Characterization of the temperature fluctuation effect on shelf life of an octopus semi-preserved product
https://doi.org/10.4081/ijfs.2020.8590
Accepted: 8 January 2020
HTML: 26
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
The aim of this work is to study the effect of temperature fluctuations on spoilage microbial flora behaviour of a semi-preserved seafood product in modified atmosphere packaging (MAP) as well as to find correct interpretation criteria for simulating temperature fluctuations during storage tests. The study concerned 54 packages of “Octopus carpaccio” that were grouped in three batches and stored at 3 different temperature profiles: the first (16 packages - Group 4°C) was stored at 4±0.5°C; the second (16 packages - Group 8°C) was stored at 8±0.5°C; the third (16 packages - Group F) was stored under a fluctuating temperature regime between 2°C and 14°C. Spoilage microflora, pH and AW has been monitored, at regular intervals, along the storage period (44 days). A predictive model was constructed according to the accredited scientific literature and validated against the observed growth curves of the above three groups. Afterwards, the predictive model has been used setting the temperature at the mean value of fluctuations (6.72°C), at the kinetic mean value of fluctuations (7.80°C) and at the 75th percentile value of fluctuations (11.14°C). The best fitting to the observed data was obtained with the kinetic mean temperature value and this result shows that this parameter can be proposed to reproduce the temperature fluctuation along the distribution and the domestic storage when a storage test has to be carried out.
Aponte M, Anastasio A, Marrone R, Mercogliano R, Peruzy M, Murru N, 2018. Impact of gaseous ozone coupled to passive refrigeration system to maximize shelf-life and quality of four different fresh fish products. LWT Food Sci. Technol., 93: 412–419. DOI: https://doi.org/10.1016/j.lwt.2018.03.073
Baranyi J, Roberts TA, 1994. A dynamic approach to predicting microbial growth in food. Int J Food Microbiol 23:277-294. DOI: https://doi.org/10.1016/0168-1605(94)90157-0
Corradini MG, 2018. Shelf Life of Food Products: From Open Labeling to Real-Time Measurements. Annu Rev Food Sci T 9: 251-269. DOI: https://doi.org/10.1146/annurev-food-030117-012433
Dalgaard P, 1995. Modelling of microbial activity and prediction of shelf life of packed fresh fish. Int J Food Microbiol 19:305-318. DOI: https://doi.org/10.1016/0168-1605(94)00136-T
Dalgaard P, Mejlholm O, Huss HH, 1997. Application of an iterative approach for development of a microbial model predicting the shelf life of packed fish. Int J Food Microbiol 19:169-179. DOI: https://doi.org/10.1016/S0168-1605(97)00101-3
European Commission, 2005. Commission Regulation (EC) No 2073/2005 of 15 November 2005 on microbiological criteria for foodstuffs. In: Official Journal, L 338/1, 22.12.2005.
European Parliament and of the Council, 2011. Regulation of 25 October 2011 on the provision of food information to consumers, amending Regulations (EC) No 1924/2006 and (EC) No 1925/2006 of the European Parliament and of the Council, and repealing Commission Directive 87/250/EEC, Council Directive 90/496/EEC, Commission Directive 1999/10/EC, Directive 2000/13/EC of the European Parliament and of the Council, Commission Directives 2002/67/EC and 2008/5/EC and Commission Regulation (EC) No 608/2004, 1169/2011/EU. In: Official Journal, L 304/18, 22.11.2011
Fu B, Labuza TP, 1993. Shelf-life prediction: theory and application. Food Control 4:125-133. DOI: https://doi.org/10.1016/0956-7135(93)90298-3
Giarratana F, Crinò C, Muscolino D, Beninati C, Ziino G, Giuffrida A, Panebianco A, 2015. Preliminary investigation on the use of allyl isothiocyanate to increase the shelf-life of gilthead sea bream (Sparus aurata) fillets. Ital J Food Saf 4:172-174. DOI: https://doi.org/10.4081/ijfs.2015.4512
Giarratana F, Muscolino D, Beninati C, Ziino G, Giuffrida A, Panebianco A, 2016. Activity of R(+) limonene on the maximum growth rate of fish spoilage organisms and related effects on shelf-life prolongation of fresh gilthead sea bream fillets. Int J Food Microbiol 237:109-13. DOI: https://doi.org/10.1016/j.ijfoodmicro.2016.08.023
Giuffrida A, Valenti D, Giarratana F, Ziino G, Panebianco A, 2013. A new approach to modelling the shelf life of Gilthead seabream (Sparus aurata). Int J Food Sci Technol 48: 1235-42. DOI: https://doi.org/10.1111/ijfs.12082
Giuffrida A, Ziino G, Valenti D, Donato G, Panebianco A, 2007. Application of an interspecific competition model to predict the growth of Aeromonas hydrophila on fish surfaces during refrigerated storage. Archiv Lebensmittelhyg 58(4): 136-141.
Giuffrida A, Giarratana F, Valenti D, Muscolino D, Parisi R, Parco A, Marotta S, Ziino G, Panebianco A, 2017. A new approach to predict the fish fillet shelf-life in presence of natural preservative agents. Ital J Food Saf 6: 88-92. DOI: https://doi.org/10.4081/ijfs.2017.6768
Giuffrida A, Valenti D, Ziino G, Spagnolo B, Panebianco A, 2009. A stochastic interspecific competition model to predict the behaviour of Listeria monocytogenes in the fermentation process of a traditional Sicilian salami. Euro Food Res Technol 228(5): 767-775. DOI: https://doi.org/10.1007/s00217-008-0988-6
Gram L, Trolle G, Huss HH, 1987. Detection of specific spoilage bacteria from fish stored at low (0°C) and high (20°C) temperatures. Int J Food Microbiol 4:65-72. DOI: https://doi.org/10.1016/0168-1605(87)90060-2
ISO, 2010. Meat and meat products — Enumeration of presumptive Pseudomonas spp. ISO Norm 13720:2010. International Standardization Organization Geneva, Switzerland.
