Prediction of the Growth Rates of Pseudomonas sp. in Seafood Based on Artificial Neural Network (ANN) Model

References

• Alak G, Aras Hisar S, Hisar O, Kaban G, Kaya M. 2010. Microbiological and chamical properties of Bonito fish (Sarda sarda) fillets packaged chitosan film, modified atmosphere and vacuum. Kafkas Univ Vet Fak Derg. 16(1):73–80.
   Google Scholar
• Albertos I, Martin-Diana AB, Cullen PJ, Tiwari BK, Ojha KS, Bourke P, Rico D. 2019. Shelf-life extension of herring (Clupea harengus) using in-package atmospheric plasma technology. Innov Food Sci Emerg Technol. 53:85–91.
   Web of Science ®Google Scholar
• Amoussou N, Marengo M, Iko Afe OH, Lejeune P, Durieux ÉDH, Douny C, Scippo ML, Gobert S. 2022. Comparison of fatty acid profiles of two cultivated and wild marine fish from Mediterranean Sea. Aquac Int. 30:1–18.
   Web of Science ®Google Scholar
• Antunes-Rohling A, Artaiz Á, Calero S, Halaihel N, Guillén S, Raso J, Alvarez I, Cebrián G. 2019. Modelling microbial growth in modified-atmosphere-packed hake (Merluccius merluccius) fillets stored at different temperatures. Food Res Int. 122:506–16.
   PubMed Web of Science ®Google Scholar
• Baranyi J, Pin C, Ross T. 1999. Validating and comparing predictive models. Int J Food Microbiol. 48:159–66.
   PubMed Web of Science ®Google Scholar
• Baranyi J, Roberts TA, McClure P. 1993. A non-autonomous differential equation to model bacterial growth. Food Microbiol. 10(1):43–59.
   Web of Science ®Google Scholar
• Beale MH, Hagan MT, Demuth HB. 2010. Neural Network Toolbox ™ 7 MATLAB. Natick (MA): The Math Works Inc.
   Google Scholar
• Boziaris IS. 2014. Introduction to seafood processing – assuring quality and safety of seafood. In: Boziaris IS, editor. Seafood processing technology, qaulity and safety. John Wiley & Sons. p. 1–7. doi:10.1002/9781118346174.ch1.
   Google Scholar
• Boziaris IS, Parlapani FF. 2017. Specific spoilage organisms (SSOs) in fish. In: Bevilacqua A, and Corbo MR, editors The microbiological quality of food. Cambridge (UK): Woodhead Publishing. p. 61–98.
   Google Scholar
• Chai T, Draxler RR. 2014. Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geosci Model Dev. 7(3):1247–50.
   Web of Science ®Google Scholar
• Chen Y, Wang X, Zhang X, Xu D, Zhang W, Qiu J, Liu Q, Dong Q. 2020. Modeling the interactions among Salmonella enteritidis, Pseudomonas aeruginosa, and Lactobacillus plantarum. J Food Saf. 40(4):e12811.
   Web of Science ®Google Scholar
• Costa JCCP, Floriano B, Villegas IMB, Rodríguez-Ruiz JP, Posada-Izquierdo GD, Zurera G, Pérez-Rodríguez F. 2020. Study of the microbiological quality, prevalence of foodborne pathogens and product shelf-life of Gilthead sea bream (Sparus aurata) and Sea bass (Dicentrarchus labrax) from aquaculture in estuarine ecosystems of Andalusia (Spain). Food Microbiol. 90:103498.
   PubMed Web of Science ®Google Scholar
• Cyprian O, Lauzon HL, Jóhannsson R, Sveinsdóttir K, Arason S, Martinsdóttir E. 2012. Shelf life of air and modified atmosphere‐packaged fresh tilapia (Oreochromis niloticus) fillets stored under chilled and superchilled conditions. Food Sci Nutr. 1(2):130–40.
   PubMedGoogle Scholar
• Dalgaard P. 1995. Modelling of microbial activity and prediction of shelf life for packed fresh fish. Int J Food Microbiol. 26(3):305–17.
   PubMed Web of Science ®Google Scholar
• Dalgaard P. 2000. Fresh and lightly preserved seafood. In: Man CMD, Jones AA, editors. Shelf life evaluation of foods. 2nd ed. Maryland (USA): Aspen Publishing Inc. p. 110–39.
