of Agronomy, Inc., Crop Science Society of America, Inc., Soil Science Society of America, Inc., Wisconsin, 2004), Chapter 14, pp. 377–398Google Scholar
  7. 7.
    W. Wang, J. Paliwal, Sens. Instrum. Food Qual. Saf. 1, 193 (2007). doi: 10.1007/s11694-007-9022-0 CrossRefGoogle Scholar
  8. 8.
    J.E. Staub, J. Bacher, in Processing Vegetables: Science and Technology, ed. by D.S. Smith, J.N. Cash, W.-K. Nip, Y.H. Hui (Technomic Publishing Company, Inc., Lancaster, 1997), Chapter 6, pp. 129–193Google Scholar
  9. 9.
    A.R. Miller, T.J. Kelley, B.D. White, J. Am. Soc. Hortic. Sci. 120, 1063 (1995)Google Scholar
  10. 10.
    D.P. Ariana, R. Lu, Trans. ASABE 51, 705 (2008)Google Scholar
  11. 11.
    A.R. Miller, Phytochemistry 28, 389 (1989). doi: 10.1016/0031-9422(89)80018-4 CrossRefGoogle Scholar
  12. 12.
    A.R. Miller, Postharv. News Inform. 3, 53N (1992)Google Scholar
  13. 13.
    A.R. Miller, J.P. Dalmasso, D.W. Kretchman, J. Am. Soc. Hortic. Sci. 112, 666 (1987)Google Scholar
  14. 14.
    A.R. Miller, T.J. Kelley, HortScience 24, 650 (1989)Google Scholar
  15. 15.
    J.A. Abbott, A.R. Miller, T.A. Campbell, J. Am. Soc. Hortic. Sci. 116, 52 (1991)Google Scholar
  16. 16.
    D.P. Ariana, R. Lu, D.E. Guyer, Comput. Electron. Agric. 53, 60 (2006). doi: 10.1016/j.compag.2006.04.001 CrossRefGoogle Scholar
  17. 17.
    X. Cheng, Y.R. Chen, Y. Tao, C.Y. Wang, M.S. Kim, A.M. Lefcourt, Trans. ASAE 47, 1313 (2004)Google Scholar
  18. 18.
    C.J. Clark, V.A. McGlone, R.B. Jordan, Postharv. Biol. Technol. 28, 87 (2003). doi: 10.1016/S0925-5214(02)00122-9 CrossRefGoogle Scholar
  19. 19.
    D.P. Ariana, R. Lu, Sens. Instrum. Food Qual. Saf (2008). doi: 10.1007/s11694-008-9058-9
  20. 20.
    K.C. Lawrence, B. Park, W.R. Windham, C. Mao, Trans. ASAE 46, 513 (2003)Google Scholar
  21. 21.
    R. Lu, Y.R. Chen, Proc. SPIE 3544, 121 (1998). doi: 10.1117/12.335771 CrossRefGoogle Scholar
  22. 22.
    Y.K. Peng, R. Lu, Postharv. Biol. Technol. 41, 266 (2006). doi: 10.1016/j.postharvbio.2006.04.005 CrossRefGoogle Scholar
  23. 23.
    J. Duckworth, in Near-infrared Spectroscopy in Agriculture, ed. by C.A. Roberts, J. Jerry Workman, J.B.R. III (American Society of Agronomy, Inc., Crop Science Society of America, Inc., Soil Science Society of America, Inc., Wisconsin, 2004), Chapter 6, pp. 115–132Google Scholar
  24. 24.
    B. Park, W.R. Windham, K.C. Lawrence, D.P. Smith, Biosyst. Eng. 96, 323 (2007). doi: 10.1016/j.biosystemseng.2006.11.012 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Sugarbeet and Bean Research Unit, USDA Agricultural Research ServiceMichigan State UniversityEast LansingUSA

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