ISO, 2017. Microbiology of the food chain - Horizontal methods for the detection and enumeration of Enterobacteriaceae Part 2: Colony-count method. ISO Norm 21528-2:2017. International Standardization Organization Geneva, Switzerland.
James C, Onarinde BA, James SJ, 2017. The Use and Performance of Household Refrigerators: A Review. Compr Rev Food Sci S 16:160-179. DOI: https://doi.org/10.1111/1541-4337.12242
Khalafalla FA, Ali F, Hassan HA 2015. Quality improvement and shelf-life extension of refrigerated Nile tilapia (Oreochromis niloticus) fillets using natural herbs. Beni-Seuf Univ J Appl Sci 4:33-40. DOI: https://doi.org/10.1016/j.bjbas.2015.02.005
Koutsoumanis K, Nychas GJE, 2000. Application of a systematic experimental procedure to develop a microbial model for rapid fish shelf life predictions. Int J Food Microbiol 60: 171-84. DOI: https://doi.org/10.1016/S0168-1605(00)00309-3
Martinez-Rios V, Bjerre Østergaard N, Gkogka E, Sand Rosshaug P, Dalgaard P, 2016. Modelling and predicting growth of psychrotolerant pseudomonads in milk and cottage cheese. Int J Food Microbool 216: 110–120. DOI: https://doi.org/10.1016/j.ijfoodmicro.2015.09.020
McClure PJ, Cole MB, Davies KW, 1994. An example of the stages in the development of a predictive mathematical model for microbial growth: the effects of NaCl, pH and temperature on the growth of Aeromonas hydrophila. Int J Food Microbiol 23:359-375 DOI: https://doi.org/10.1016/0168-1605(94)90163-5
Mejlholm O, Dalgaard P, 2002. Antimicrobial effect of essential oils on the seafood spoilage micro-organism Photobacterium phosphoreum in liquid media and fish products. Lett App Microbiol 34: 27-31. DOI: https://doi.org/10.1046/j.1472-765x.2002.01033.x
Muscolino D, Giarratana F, Beninati C, Ziino G, Giuffrida A, Panebianco A, 2016. Effects of allyl isothiocyanate on the shelf-life of gilthead sea bream (Sparus aurata) fillets. Czech J Food Sci 34: 160-165. DOI: https://doi.org/10.17221/374/2015-CJFS
Nunes MCN, Nicometo M, ´Emond JP, Melis RB, Uysal I. 2014. Improvement in fresh fruit and vegetable logistics quality: Berry logistics field studies. Philos Trans R Soc A: Math Phys Eng Sci 372:20130307. DOI: https://doi.org/10.1098/rsta.2013.0307
Pelletier W, Brecht JK, Nunes MCN, ´Emond JP, 2011. Quality of strawberries shipped by truck from California to Florida as influenced by postharvest temperature management practices. Hort Technology 21:482–93. DOI: https://doi.org/10.21273/HORTTECH.21.4.482
Pin C, Baranyi J, 1998. Predictive models as means to quantify the interactions of spoilage organisms. Int J Food Microbiol 41: 59-72. DOI: https://doi.org/10.1016/S0168-1605(98)00035-X
Raab V, Bruckner S, Beierle E, Kampmann Y, Petersen B, Kreyenschmidt J, 2008. Generic model for the prediction of remaining shelf life in support of cold chain management in pork and poultry supply chain. J Chain Netw Sci 8:59–73. DOI: https://doi.org/10.3920/JCNS2008.x089
Ratkowsky DA, Lowry RK, McMeekin TA, Stokes AN, Chandler RE 1983. Model for bacterial culture growth rate throughout the entire biokinetic temperature range. J Bacteriol 154(3): 1222-1226. DOI: https://doi.org/10.1128/JB.154.3.1222-1226.1983
Ratkowsky DA, Ross T, Macario N, Dommett TW, Kamperman L, 1996. Choosing probability distributions for modelling generation time variability. J Appl Bacteriol 80:131-137. DOI: https://doi.org/10.1111/j.1365-2672.1996.tb03200.x
Renard CM, Ginies C, Gouble B, Bureau S, Causse M. 2013 Home conservation strategies for tomato (Solanum lycopersicum): storage temperature vs. duration--is there a compromise for better aroma preservation? Food Chem. 139:825-36. DOI: https://doi.org/10.1016/j.foodchem.2013.01.038
Roccato A, Uyttendaele M, Membré JM, 2017. Analysis of domestic refrigerator temperatures and home storage time distributions for shelf-life studies and food safety risk assessment. Food Res Int 96:171-181. DOI: https://doi.org/10.1016/j.foodres.2017.02.017
Vergara A, Festino AR, Di Ciccio P, Costanzo C, Pennisi L, Ianieri A, 2014. The management of the domestic refrigeration: Microbiological status and temperature. Br Food J 116(6):1047-1057. DOI: https://doi.org/10.1108/BFJ-05-2012-0103
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Downloads
Citations