   Google Scholar
• Dalgaard P, Koutsoumanis K. 2001. Comparison of maximum specific growth rates and lag times estimated from absorbance and viable count data by different mathematical models. J Microbiol Methods. 43(3):183–96.
   PubMed Web of Science ®Google Scholar
• Del Nobile MA, Corbo MR, Speranza B, Sinigaglia M, Conte A, Caroprese M. 2009. Combined effect of MAP and active compounds on fresh blue fish burger. Int J Food Microbiol. 135(3):281–87.
   PubMed Web of Science ®Google Scholar
• Drosinos EH, Lambropoulou K, Mitre E, Nychas GJ. 1997. Attributes of fresh gilt‐head seabream (Sparus aurata) fillets treated with potassium sorbate, sodium gluconate and stored under a modified atmosphere at 0±1° C. J Appl Microbiol. 83(5):569–75.
   Web of Science ®Google Scholar
• Dufresne I, Smith JP, Liu JN, Tarte I, Blanchfield B, Austin JW. 2000. Effect of headspace oxygen and films of different oxygen transmission rate on toxin production by Clostridium botulinum type E in rainbow trout fillets stored under modified atmospheres. J Food Saf. 20(3):157–75.
   Web of Science ®Google Scholar
• Françoise L, Joffraud JJ. 2011. Microbial degradation of seafood. In: Montet D, and Ray RC, editors. Aquaculture microbiology and biotechnology. Vol. 2. Enfield (USA). CRC Press. p. 47–72.
   Google Scholar
• Ge Y, Zhu J, Ye X, Yang Y. 2017. Spoilage potential characterization of Shewanella and Pseudomonas isolated from spoiled large yellow croaker (Pseudosciaena crocea). Lett Appl Microbiol. 64(1):86–93.
   PubMed Web of Science ®Google Scholar
• Giarratana F, Nalbone L, Ziino G, Giuffrida A, Panebianco F. 2020. Characterization of the temperature fluctuation effect on shelf life of an octopus semi-preserved product. Ital J Food Saf. 9(1). doi:10.4081/ijfs.2020.8590.
   PubMed Web of Science ®Google Scholar
• Gram L, Dalgaard P. 2002. Fish spoilage bacteria–problems and solutions. Curr Opin Biotechnol. 13(3):262–66.
   PubMed Web of Science ®Google Scholar
• Han F, Huang X, Teye E, Gu H. 2015. Quantitative analysis of fish microbiological quality using electronic tongue coupled with nonlinear pattern recognition algorithms. J Food Saf. 35(3):336–44.
   Web of Science ®Google Scholar
• Hansen AÅ, Moen B, Rødbotten M, Berget I, Pettersen MK. 2016. Effect of vacuum or modified atmosphere packaging (MAP) in combination with a CO2 emitter on quality parameters of cod loins (Gadus morhua). Food Packag Shelf Life. 9:29–37.
   Web of Science ®Google Scholar
• Hiura S, Koseki S, Koyama K. 2021. Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database. Sci Rep. 11(1):1–11.
   PubMed Web of Science ®Google Scholar
• Huang Y, Kangas LJ, Rasco BA. 2007. Applications of artificial neural networks (ANNs) in food science. Crit Rev Food Sci Nutr. 47(2):113–26.
   PubMed Web of Science ®Google Scholar
• Khair U, Fahmi H, Al Hakim S, Rahim R. 2017, December. Forecasting error calculation with mean absolute deviation and mean absolute percentage error. J Phys. 930(1):012002. IOP Publishing. doi:10.1088/1742-6596/930/1/012002.
   Google Scholar
• Koutsoumanis K. 2001. Predictive modeling of the shelf life of fish under nonisothermal conditions. Appl Environ Microbiol. 67(4):1821–29.
   PubMed Web of Science ®Google Scholar
• Koutsoumanis K, Giannakourou MC, Taoukis PS, Nychas GJE. 2002. Application of shelf life decision system (SLDS) to marine cultured fish quality. Int J Food Microbiol. 73(2–3):375–82.
