Spatial frequency domain imaging for detecting bruises of pears

Original Paper
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Abstract

A spatial frequency domain imaging system (SFDI) was developed to estimate the optical properties of biological samples. The system was calibrated by using a series of self-made solid phantoms which covering a wide range of absorption (µ a ) and reduced scattering coefficients (\({\mu ^{\prime}_s}\)). The relative errors between the reference and calibrated values were regarded as the evaluation parameters for validation effectiveness of the system, the results showed that the maximum relative errors of µ a and \({\mu ^{\prime}_s}\) are 3.92 and 2.39% respectively. Quantitative absorption and scattering maps at the wavelength of 527 nm were obtained for four kinds of pear samples (normal and bruised with three levels of bruised severity with impact energy of 0.025, 0.075 and 0.125 J). The normal pears could be distinguished from the bruised pears by comparing values of coefficient of variation (CV) of \({\mu ^{\prime}_s}\) maps, and the correct rates of the discriminant analysis were 100% for non-bruised pears,and 98.33% for bruised pears. Bruises induced using 0.025 J (class 1) were distinguishable from those induced using 0.075 and 0.125 J (class 2) by comparing ratio between mean values of bruised region and normal region of \({\mu ^{\prime}_s}\) maps, the results turned out that the class 1 had the correct rate of 90%, and class 2 had the correct rate of 87.5%. The research showed that SFDI has the potential to be used in the detection of bruises on ‘Crown’ pears and the proposed calibration method lay the foundation for future studies. Future work will be conducted with an emphasis on the acquisition of more spectral information, surface profile correction and the acceleration of detection speed.

Keywords

Spatial frequency domain imaging Absorption Scattering Pears Bruise 

Notes

Acknowledgements

The authors appreciate the financial support from the National Key Research and Development Program (2016YFD0700203), the National Natural Science Fund of China (31401289), and the Science Foundation of Zhejiang Sci-Tech University (ZSTU) (Grant No. 16022177-Y).

Compliance with ethical standards

Conflict of interest

Xueming He, Xiaping Fu, Tingwei Li and Xiuqin Rao declares that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human or animal subjects.

