[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
:: Volume 10, Issue 40 (2019) ::
joc 2019, 10(40): 1-8 Back to browse issues page
Development of quality index method of Goldlined seabream Rhabdosargus sarba stored at refrigerator
Milad Ahmadi Shalhe, Ainaz Khodanazary Dr , Seyyed Mehdi Hosseini Dr
Assistant professor, Department of Fisheries, Faculty of Marine Natural Resources, Khorramshahr University of Marine Science and Technology , khodanazary@yahoo.com
Abstract:   (1218 Views)
A quality index method scheme for Goldlined seabream Rhabdosargus sarba stored at refrigerator was developed and its efficiency for freshness evaluation was compared with colorimeter, microbial and physicochemical methods. The quality index method results indicated a shelf life of 9 days. Physicochemical (TVBN, pH, TBARS and FFA) microbiological (mesophilic, psychrophilic and Enterobacteriaceae) and sensory analysis were carried out at 0, 3, 6, 9 and 12 days of storage. Variations in TVBN, pH, TBARS and FFA were observed throughout the storage period. Sensory analysis attributes exhibited significant variations and correlations with time storage, which is a showing of the fish´ loss of freshness. QI showed a linear relationship to storage time (QIM= 8.23× storage time-7.23, R2= 0.988), and the shelf life of Goldlined seabream could be estimated with an accuracy of ± 3 days. A regression analysis using the acceptability limit mesophilic counts (7 log cfu/g) showed that shelf life for Goldlined seabream Rhabdosargus sarba stored at refrigerator was 9 days. TVBN, pH, TBARS, microbiology, color and sensory analysis displayed very strong correlations with storage time, and they may be considered suitable indicators for evaluating of shelf life of Goldlined seabream stored at refrigerator.
Keywords: Rhabdosargus sarba, Quality index method, Refrigerator.
Full-Text [PDF 741 kb]   (303 Downloads)    
Type of Study: Research | Subject: Marine Biology
Received: 2020/05/14 | Accepted: 2020/05/14 | ePublished: 2020/05/14
1. Danehkar, A., 1994. Study on Sirik region mangroves. Master Thesis, Tarbiat Modarres University, Noor. (in Persian).
2. Danehkar, A., 2006. Management and development plan of Mangrove forests in Hormozgan province. First volume. Natural Resources Office of Hormozgan Province: Nature and Natural Resources Consulting Engineers. (in Persian).
3. Safyari, Sh., 2017. Mangrove Forests in Iran. Nature of Iran, (2) 2: 49-57. (in Persian).
4. Safyari, Sh. and Mansouri, M., 2008. Development of Mangrove Forests, Publications of the Forestry, Rangeland and Watershed Organization of Iran Natural Resources Office of Hormozgan Province, 498 p. (in Persian).
5. Erfani, M., Nouri, Gh., Danehkar, A., Mohajer Maravi, M. and Mahmoudi, B., 2009. Study of mangrove forests vegetation parameters in Goater Bay in southeastern Iran. Taxonomy and Biosystematics Journal, (1) 1: 33-46. (in Persian).
6. Alongi D. M., Perillo G. M. E., Wolanski E., Cahoon D. R. and Brinson M. M., 2009. Paradigm shifts in mangrove biology. Coastal Wetlands: An integrated ecosystem approach. Elsevier, Londres, Inglaterra, 615-640.
