Advertisement

Food Engineering Reviews

, Volume 11, Issue 3, pp 184–199 | Cite as

Estimation of Safety and Quality Losses of Foods Stored in Residential Refrigerators

  • Veronica Rodriguez-Martinez
  • Gonzalo Velazquez
  • Sofia Massa-Barrera
  • Jorge Welti-Chanes
  • Fabian FagottiEmail author
  • J. Antonio TorresEmail author
Article
  • 185 Downloads

Abstract

This article overviews the technological evolution of residential refrigerators, key national and international regulations covering them, and summarizes the information available to estimate the quality and safety deterioration in foods and beverages stored in them. At present, the national and international government standardized performance tests used to assess residential refrigerators focus on energy consumption. Efforts by refrigerator manufacturers to consider the impact of temperature fluctuation, temperature recovery, extreme ambient temperature, door openings, and other factors affecting temperature control and, thus, food safety and quality need to be harmonized, validated, and implemented as official standardized tests. Published predictive models here summarized, and describing microbial growth and other product degradation mechanisms, could be combined with energy efficiency evaluations in future science-based regulations seeking a balance between energy consumption and food preservation. While numerous mathematical models are available, this review identified a serious lack of model parameter values to allow a combined assessment of energy consumption and food preservation in residential refrigerators. Much of past research has focused on temperature abuse effects and, thus, not applicable to estimating food preservation under the prevailing temperatures in residential refrigerators. Particularly urgent is the data on the microorganisms’ response at multiple temperature levels, allowing the development of secondary models to assess the temperature effect on the safety and quality of a diverse but representative pool of products. New standardized testing procedures could then be developed to guide the design of new residential refrigerators minimizing food waste and the frequency of foodborne diseases while meeting energy consumption requirements.

Keywords

Microbial growth Refrigeration Modeling Energy consumption regulations 

Notes

Funding Information

The authors acknowledge the support from Tecnologico de Monterrey (Research chair funds GEE 1A01001 and CDB081) and from Embraco Mexico S de RL de CV.