References

  1. 1.
    R. Lu, Detection of bruises on apples using near-infrared hyperspectral imaging. Trans. ASAE. 46(2), 523–530 (2003)Google Scholar
  2. 2.
    J.B. Li, W.Q. Huang, C.J. Zhao, Machine vision technology for detecting the external defects of fruits—a review. J. Photogr. Sci. 63(5), 241–251 (2014)Google Scholar
  3. 3.
    P. Martinsen, R. Oliver, R. Seelye, V.A. Mcglone, T. Holmes, M. Davy, W.J. Jason, I.C. Hallett, K. Moynihan, Quantifying the diffuse reflectance change caused by fresh bruises on apples. Trans. ASABE 57(2), 565–572 (2014)Google Scholar
  4. 4.
    B.M. Nicolaï, B.E. Verlinden, M. Desmet, S. Saevels, W. Saeys, K. Theron, R. Cubeddu, A. Pifferi, A. Torricelli, Time-resolved and continuous wave NIR reflectance spectroscopy to predict soluble solids content and firmness of pear. Postharvest Biol. Technol. 47(1), 68–74 (2008)CrossRefGoogle Scholar
  5. 5.
    V. Tuchin, Tissue Optics: Light Scattering Methods and Instruments for Medical Diagnosis, 2nd edn. (SPIE, Bellingham, 2000)Google Scholar
  6. 6.
    F. Bot, M. Anese, M.A. Lemos, G. Hungerford, Use of time-resolved spectroscopy as a method to monitor carotenoids present in tomato extract obtained using ultrasound treatment. Phytochem. Anal. 27(1), 32–40 (2016)CrossRefGoogle Scholar
  7. 7.
    S. Lurie, M. Vanoli, A. Dagar, A. Weksler, F. Lovati, P.E. Zerbini, L. Spinelli, A. Torricelli, J. Feng, A. Rizzolo, Chilling injury in stored nectarines and its detection by time-resolved reflectance spectroscopy. Postharvest Biol. Technol. 59(3), 211–218 (2011)CrossRefGoogle Scholar
  8. 8.
    A. Torricelli, L. Spinelli, M. Vanoli, A. Rizzolo, P.E. Zerbini, Time-resolved reflectance spectroscopy for nondestructive assessment of fruit and vegetable quality. In Optics East (2007)Google Scholar
  9. 9.
    M. Vanoli, A. Rizzolo, M. Grassi, A. Farina, A. Pifferi, L. Spinelli, B.E. Verlinden, A. Torricelli, Non destructive detection of brown heart in ‘Braeburn’ apples by time-resolved reflectance spectroscopy. Procedia Food Sci. 1(1), 413–420 (2011)CrossRefGoogle Scholar
  10. 10.
    M. Vanoli, P.E. Zerbini, A. Rizzolo, L. Spinelli, A. Torricelli, M. Erkan, U. Aksoy, Time-resolved reflectance spectroscopy for the non-destructive detection of inner attributes and defects of fruit. Acta Hortic. 877(2009), 1379–1386 (2010)CrossRefGoogle Scholar
  11. 11.
    Z.P. Eccher, M. Vanoli, M. Grassi, A. Rizzolo, M. Fibiani, G. Biscotti, A. Pifferi, A. Torricelli, R. Cubeddu, Time-Resolved Reflectance Spectroscopy as a non-destructive tool to assess the maturity at harvest and to model the softening of nectarines. Acta Hortic. 682, 1459–1464 (2005)CrossRefGoogle Scholar
  12. 12.
    A. Torricelli, L. Spinelli, D. Contini, M. Vanoli, A. Rizzolo, P.E. Zerbini, Time-resolved reflectance spectroscopy for non-destructive assessment of food quality. J. Food Meas. Charact. 2(2), 82–89 (2008)Google Scholar
  13. 13.
    M. Vanoli, A. Rizzolo, M. Grassi, A. Farina, A. Pifferi, L. Spinelli, A. Torricelli, Time-resolved reflectance spectroscopy nondestructively reveals structural changes in ‘Pink Lady ®;’ apples during storage. Procedia Food Sci. 1, 81–89 (2011)CrossRefGoogle Scholar
  14. 14.
    E. Herremans, E. Bongaers, P. Estrade, E. Gondek, M. Hertog, E. Jakubczyk, N.N.D. Trong, A. Rizzolo, W. Saeys, L. Spinelli, Microstructure–texture relationships of aerated sugar gels: novel measurement techniques for analysis and control. Innov. Food Sci. Emerg. Technol. 18(2), 202–211 (2013)CrossRefGoogle Scholar
  15. 15.
    J.J. Xia, E.P. Berg, J.W. Lee, G. Yao, Characterizing beef muscles with optical scattering and absorption coefficients in VIS-NIR region. Meat Sci 75, 78–83 (2007)CrossRefGoogle Scholar
  16. 16.
    N.N.D. Trong, C. Erkinbaev, M. Tsuta, J.D. Baerdemaeker, B. Nicolaï, W. Saeys, (2014). Spatially resolved diffuse reflectance in the visible and near-infrared wavelength range for non-destructive quality assessment of ‘Braeburn’ apples. Postharvest Biol. Technol. 91, 39–48CrossRefGoogle Scholar
  17. 17.
    N.N.D. Trong, A. Rizzolo, E. Herremans, M. Vanoli, G. Cortellino, C. Erkinbaev, M. Tsuta, L. Spinelli, D. Contini, A. Torricelli, Optical properties—microstructure–texture relationships of dried apple slices: spatially resolved diffuse reflectance spectroscopy as a novel technique for analysis and process control. Innov. Food Sci. Emerg. Technol. 24(4), 160–168 (2013)Google Scholar
