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FTIR-based rapid microbial quality estimation of fresh-cut jackfruit (Artocarpus heterophyllus) bulbs

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Abstract

FTIR in combination with chemometric tools was utilised to evaluate the microbial counts for fresh cut jackfruit samples stored at 4 and 10 °C. Predictive models were prepared for total viable counts and yeast & mould counts using partial least square regression (PLS-R) and artificial neural networks (ANN) from FTIR data. Raw FTIR data and its first derivative were exploited for model building. Models built with both ANN and PLS-R using FTIR data demonstrated a high correlation value of R2 > 0.85 for 10 °C stored samples. Variable importance projection score obtained from PLS-R models suggested production of acids after utilization of sugars due to microbial activity during storage. Feasibility of utilising FTIR as a rapid non-destructive methodology for estimation of microbial counts for fresh cut jackfruit is demonstrated.

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Data availability

The raw FTIR data is available in data repository https://data.mendeley.com//datasets/2rvxddkhy8/1

Code availability

Not applicable.

References

  1. APAARI, Jackfruit Improvement in the Asia-Pacific Region—A Status Report (Asia-Pacific Association of Agricultural Research Institutions, Bangkok, Thailand, 2012)

    Google Scholar 

  2. M.S. Arfin, M.N. Islam, M.F.B. Hossain, M.A. Hossain, M.A. Rahman, Bull. Inst. Trop. Agric. (2016). https://doi.org/10.11189/bita.39.075

    Article  Google Scholar 

  3. V. Adiani, S. Gupta, R. Padole, P.S. Variyar, A. Sharma, Postharvest Biol. Technol. (2014). https://doi.org/10.1016/j.postharvbio.2014.07.006

    Article  Google Scholar 

  4. V. Adiani, S. Gupta, R. Ambolikar, P.S. Variyar, Sensor Actuat B-Chem. (2018). https://doi.org/10.1016/j.snb.2018.01.095

    Article  Google Scholar 

  5. V. Adiani, S. Gupta, P.S. Variyar, Sci. Rep. (2020). https://doi.org/10.1038/s41598-020-62895-y

    Article  PubMed  PubMed Central  Google Scholar 

  6. U. Siripatrawan, B.R. Harte, Anal. Chim. Acta (2007). https://doi.org/10.1016/j.aca.2006.08.007

    Article  PubMed  Google Scholar 

  7. G. Elmasry, M. Kamruzzaman, D.-W. Sun, P. Allen, Crit. Rev. Food Sci. (2012). https://doi.org/10.1080/10408398.2010.543495

    Article  Google Scholar 

  8. E. Baldwin, J. Bai, A. Plotto, S. Dea, Sensors (2011). https://doi.org/10.3390/s110504744

    Article  PubMed  PubMed Central  Google Scholar 

  9. L. Huang, J. Zhao, Q. Chen, Y. Zhang, Food Res. Int. (2013). https://doi.org/10.1016/j.foodres.2013.08.011

    Article  Google Scholar 

  10. K. Arshak, E. Moore, G.M. Lyons, J. Harris, S. Clifford, Sensor Rev. (2004). https://doi.org/10.1108/02602280410525977

    Article  Google Scholar 

  11. C. Ferraria, G. Focaa, A. Ulrici, Anal. Chim. Acta (2013). https://doi.org/10.1016/j.aca.2013.10.009

    Article  Google Scholar 

  12. E. Birkel, L.E. Rodriguez-Saona, J. Am. Oil Chem.’ Soc. (2011). https://doi.org/10.1007/S11746-011-1814-Z

    Article  Google Scholar 

  13. D.I. Ellis, R. Goodacre, Trends Food Sci. Technol. (2001). https://doi.org/10.1016/S0924-2244(02)00019-5

    Article  Google Scholar 

  14. D.I. Ellis, D. Broadhurst, R. Goodacre, Anal. Chim. Acta (2004). https://doi.org/10.1016/j.aca.2004.03.060

    Article  Google Scholar 

  15. V. Di Egidio, N. Sinelli, S.L. Limbo, T.L. Franzetti, Postharvest Biol. Technol. (2009). https://doi.org/10.1016/j.postharvbio.2009.06.006

