Smart sensor to predict retail fresh fish quality under ice storage

Antonacopoulos, 1960, Verbesserte apparatur zur quantitativen destillation wasserdampfflüchtiger stoffe, Z. für Lebensm. Forsch., 113, 113, 10.1007/BF01858931 Baixas-Nogueras, 2001, Trimethylamine and total volatile basic nitrogen determination by flow injection/gas diffusion in mediterranean hake (Merluccius merluccius), J. Agric. Food Chem., 49, 1681, 10.1021/jf000649n Balsa-Canto, 2010, An iterative identification procedure for dynamic modeling of biochemical networks, BMC Syst. Biol., 4, 1, 10.1186/1752-0509-4-11 Balsa-Canto, 2016, Amigo2, a toolbox for dynamic modeling, optimization and control in systems biology, Bioinformatics, 10.1093/bioinformatics/btw411 Baranyi, 1994, A dynamic approach to predicting bacterial growth in food, Int. J. Food Microbiol., 23, 277, 10.1016/0168-1605(94)90157-0 Bhadra, 2015, Non-destructive detection of fish spoilage using a wireless basic volatile sensor, Talanta, 134, 718, 10.1016/j.talanta.2014.12.017 Bonilla, 2007, Development of quality index method (QIM) scheme for fresh cod (Gadus morhua) fillets and application in shelf life study, Food Control, 18, 352, 10.1016/j.foodcont.2005.10.019 Busschaert, 2010, Estimating distributions out of qualitative and (semi) quantitative microbiological contamination data for use in risk assessment, Int. J. Food Microbiol., 138, 260, 10.1016/j.ijfoodmicro.2010.01.025 Chaillou, 2015, Origin and ecological selection of core and food-specific bacterial communities associated with meat and seafood spoilage, ISME J., 9, 1105, 10.1038/ismej.2014.202 Cheng, 2015, Recent advances in methods and techniques for freshness quality determination and evaluation of fish and fish fillets: a review, Crit. Rev. Food Sci. Nutr., 55, 1012, 10.1080/10408398.2013.769934 Council Regulation(EC) No 2406/96, 1996, Laying down common marketing standards for certain fishery products, OJ L, 334, 1 Dalgaard, 1995, Modelling of microbial activity and prediction of shelf life for packed fresh fish, Int. J. Food Microbiol., 26, 305, 10.1016/0168-1605(94)00136-T Dalgaard, 2000 García, 2015, Quality and shelf-life prediction for retail fresh hake (Merluccius merluccius), Int. J. Food Microbiol., 208, 65, 10.1016/j.ijfoodmicro.2015.05.012 Giuffrida, 2013, A new approach to modelling the shelf life of gilthead seabream (Sparus aurata), Int. J. Food Sci. Technol., 48, 1235, 10.1111/ijfs.12082 Gram, 2002, Fish spoilage bacteria–problems and solutions, Curr. Opin. Biotechnol., 13, 262, 10.1016/S0958-1669(02)00309-9 Heising, 2014, Non-destructive sensing of the freshness of packed cod fish using conductivity and ph electrodes, J. Food Eng., 124, 80, 10.1016/j.jfoodeng.2013.10.008 Heising, 2012, A non-destructive ammonium detection method as indicator for freshness for packed fish: application on cod, J. Food Eng., 110, 254, 10.1016/j.jfoodeng.2011.05.008 Heising, 2014, Mathematical models for the trimethylamine (tma) formation on packed cod fish fillets at different temperatures, Food Res. Int., 56, 272, 10.1016/j.foodres.2014.01.011 Howgate, 2010, A critical review of total volatile bases and trimethylamine as indices of freshness of fish. part 2. formation of the bases, and application in quality assurance, Elec. J. Env. Agric. Food Chem., 9, 58 Huang, 2015, Non-destructive evaluation of total volatile basic nitrogen (tvb-n) and k-values in fish using colorimetric sensor array, Anal. Methods, 7, 1615, 10.1039/C4AY02623C Huss, 1995 International Commission on Microbial Specifications for Foods (ICMSF), 1986 Jørgensen, 1988, Microbiological quality and shelf life prediction of chilled fish, Int. J. Food Microbiol., 6, 295, 10.1016/0168-1605(88)90023-2 Koutsoumanis, 2002, Application of shelf life decision system (slds) to marine cultured fish quality, Int. J. Food Microbiol., 73, 375, 10.1016/S0168-1605(01)00659-6 Koutsoumanis, 2000, Application of a systematic experimental procedure to develop a microbial model for rapid fish shelf life predictions, Int. J. Food Microbiol., vol. 60, 171, 10.1016/S0168-1605(00)00309-3 Liu, 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, 142, 10.1016/j.lwt.2014.09.030 Lücke, 1935, Bestimmung des flüchtigen basischen Stickstoffs in Fischen als Maßstab für ihren Frischezustand, Z. für Leb. und Forschung A, 70, 441 Luten, 1997, QIM: a European tool for fish freshness evaluation in the fishery chain, 287 McMeekin, 1992, Application of predictive microbiology to assure the quality and safety of fish and fish products, Int. J. Food Microbiol., 15, 13, 10.1016/0168-1605(92)90132-M Nuin, 2008, Modelling spoilage of fresh turbot and evaluation of a time–temperature integrator (tti) label under fluctuating temperature, Int. J. Food Microbiol., 127, 193, 10.1016/j.ijfoodmicro.2008.04.010 Oehlenschläger, 1998, Detailed experimental iced-storage characteristics of barents sea cod (Gadus morhua), Informationen für Fischwirtsch. aus Fisch., 45, 35 Ruiz-Capillas, 2001, Correlation between biochemical and sensory quality indices in hake stored in ice, Food Res. Int., 34, 441, 10.1016/S0963-9969(00)00189-7 Saguy, 2016, Challenges and opportunities in food engineering: modeling, virtualization, open innovation and social responsibility, J. Food Eng., 176, 2, 10.1016/j.jfoodeng.2015.07.012 Sivertsen, 2011, Automatic freshness assessment of cod (gadus morhua) fillets by vis/nir spectroscopy, J. Food Eng., 103, 317, 10.1016/j.jfoodeng.2010.10.030 Taoukis, 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, 21, 10.1016/S0168-1605(99)00142-7 Tsironi, 2009, Shelf life modelling of frozen shrimp at variable temperature conditions, LWT Food Sci. Technol., 42, 664, 10.1016/j.lwt.2008.07.010 Vilas, 2016, Towards predictive food process models: a protocol for parameter estimation, Crit. Rev. Food Sci. Nutr., 10.1080/10408398.2016.1186591 Vogel, 2005, Identification of Shewanella baltica as the most important H2 S-producing species during iced storage of danish marine fish, Appl. Environ. Microbiol., 71, 6689, 10.1128/AEM.71.11.6689-6697.2005 Walter, 1997 Wang, 2015, Modeling quality changes in brined bream (megalobrama amblycephala) fillets during storage: comparison of the arrhenius model, bp, and rbf neural network, Food Bioprocess Technol., 8, 2429, 10.1007/s11947-015-1595-8 Yao, 2011, Establishment of kinetic models based on electrical conductivity and freshness indictors for the forecasting of crucian carp (Carassius carassius) freshness, J. Food Eng., 107, 147, 10.1016/j.jfoodeng.2011.06.034