   PubMed Web of Science ®Google Scholar
• Koutsoumanis K, Lampropoulou K, Nychas GJE. 1999. Biogenic amines and sensory changes associated with the microbial flora of Mediterranean gilt-head sea bream (Sparus aurata) stored aerobically at 0, 8, and 15 C. J Food Protect. 62(4):398–402.
   PubMed Web of Science ®Google Scholar
• Koutsoumanis K, Nychas GJE. 2000a. Application of a systematic experimental procedure to develop a microbial model for rapid fish shelf life predictions. Int J Food Microbiol. 60(2–3):171–84.
   PubMed Web of Science ®Google Scholar
• Koutsoumanis KP, Taoukis PS, Drosinos EH, Nychas GJE. 2000b. Applicability of an Arrhenius model for the combined effect of temperature and CO2 packaging on the spoilage microflora of fish. Appl Environ Microbiol. 66(8):3528–34.
   PubMed Web of Science ®Google Scholar
• Kritikos A, Aska I, Ekonomou S, Mallouchos A, Parlapani FF, Haroutounian SA, Boziaris IS. 2020. Volatilome of chill-stored european seabass (Dicentrarchus labrax) fillets and atlantic salmon (Salmo salar) slices under modified atmosphere packaging. Molecules. 25(8):1981.
   PubMed Web of Science ®Google Scholar
• Kuuliala L, Al Hage Y, Ioannidis AG, Sader M, Kerckhof FM, Vanderroost M, Boon N, De Baets B, De Meulenaer B, Ragaert P, et al. 2018. Microbiological, chemical and sensory spoilage analysis of raw Atlantic cod (Gadus morhua) stored under modified atmospheres. Food Microbiol. 70:232–44.
   PubMed Web of Science ®Google Scholar
• Lalabadi HM, Sadeghi M, Mireei SA. 2020. Fish freshness categorization from eyes and gills color features using multi-class artificial neural network and support vector machines. Aquac Eng. 90:102076.
   Web of Science ®Google Scholar
• Lerfall J, Thomassen GMB, Jakobsen AN. 2018. Quality of fresh saithe (Pollachius virens) in modified atmosphere packages as affected by the gas composition. Food Packag Shelf Life. 18:147–56.
   Web of Science ®Google Scholar
• Levin RE. 2009. Assessment of seafood spoialage and the microorganisms involved. In: Nollet NML, Toldrá F, editors. Handbook of seafood and seafood product analysis. CRC Press Taylor & Francis Group New York. p. 516–32. doi:10.1201/9781420046359-c28.
   Google Scholar
• Li P, Chen Z, Tan M, Mei J, Xie J. 2020. Evaluation of weakly acidic electrolyzed water and modified atmosphere packaging on the shelf life and quality of farmed puffer fish (Takifugu obscurus) during cold storage. J Food Safe. 40(3):e12773.
   Web of Science ®Google Scholar
• Liu X, Jiang Y, Shen S, Luo Y, Gao L. 2015. Comparison of Arrhenius model and artificial neuronal network for the quality prediction of rainbow trout (Oncorhynchus mykiss) fillets during storage at different temperatures. LWT Food Sci Technol. 60(1):142–47.
   Web of Science ®Google Scholar
• Mejlholm O, Gunvig A, Borggaard C, Blom-Hanssen J, Mellefont L, Ross T, Leroi F, Else T, Visser D, Dalgaard P. 2010. Predicting growth rates and growth boundary of Listeria monocytogenes—An international validation study with focus on processed and ready-to-eat meat and seafood. Int J Food Microbiol. 141(3):137–50.
   PubMed Web of Science ®Google Scholar
• Murphy AH. 1988. Skill scores based on the mean square error and their relationships to the correlation coefficient. Mon Wea Rev. 116(12):2417–24.
   Web of Science ®Google Scholar
• Neumeyer K, Ross T, McMeekin TA. 1997. Development of a predictive model to describe the effects of temperature and water activity on the growth of spoilage pseudomonads. Int J Food Microbiol. 38(1):45–54.
   PubMed Web of Science ®Google Scholar
• Noseda B, Islam MT, Eriksson M, Heyndrickx M, De Reu K, Van Langenhove H, Devlieghere F. 2012. Microbiological spoilage of vacuum and modified atmosphere packaged Vietnamese Pangasius hypophthalmus fillets. Food Microbiol. 30(2):408–19.
