Food Biophysics

, Volume 14, Issue 1, pp 13–21 | Cite as

Texture of Bananas Submitted to Different Freeze Drying Cycle Applying Scanning Electron Microsocopy with Image Analysis Techniques

  • Agustina Roa Andino
  • Facundo Pieniazek
  • Valeria MessinaEmail author


Prediction of texture in bananas submitted to different freeze drying cycle was investigated applying scanning electron microscopy combined with image analysis technique. Freeze drying was performed at different cycles. Microstructure was analyzed using a Scanning Electron Microscopy; Texture parameters were analyzed by Gray Level Co-Matrix Analysis and by conventional analysis; colour by image analysis and porosity by conventional technique. Micrographs revealed that a higher porous size structure was obtained when freeze drying cycles was performed at shorter cycles. Significant difference (P < 0.0001) were obtained for texture, senescence and porosity. A linear trend with a linear correlation was applied for instrumental vs. image texture. Results showed that image features (contrast, correlation, entropy, energy and homogeneity) correlated with mechanical texture. When short cycles were applied minimum damage on texture and senescence parameters appeared. Prediction of texture can be performed easily as a quantitative and non invasive technique that could be related in future studies for quality.


Image analysis Quality Surface analysis techniques Scanning electron microscopy 


Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.


  1. 1.
    G. Echeverría, A. Cantína, M. Ortiz, I. López, J. Lara, A. Graell, Sci. Hortic. 190, 179–186 (2015)CrossRefGoogle Scholar
  2. 2.
    S. Bakal, G. Sharma, S. Sonawane, R. Verma, J. Food Sci. Technol. 49, 608–613 (2012)CrossRefGoogle Scholar
  3. 3.
    S. Devahastin, C. Niamnu, Int. J. Food Sci. Technol. 45, 1755–1767 (2010)CrossRefGoogle Scholar
  4. 4.
    A. Etsey, E. Sakyi-Dawson, S. Sefa-Dedeh, E. Afoakwa, K. Tano-Debrah, G. Annor, Afr. J. Biotechnol. 6, 799–802 (2007)Google Scholar
  5. 5.
    A. Mujumdar, C. Law, Trends and applications in postharvest processing. Food Bioprocess Technol. 3, 843–852Google Scholar
  6. 6.
    A. Szczesnia, Food Qual. Prefer. 13, 215–225 (2002)CrossRefGoogle Scholar
  7. 7.
    J. Russ, Image analysis of food microstructure (CRC LLC, Boca Raton, 2005), pp. 1–20Google Scholar
  8. 8.
    R. Haralick, K. Shanmuham, I. Dinstein, IEEE Trans. Systems Man Cybernet 3, 610–621 (1973)CrossRefGoogle Scholar
  9. 9.
    N. Rojas, Inv. Agraria 37, 68–74 (2017)Google Scholar
  10. 10.
    G. Guangchun, Z. Shengying, Z. Cui, Y. Xiaobin, L. Ziqiao, J. Comput. Theor. Nanosci. 12, 2937–2942 (2015)CrossRefGoogle Scholar
  11. 11.
    H. Peng, W. Xiaoqing, W. Chenglin, J. Yingpu, Int. J. Innov. Comput. 10, 67–80 (2014)Google Scholar
  12. 12.
    T. Brosnan, D. Sun, J. Food Eng. 61, 3–16 (2004)CrossRefGoogle Scholar
  13. 13.
    S. Dubey, A. Jalal, J. Intell. Syst. 1, 2–15 (2014)Google Scholar
  14. 14.
    F. Pieniazek, A. Sancho, V. Messina, J. Food Process. Preserv. (2015).
  15. 15.
    V. Messina, A. Sancho, F. Pieniazek, Int. J. Food Sci. Technol. 51, 1268–1275 (2015)CrossRefGoogle Scholar
  16. 16.
    S. Kono, I. Kawamura, S. Yamagami, T. Araki, Y. Sagara, Int. J. Refrig. 56, 165–172 (2015)CrossRefGoogle Scholar
  17. 17.
    B. Koc, I. Eren, F. Kaymak Ertiken, J. Food Eng. 85, 340–349 (2008)CrossRefGoogle Scholar
  18. 18.
    X. Ou, W. Pan, P. Xiao, Int. J. Pharm. 460, 28–32 (2014)CrossRefGoogle Scholar
  19. 19.
    A. Laddi, S. Sharma, A. Kumar, P. Kapur, Food Eng 115, 226–231 (2013)CrossRefGoogle Scholar
  20. 20.
    M. Karimi, M. Fathi, Z. Sheykholeslam, B. Sahraiyan, F. Naghipoor, Biotechnol. Bioprocess Eng. 2, 2–7 (2012)Google Scholar
  21. 21.
    P. Pieniazek, V. Messina, Int. J. Food (2016).
  22. 22.
    H. Kiani, D. Sun, Trends Food Sci. Technol. 22, 407–426 (2011)CrossRefGoogle Scholar
  23. 23.
    R. Islam, A. Kalam Azad, A. Ratikanta Haldar, R. Shahidur, JBERR 3, 16–20 (2016)Google Scholar
  24. 24.
    Ó. Rodríguez, J. Santacatalina, S. Simal, V. Garcia-Perez, A. Femenia, C. Rosselló, J. Food Eng. 129, 21–29 (2014)CrossRefGoogle Scholar
  25. 25.
    B. Schulze, M. Hubbermann, K. Schwarz, LWT Food Sci. Technol. 57, 426–433 (2014)CrossRefGoogle Scholar
  26. 26.
    S. Eim, D. Urrea, C. Rosselló, V. García-Pérez, A. Femenia, S. Simal, Dry. Technol. 31, 951–962 (2013)CrossRefGoogle Scholar
  27. 27.
    R. Mousavi, T. Miri, P. Cox, P. Fryer, Int. J. Food Sci. Technol. 42, 714–727 (2007)CrossRefGoogle Scholar
  28. 28.
    G. Rajkumar, S. Shanmugam, M. Sousa Galvao, R. Monizete Dutra Sandes, N. Santos Leite, T. Narendra Narain, A. Mujumdar, LWT Food Sci. Technol. 80, 501–509 (2015)CrossRefGoogle Scholar
  29. 29.
    S. Mounir, Dry. Technol. 33, 1369–1381 (2015)CrossRefGoogle Scholar
  30. 30.
    A. Heredia, I. Peinado, C. Barrera, A. Andres, J. Food Compos. Anal. 22, 285–294 (2009)CrossRefGoogle Scholar
  31. 31.
    N. Phisut, IFRJ 19, 7–18 (2012)Google Scholar

Copyright information

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

Authors and Affiliations

  • Agustina Roa Andino
    • 1
  • Facundo Pieniazek
    • 2
  • Valeria Messina
    • 1
    • 2
    Email author
  1. 1.CINSO - UNIDEF (Strategic I & D for Defense)- MINDEF-CITEDEF-CONICETBuenos AiresArgentina
  2. 2.The National Council for Scientific and Technical Research (CONICET)Buenos AiresArgentina

Personalised recommendations