7. Austin, M.P. (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling. 157, 101-118. [DOI:10.1016/S0304-3800(02)00205-3]
8. Bosso, L., Scelza, R., Varlese, R., Meca, G., Testa, A., Rao, M. A., and Cristinzio, G., 2016. Assessing the effectiveness of Byssochlamys nivea and Scopulariopsis brumptii in pentachlorophenol removal and biological control of two Phytophthora species. Fungal biology, 120:4, 645-653.‏ [DOI:10.1016/j.funbio.2016.01.004]
9. Brandt, A. R., Heath, G. A., Kort, E. A., O'sullivan, F., Pétron, G., Jordaan, S. M and Wofsy, S., 2014. Methane leaks from North American natural gas systems. Science, 343(6172), 733-735. [DOI:10.1126/science.1247045]
10. Carrillo-Angeles, I. G., Suzán-Azpiri, H., Mandujano, M. C., Golubov, J., Martínez-Ávalos, J. G., 2016. Niche breadth and the implications of climate change in the conservation of the genus Astrophytum (Cactaceae). Journal of Arid Environments, 124, 310-317. [DOI:10.1016/j.jaridenv.2015.09.001]
11. Crase, B., Vesk, P., V., Liedloff, A. and Wintle, B., A., 2015. Modelling both dominance and species distribution provides a more complete picture of changes to mangrove ecosystems under climate change. Global Change Biology 21:8, 3005-3020. [DOI:10.1111/gcb.12930]
12. Dudik, M., Philips, S. J., and Shapire, R. E., 2004. A maximum entropy approach to species distribution modelling. In Proceedings of the 21st International Conference on Machine Learning. [DOI:10.1145/1015330.1015412]
13. Faith,D.P., 1992. Conservation evaluation and phylogenetic diversity. Biol. Conserv., 61, 1-10. [DOI:10.1016/0006-3207(92)91201-3]
14. Giovannelli, J. F., Idier, J., Muller, D., and Desodt, G., 2001. Regularized adaptive long autoregressive spectral analysis. IEEE transactions on Geoscience and Remote Sensing, 39:10, 2194-2202.‏ [DOI:10.1109/36.957282]
15. Graham, C.H., Ron, S.R., Santos, J.C., Schneider, C.J. and Moritz, C., 2004. Integrating phylogenetics and environmental niche models to explore speciation mechanisms in dendrobatid frogs. Evolution. 58, 1781-1793. [DOI:10.1111/j.0014-3820.2004.tb00461.x]
16. Guisan, A. and Zimmermann, N.E., 2000. Predictive habitat distribution models in ecology. Ecological Modelling. 135, 147-186. [DOI:10.1016/S0304-3800(00)00354-9]
17. Guisan, A. and Thuiller, W., 2005. Predicting species distribution: offering more than simple habitat models. Ecology Letters. 8, 993-1009. [DOI:10.1111/j.1461-0248.2005.00792.x]
18. Guisan, A., Tingley, R., Baumgartner, J. B., Naujokaitis‐Lewis, I., Sutcliffe, P. R., Tulloch, A. I., Tracey J. Regan., Brotons, L., McDonald‐Madden, E., Martin, T.G., Mantyka‐Pringle, C., Rhodes, J. R., Maggini, R., Setterfield, S. A., Elith. J., Schwartz, M.W., Wintle, B.A., Broennimann. O., Austin. M., Ferrier. S., Kearney, M.R., H.P. Possingham., Buckley. Y. M., and Martin, T. G., 2013. Predicting species distributions for conservation decisions. Ecology letters, 16(12), 1424-1435.‏ [DOI:10.1111/ele.12189]
19. Hannah, L., Midgley, G., Andelman, S., Araújo, M., Hughes, G., Martinez-Meyer, E., Richard, P., and Williams, P., 2007. Protected area needs in a changing climate. Frontiers in Ecology and the Environment, 5(3), 131-138.‏ [DOI:10.1890/1540-9295(2007)5[131:PANIAC]2.0.CO;2]
20. ITTO (the International Tropical Timber Organization)., 2012. Tropical Forest Update. Newsletter: 21(2). Last accessed on 22 July 2017 at URl: http://www.itto.int/tfu/id=2890.
21. Martínez-Meyer, E., Peterson, A.T. and Navarro-Sigüenza, A.G., 2004. Evolution of seasonal ecological niches in the Passerina buntings (Aves: Cardinalidae). Proceedings of the Royal Society of London B: Biological Sciences, 271, 1151-1157. [DOI:10.1098/rspb.2003.2564]
22. McCarty, J. P., Wolfenbarger, L. L. and Wilson, J. A., 2009. Biological Impacts of Climate Change.Encyclopedia of Life Sciences (ELS). Chichester: John Wiley and Sons, Ltd. DOI: 10.1002/9780470015902.a0020480. [DOI:10.1002/9780470015902.a0020480]
23. Nitto, D. D., Neukermans, G.,Koedam, N., Defever, H., Pattyn, F., Kairo, J. G., and Dahdouh-Guebas, F., 2014. Mangroves facing climate change: landward migration potential in response to projected scenarios of sea level rise. Biogeosciences, 11(3), 857-871.‏ [DOI:10.5194/bg-11-857-2014]