   PubMed Web of Science ®Google Scholar
• Odeyemi OA, Burke CM, Bolch CC, Stanley R. 2018. Seafood spoilage microbiota and associated volatile organic compounds at different storage temperatures and packaging conditions. Int J Food Microbiol. 280:87–99.
   PubMed Web of Science ®Google Scholar
• Paleologos EK, Savvaidis IN, Kontominas MG. 2004. Biogenic amines formation and its relation to microbiological and sensory attributes in ice-stored whole, gutted and filleted Mediterranean Sea bass (Dicentrarchus labrax). Food Microbiol. 21(5):549–57.
   Web of Science ®Google Scholar
• Parlapani FF, Mallouchos A, Haroutounian SA, Boziaris IS. 2014. Microbiological spoilage and investigation of volatile profile during storage of sea bream fillets under various conditions. Int J Food Microbiol. 189:153–63.
   PubMed Web of Science ®Google Scholar
• Parlapani FF, Meziti A, Kormas KA, Boziaris IS. 2013. Indigenous and spoilage microbiota of farmed sea bream stored in ice identified by phenotypic and 16S rRNA gene analysis. Food Microbiol. 33(1):85–89.
   PubMed Web of Science ®Google Scholar
• Parlapani FF, Verdos GI, Haroutounian SA, Boziaris IS. 2015. The dynamics of Pseudomonas and volatilome during the spoilage of gutted sea bream stored at 2° C. Food Control. 55:257–65.
   Web of Science ®Google Scholar
• Poli BM, Messini A, Parisi G, Scappini F, Vigiani V, Giorgi G, Vincenzini M. 2006. Sensory, physical, chemical and microbiological changes in European sea bass (Dicentrarchus labrax) fillets packed under modified atmosphere/air or prepared from whole fish stored in ice. Int J Food Sci Technol. 41(4):444–54.
   Web of Science ®Google Scholar
• Priyadarshini R, Dash N, Swarnkar T, Misra R. 2010. Functional analysis of artificial neural network for dataset classification. Int J Comput Commun. 1(2):49–54.
   Google Scholar
• Renaud O, Victoria-Feser MP. 2010. A robust coefficient of determination for regression. J Stat Plan Inference. 140(7):1852–62.
   Web of Science ®Google Scholar
• Rivai M, Attamimi M, Firdaus MH. 2019 November. Fish quality recognition using electrochemical gas sensor array and neural network. In: 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM); Surabaya, Indonesia: IEEE. p. 1–5.
   Google Scholar
• Rohatgi A. 2021. WebPlotDigitizer, V 4.5, Pasifica, California, USA. https://automeris.io/WebPlotDigitizer.
   Google Scholar
• Ross T. 1996. Indices for performance evaluation of predictive models in food microbiology. J Appl Bacteriol. 81(5):501–08.
   PubMedGoogle Scholar
• Ross T, Dalgaard P, Tienungoon S. 2000. Predictive modelling of the growth and survival of Listeria in fishery products. Int J Food Microbiol. 62:231–45.
   PubMed Web of Science ®Google Scholar
• Schober P, Boer C, Schwarte LA. 2018. Correlation coefficients: Appropriate use and interpretation. Anesth Analg. 126(5):1763–68.
   PubMed Web of Science ®Google Scholar
• Shaohua X, Xiaoshuan Z, Weijun L, Dong T, Jinyou H. 2012. Modeling growth of specific spoilage organisms in tilapia: comparison Baranyi with chi-square automatic interaction detection (CHAID) model. Afr J Biotechnol. 11(26):6910–17.
   Google Scholar
• Shi C, Qian J, Zhu W, Liu H, Han S, Yang X. 2019. Nondestructive determination of freshness indicators for tilapia fillets stored at various temperatures by hyperspectral imaging coupled with RBF neural networks. Food Chem. 275:497–503.
   PubMed Web of Science ®Google Scholar
• Shi X, Zhang J, Shi C, Tan Y, Hong H, Luo Y. 2022. Nondestructive prediction of freshness for bighead carp (Hypophthalmichthys nobilis) head by excitation-emission matrix (EEM) analysis based on fish eye fluid: comparison of BPNNs and RBFNNs. Food Chem. 382:132341.