  18. 18.
    L. Baranyai, M. Zude, Analysis of laser light propagation in kiwifruit using backscattering imaging and Monte Carlo simulation. Comput. Electron. Agric. 69(1), 33–39 (2009)CrossRefGoogle Scholar
  19. 19.
    D. Lorente, M. Zude, C. Regen, L. Palou, J. Gómez-Sanchis, J. Blasco, Early decay detection in citrus fruit using laser-light backscattering imaging. Postharvest Biol. Technol. 86(8), 424–430 (2013)CrossRefGoogle Scholar
  20. 20.
    H. Cen, R. Lu, F. Mendoza, R.M. Beaudry, Relationship of the optical absorption and scattering properties with mechanical and structural properties of apple tissue. Postharvest Biol. Technol. 85(11), 30–38 (2013)CrossRefGoogle Scholar
  21. 21.
    H. Cen, R. Lu, F.A. Mendoza, Analysis of absorption and scattering spectra for assessing the internal quality of apple fruit. Acta Hortic. 945(945), 181–188 (2012)Google Scholar
  22. 22.
    H. Cen, R. Lu, F.A. Mendoza, D.P. Ariana, Assessing multiple quality attributes of peaches using optical absorption and scattering properties. Trans. ASABE. 55(2), 647–657 (2012)CrossRefGoogle Scholar
  23. 23.
    Q. Zhu, C. He, R. Lu, F. Mendoza, H. Cen, Ripeness evaluation of ‘Sun Bright’ tomato using optical absorption and scattering properties. Postharvest Biol. Technol. 103, 27–34 (2015)Google Scholar
  24. 24.
    X. He, X. Fu, X. Rao, Z. Fang, (2016). Assessing firmness and SSC of pears based on absorption and scattering properties using an automatic integrating sphere system from 400 to 1150 nm. Postharvest Biol. Technol. 121, 62–70CrossRefGoogle Scholar
  25. 25.
    A. López-Maestresalas, B. Aernouts, R.V. Beers, S. Arazuri, C. Jarén, J.D. Baerdemaeker, W. Saeys, Bulk optical properties of potato flesh in the 500–1900 nm range. Food Bioprocess Technol. 9(3), 1–8 (2016)CrossRefGoogle Scholar
  26. 26.
    P.I. Rowe, R. Künnemeyer, A. Mcglone, S. Talele, P. Martinsen, R. Seelye, Relationship between tissue firmness and optical properties of ‘Royal Gala’ apples from 400 to 1050 nm. Postharvest Biol. Technol. 94, 89–96 (2014)CrossRefGoogle Scholar
  27. 27.
    W. Wang, C. Li, R.D. Gitaitis, Optical properties of healthy and diseased onion tissues in the visible and near-infrared spectral region. Trans. ASABE 57(6), 1771–1782 (2014)Google Scholar
  28. 28.
    E. Zamora-Rojas, B. Aernouts, A. Garrido-Varo, D. Pérez-Marín, J.E. Guerrero-Ginel, W. Saeys, Double integrating sphere measurements for estimating optical properties of pig subcutaneous adipose tissue. Innov. Food Sci. Emerg. Technol. 19(4), 218–226 (2013)CrossRefGoogle Scholar
  29. 29.
    D.J. Cuccia, F. Bevilacqua, A.J. Durkin, B.J. Tromberg, Modulated imaging: quantitative analysis and tomography of turbid media in the spatial-frequency domain. Opt. Lett. 30(11), 1354–1356 (2005)CrossRefGoogle Scholar
  30. 30.
    D.J. Cuccia, F. Bevilacqua, A.J. Durkin, F.R. Ayers, B.J. Tromberg, Quantitation and mapping of tissue optical properties using modulated imaging. J. Biomed. Optics 14(2), 444–448 (2009)CrossRefGoogle Scholar
  31. 31.
    E.R. Anderson, D.J. Cuccia, A.J. Durkin, Detection of bruises on golden delicious apples using spatial-frequency-domain imaging. Proc. SPIE 6430, 36 (2007)Google Scholar
  32. 32.
    Y. Lu, R. Li, R. Lu, Structured-illumination reflectance imaging (SIRI) for enhanced detection of fresh bruises in apples. Postharvest Biol. Technol. 117, 89–93 (2016)CrossRefGoogle Scholar
  33. 33.
    B.W. Pogue, M.S. Patterson, Review of tissue simulating phantoms for optical spectroscopy, imaging and dosimetry. J. Biomed. Optics 11(4), 041102 (2006)CrossRefGoogle Scholar
  34. 34.
    C. Mätzler, (2002). MATLAB functions for Mie scattering and absorptionGoogle Scholar
  35. 35.
    R. Lu, H. Cen, M. Huang, D.P. Ariana, Spectral absorption and scattering properties of normal and bruised apple tissue. Trans. ASABE. 53(1), 263–269 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouPeople’s Republic of China
  2. 2.Faculty of Mechanical Engineering and AutomationZhejiang Sci-Tech UniversityHangzhouPeople’s Republic of China
  3. 3.Key Laboratory of On Site Processing Equipment for Agricultural ProductsMinistry of AgricultureHangzhouPeople’s Republic of China

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