24. Peterson, A.T., 2006. Uses and requirements of ecological niche models and related distributional models. Biodiversity Informatics. 3, 59-72. [DOI:10.17161/bi.v3i0.29]
25. Phillips, S. J. Anderson, R. P. Schapire, R. E., 2006. Maximum entropy modeling of species geographic distributions. Ecological models. 190, 231-259. [DOI:10.1016/j.ecolmodel.2005.03.026]
26. Redding, D.W and Mooers, A.O., 2006. Incorporating evolutionary measures into conservation prioritization. Conservation Biology, 20, 1670-1678. [DOI:10.1111/j.1523-1739.2006.00555.x]
27. Renner, I. W., and Warton, D. I., 2013. Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology. Biometrics, 69(1), 274-281.‏ [DOI:10.1111/j.1541-0420.2012.01824.x]
28. Robinson, L. M., Elith, J., Hobday, A. J., Pearson, R. G., Kendall, B. E., Possingham, H. P., and Richardson, A. J., 2011. Pushing the limits in marine species distribution modelling: lessons from the land present challenges and opportunities. Global Ecology and Biogeography, 20(6), 789-802.‏ [DOI:10.1111/j.1466-8238.2010.00636.x]
29. Royle, J. A., Chandler, R. B., Yackulic, C., & Nichols, J. D., 2012. Likelihood analysis of species occurrence probability from presence‐only data for modelling species distributions. Methods in Ecology and Evolution, 3 (3), 545-554.‏ [DOI:10.1111/j.2041-210X.2011.00182.x]
30. Shahparian M, Fakheran S, Moradi H, Hemami M, Shafiezadeh M. Modeling Habitat Suitability of the Dolphins Using MaxEnt in Makran Sea, South of Iran. joc. 2017; 7 (28) :47-56. (in Persian). [DOI:10.18869/acadpub.joc.7.28.47]
31. Singh, H. S., 2003. Vulnerability and adaptability of Tidal forests in response to climate change in India.Indian forester, Indian for, 129(6): 749-756.
32. Smeraldo, S., Di Febbraro, M., Cirovic, D., Bosso, L., Trbojevic, I. and Russo, D., 2017. Species distribution models as a tool to predict range expansion after reintroduction: A case study on Eurasian beavers (Castor fiber). Journal for nature conservation, 37, 12-20.‏ [DOI:10.1016/j.jnc.2017.02.008]
33. Spalding, M. D., F. Blasco and C. Field., 1997. World Mangrove Atlas. Okinava, Japan: The international Society for Mangrove ecosystem. 178 pp.
34. Spalding, M., Kainuma, M., and Collins, L. 2010., World atlas of mangroves. A collaborative project of ITTO, ISME, FAO, UNEP-WCMC. London, UK: Earthscan, 319 pp.
35. Spalding M, McIvor A, Tonneijck FH, Tol S and van Eijk P., 2014. Mangroves for coastal defence. Guidelines for coastal managers and policy makers. Published by Wetlands International and The Nature Conservancy. 42 pp.
36. Thomas, C.D., Cameron, A., Green, R.E., Bakkenes, M., Beaumont, L.J., Collingham, Y.C., Erasmus, B.F.N., Ferreira de Siqueira, M., Grainger, A., Hannah, L., Hughes, L., Huntley,B., van Jaarsveld, A.S., Midgley, G.F., Miles, L., Ortega-Huerta, M.A., Townsend Peterson, A.T., Phillips, O.L. and Williams, S.E., 2004. Extinction risk from climate change. Nature. 427, 145-148. [DOI:10.1038/nature02121]
37. Thuiller, W., Richardson, D.M., Pysek, P., Midgley, G.F., Hughes, G.O. and Rouget, M., 2005. Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale. Global Change Biology, 11, 2234-2250. [DOI:10.1111/j.1365-2486.2005.001018.x]
38. Vane-Wright, R. I., Humphries, C. J., and Williams, P. H., 1991. What to protect? -Systematics and the agony of choice. Biological conservation, 55:3, 235-254.‏ [DOI:10.1016/0006-3207(91)90030-D]
39. Yackulic, C. B., Chandler, R., Zipkin, E. F., Royle, J. A., Nichols, J. D., Campbell Grant, E. H., and Veran, S. 2013., Presence‐only modelling using MAXENT: when can we trust the inferences?. Methods in Ecology and Evolution, 4:3, 236-243. 24-31. [DOI:10.1111/2041-210x.12004]
Send email to the article author

Add your comments about this article
Your username or Email:


XML   Persian Abstract   Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Ahmadi Shalhe M, Khodanazary A, Hosseini S M. Development of quality index method of Goldlined seabream Rhabdosargus sarba stored at refrigerator. joc. 2019; 10 (40) :1-8
URL: http://joc.inio.ac.ir/article-1-1523-en.html

Volume 10, Issue 40 (2019) Back to browse issues page
نشریه علمی پژوهشی اقیانوس شناسی Journal of Oceanography
Persian site map - English site map - Created in 0.09 seconds with 30 queries by YEKTAWEB 4282