    Article  Google Scholar 

  16. J. Baranyi, T.A. Robert, Int. J. Food Microbiol. (1994). https://doi.org/10.1016/0168-1605(94)90157-0

    Article  PubMed  Google Scholar 

  17. S. Bureau, D. Cozzolino, C.J. Clark, Postharvest Biol. Technol. (2019). https://doi.org/10.1016/j.postharvestbio.2018.10.003

    Article  Google Scholar 

  18. G.F Mohamed, M.S Shaheen, S.K.H. Khalil, A.M.S. Hussein, M. KamilMohie (2011) Nat. Sci. http://www.sciencepub.net/nature.

  19. H. Vardin, A. Tay, B. Ozen, L. Mauer, Food Chem. (2008). https://doi.org/10.1016/j.foodchem.2007.11.027

    Article  PubMed  Google Scholar 

  20. M. Kacurakova, M. Mathlouthi, Carbohydr. Res. (1996). https://doi.org/10.1016/0008-6215(95)00412-2

    Article  PubMed  Google Scholar 

  21. H. Kodad, R. Mokhlisse, E. Davin, G. Mille, Can. J. Appl. Spectrosc. 39, 107 (1994)

    Google Scholar 

  22. M. Ammor, A. Argyri, G. Nychas, Meat Sci. (2009). https://doi.org/10.1016/j.meatsci.2008.10.015

    Article  PubMed  Google Scholar 

  23. F. Duarte, A.N. Barros, I. Delgadillo, C.U. Almeida, A.M. Gil, J. Agric. Food Chem. (2002). https://doi.org/10.1021/jf011575y

    Article  PubMed  Google Scholar 

  24. P. Ragaert, F. Devlieghere, E. Devuyst, J. Dewulf, H. Van Langenhove, J. Debevere, Int. J. Food Microbiol. (2006). https://doi.org/10.1016/j.ijfoodmicro.2006.06.018

    Article  PubMed  Google Scholar 

  25. O.J. Caleb, U.L. Opara, P.W. Mahajan, M. Manley, L. Mokwena, A.G.J. Tredoux, Postharvest Biol. Tech. (2013). https://doi.org/10.1016/j.postharvbio.2013.01.006

    Article  Google Scholar 

  26. A. Argyri, A. Mallouchos, E. Panagou, G. Nychas, Int. J. Food Microbiol. (2015). https://doi.org/10.1016/j.ijfoodmicro.2014.09.020

    Article  PubMed  Google Scholar 

  27. S. Lek, Y.S. Park, Artificial neural networks, in Encyclopedia of Ecology. ed. by S.E. Jorgensen, B. Fath (Elsevier, Amsterdam, 2008), p. 237

    Chapter  Google Scholar 

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Funding

This research was possible by utilizing the resources of Bhabha Atomic Research Center, India.

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Authors and Affiliations

  1. Food Technology Division, Bhabha Atomic Research Centre, Mumbai, India

    Vanshika Adiani & Sumit Gupta

  2. Homi Bhabha National Institute, Anushakti Nagar, Mumbai, India

    Vanshika Adiani & Prasad S.Variyar

  3. Food Technology Division, Food Flavor and Aroma Chemistry Section, BARC, Mumbai, 400085, India

    Vanshika Adiani

Authors
  1. Vanshika Adiani
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  2. Sumit Gupta
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  3. Prasad S.Variyar
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Correspondence to Vanshika Adiani.

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Adiani, V., Gupta, S. & S.Variyar, P. FTIR-based rapid microbial quality estimation of fresh-cut jackfruit (Artocarpus heterophyllus) bulbs. Food Measure 16, 1944–1951 (2022). https://doi.org/10.1007/s11694-022-01312-6

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  • DOI: https://doi.org/10.1007/s11694-022-01312-6

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