   PubMed Web of Science ®Google Scholar
• Sørensen JS, Bøknæs N, Mejlholm O, Dalgaard P. 2020a. Superchilling in combination with modified atmosphere packaging resulted in long shelf-life and limited microbial growth in Atlantic cod (Gadus morhua L.) from capture-based-aquaculture in Greenland. Food Microbiol. 88:103405.
   PubMed Web of Science ®Google Scholar
• Sørensen JS, Ørnfeld-Jensen O, Bøknæs N, Mejlholm O, Jessen F, Dalgaard P. 2020. Thawed and chilled Atlantic cod (Gadus morhua L.) from greenland-options for improved distribution. LWT. 131:109473.
   Web of Science ®Google Scholar
• Speranza B, Bevilacqua A, Conte A, Del Nobile MA, Sinigaglia M, Corbo MR. 2013. Use of desirability approach to predict the inhibition of Pseudomonas fluorescens, Shewanella putrefaciens and Photobacterium phosphoreum in fish fillets through natural antimicrobials and modified atmosphere packaging. Food Bioproc Tech. 6(9):2319–30.
   Web of Science ®Google Scholar
• Sun Y, Zhang M, Adhikari B, Devahastin S, Wang H. 2022. Double-layer indicator films aided by BP-ANN-enabled freshness detection on packaged meat products. Food Packag Shelf Life. 31:100808.
   Web of Science ®Google Scholar
• Svozil D, Kvasnicka V, Pospichal J. 1997. Introduction to multi-layer feed-forward neural networks. Chemometr Intel Lab Sys. 39(1):43–62.
   Web of Science ®Google Scholar
• Taoukis PS, Koutsoumanis K, Nychas GJE. 1999. Use of time–temperature integrators and predictive modelling for shelf life control of chilled fish under dynamic storage conditions. Int J Food Microbiol. 53(1):21–31.
   PubMed Web of Science ®Google Scholar
• Te Giffel MC, Zwietering MH. 1999. Validation of predictive models describing the growth of Listeria monocytogenes. Int J Food Microbiol. 46(2):135–49.
   PubMed Web of Science ®Google Scholar
• Thakur A, Anju K, Dhiman NS, Thakur H, Chauhan M, Gautam S. 2019. An introduction to seafood and recent advances in the processing of seafood products. Int Arc Appl Sci Technol. 10(2):169–80.
   Google Scholar
• Tryfinopoulou P, Tsakalidou E, Nychas GJ. 2002. Characterization of Pseudomonas spp. associated with spoilage of gilt-head sea bream stored under various conditions. Appl Environ Microbio. 68(1):65–72.
   PubMed Web of Science ®Google Scholar
• Wang XY, Xie J. 2020. Growth kinetics and spoilage potential of co-culturing Acinetobacter johnsonii and Pseudomonas fluorescens from bigeye tuna (Thunnus obesus) during refrigerated storage. Curr Microbiol. 77(8):1637–46.
   PubMed Web of Science ®Google Scholar
• Wang W, Zhang X, Gombault S, Knapskog SJ. 2009. Attribute normalization in network intrusion detection. In: 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks. Kaohsiung, Taiwan: IEEE. p. 448–53.
   Google Scholar
• Wang H, Zheng Y, Shi W, Wang X. 2022. Comparison of Arrhenius model and artificial neuronal network for predicting quality changes of frozen tilapia (Oreochromis niloticus). Food Chem. 372:131268.
   PubMed Web of Science ®Google Scholar
• Yang X, Liu Y, Chen J, Lv Y, Luo Y. 2018. Quality attributes and shelf life modeling of Pacific white shrimp (Litopenaeus vannamei) stored at different temperatures. J Aquat Food Prod Tech. 27(9):998–1008.
   Web of Science ®Google Scholar
• Zhang Q, Deng D, Dai W, Li J, Jin X. 2020. Optimization of culture conditions for differentiation of melon based on artificial neural network and genetic algorithm. Sci Rep. 10(1):1–8.
   PubMed Web of Science ®Google Scholar