References

  1. 1.
    Almonacid-Merino SF, Thomas DR, Torres JA (1993) Numerical and statistical methodology to analyze microbial spoilage of refrigerated solid foods exposed to temperature abuse. J Food Sci 58:914–920CrossRefGoogle Scholar
  2. 2.
    Almonacid-Merino SF, Torres JA (1993) Mathematical models to evaluate temperature abuse effects during distribution of refrigerated solid foods. J Food Eng 20:223–245CrossRefGoogle Scholar
  3. 3.
    Almonacid-Merino SF, Torres JA (2010) Uncertainty of microbial shelf-life estimations for refrigerated foods due to the experimental variability of the model parameters. J Food Process Eng 33:66–84CrossRefGoogle Scholar
  4. 4.
    American National Standards Institute (2016) AHAM HRF-1-2016—energy and internal volume of refrigerating appliances. https://blog.ansi.org/2016/11/aham-hrf-1-2016-energy-volume-refrigerating-appliances/#gref. Accessed 16 Mar 2018
  5. 5.
    Asia-Pacific Economic Cooperation Secretariat (2016) Differences/synergies between energy efficiency test methods for refrigerators in APEC region and with the new IEC 62552: desktop research. Asia-Pacific Economic Cooperation Secretariat. http://kms.energyefficiencycentre.org/sites/default/files/EWG%2004%202014A_Desktop%20Research_FINAL-20160314-clean_pdf.pdf. Accessed 09 Sep 2018 2018
  6. 6.
    Ayala-Zavala JF, Wang SY, Wang CY, González-Aguilar GA (2004) Effect of storage temperatures on antioxidant capacity and aroma compounds in strawberry fruit. LWT Food Sci Technol 37:687–695CrossRefGoogle Scholar
  7. 7.
    Azevedo I, Regalo M, Mena C, Almeida G, Carneiro Ĺ, Teixeira P, Hogg T, Gibbs PA (2005) Incidence of Listeria spp. in domestic refrigerators in Portugal. Food Control 16:121–124CrossRefGoogle Scholar
  8. 8.
    Bansal PK (2003) Developing new test procedures for domestic refrigerators: harmonisation issues and future R&D needs—a review. Int J Refrig 26:735–748CrossRefGoogle Scholar
  9. 9.
    Bansal PK, Krüger R (1995) Test standards for household refrigerators and freezers I: preliminary comparisons. Int J Refrig 18:4–20CrossRefGoogle Scholar
  10. 10.
    Baranyi J (1998) Comparison of stochastic and deterministic concepts of bacterial lag. J Theor Biol 192:403–408CrossRefPubMedGoogle Scholar
  11. 11.
    Baranyi J, Roberts TA (1995) Mathematics of predictive food microbiology. Int J Food Microbiol 26:199–218CrossRefPubMedGoogle Scholar
  12. 12.
    Bøgh-Sørensen L, Löndahl G (2005) Temperature indicators and time-temperature integrators. EcoLibrium 2:30–32Google Scholar
  13. 13.
    Bonino D, Corno F, de Russis L (2012) Home energy consumption feedback: a user survey. Energy Buildings 47:383–393CrossRefGoogle Scholar
  14. 14.
    Brown T, Hipps NA, Easteal S, Parry A, Evans JA (2014) Reducing domestic food waste by lowering home refrigerator temperatures. Int J Refrig 40:246–253CrossRefGoogle Scholar
  15. 15.
    Buchanan K, Russo R, Anderson B (2015) The question of energy reduction: the problem(s) with feedback. Energy Policy 77:89–96CrossRefGoogle Scholar
  16. 16.
    Buchanan RL, Stahl HG, Whiting RC (1989) Effects and interactions of temperature, pH, atmosphere, sodium chloride, and sodium nitrite on the growth of Listeria monocytogenes. J Food Prot 52:844–851CrossRefPubMedGoogle Scholar
  17. 17.
    Canadian Standards Association (2015) CAN/CSA-C300-15—energy performance and capacity of household refrigerators, refrigerator-freezers, freezers, and wine chillers. http://shop.csa.ca/en/canada/energy-efficiency/cancsa-c300-15/invt/27013362015. Accessed 16 Mar 2018
  18. 18.
    Cantwell MI, Reid MS (1993) Postharvest physiology and handling of fresh culinary herbs. J Herbs Spices Med Plants 1:93–127CrossRefGoogle Scholar
  19. 19.
    Cárdenas FC, Giannuzzi L, Zaritzky NE (2008) Mathematical modelling of microbial growth in ground beef from Argentina. Effect of lactic acid addition, temperature and packaging film. Meat Sci 79:509–520CrossRefPubMedGoogle Scholar
  20. 20.
    Carpentier B, Lagendijk E, Chassaing D, Rosset P, Morelli E, Noël V (2012) Factors impacting microbial load of food refrigeration equipment. Food Control 25:254–259CrossRefGoogle Scholar
  21. 21.
    Chandler RE, McMeekin TA (1989) Temperature function integration as the basis of an accelerated method to predict the shelf life of pasteurized, homogenized milk. Food Microbiol 6:105–111CrossRefGoogle Scholar
  22. 22.
    China National Standards (2016) GB/T 8059-2016, Household and similar refrigerating appliances. http://www.gbstandards.org/GB_standards/GB_standard.asp?id=60626. Accessed 10 Nov 2018
  23. 23.
    Chotyakul N, Pérez-Lamela C, Torres JA (2012) Effect of model parameter variability on the uncertainty of refrigerated microbial shelf-life estimates. J Food Process Eng 35:829–839CrossRefGoogle Scholar
  24. 24.
    Corbo MR, Altieri C, D’Amato D, Campaniello D, del Nobile MA, Sinigaglia M (2004) Effect of temperature on shelf life and microbial population of lightly processed cactus pear fruit. Postharvest Biol Technol 31:93–104CrossRefGoogle Scholar
  25. 25.
    da Silva NB, Longhi DA, Martins WF, Laurindo JB, de Aragão GMF, Carciofi BAM (2017) Modeling the growth of Lactobacillus viridescens under non-isothermal conditions in vacuum-packed sliced ham. Int J Food Microbiol 240:97–101CrossRefPubMedGoogle Scholar
  26. 26.
    da Silva PRS, Tessaro IC, Marczak LDF (2013) Integrating a kinetic microbial model with a heat transfer model to predict Byssochlamys fulva growth in refrigerated papaya pulp. J Food Eng 118:279–288CrossRefGoogle Scholar
  27. 27.
    Dalgaard P, Huss HH (1997) Mathematical modelling used for evaluation and prediction of microbial fish spoilage. In: Shahidi F, Jones Y, Kitts DD (eds) Seafood safety, processing, and biotechnology. Technomic Publishing Co. Inc., Lancaster, PA, pp 73–90Google Scholar
  28. 28.
    DiMascio M (2014) How your refrigerator has kept its cool over 40 years of efficiency improvements. American Council for an Energy-Efficient Economy (ACEEE), Washington, D.C.Google Scholar
  29. 29.
    Dincer I (2010) Food refrigeration aspects. In: Farid MM (ed) Mathematical modeling of food processing. CRC, Boca Raton, FL, pp 399–451CrossRefGoogle Scholar
  30. 30.
    Fang T, Gurtler JB, Huang L (2012) Growth kinetics and model comparison of Cronobacter sakazakii in reconstituted powdered infant formula. J Food Sci 77:E247–E255CrossRefPubMedGoogle Scholar
  31. 31.
    Fang T, Liu Y, Huang L (2013) Growth kinetics of Listeria monocytogenes and spoilage microorganisms in fresh-cut cantaloupe. Food Microbiol 34:174–181CrossRefPubMedGoogle Scholar
  32. 32.
    Fu D, Taoukis PS, Labuza TP (1991) Predictive microbiology for monitoring spoilage of dairy products with time-temperature integrators. J Food Sci 56:1209–1215CrossRefGoogle Scholar
  33. 33.
    Garrido V, García-Jalón I, Vitas AI (2010) Temperature distribution in Spanish domestic refrigerators and its effect on Listeria monocytogenes growth in sliced ready-to-eat ham. Food Control 21:896–901CrossRefGoogle Scholar
  34. 34.
    Geppert J (2011) Modelling of domestic refrigerators’ energy consumption under real life conditions in Europe. PhD, Rheinischen Friedrich-Wilhelms UniversityGoogle Scholar
  35. 35.
    Geppert J, Stamminger R (2013) Analysis of effecting factors on domestic refrigerators’ energy consumption in use. Energy Convers Manag 76:794–800CrossRefGoogle Scholar
  36. 36.
    Giannuzzi L, Pinotti A, Zaritzky N (1998) Mathematical modelling of microbial growth in packaged refrigerated beef stored at different temperatures. Int J Food Microbiol 39:101–110CrossRefPubMedGoogle Scholar
  37. 37.
    Gustafsson J, Cederberg C, Sonesson U, van Otterdijk R, Maybeck A (2011) Global food losses and food waste—extent, causes and prevention. Food and Agriculture Organization of the United Nations, Rome, ItalyGoogle Scholar
  38. 38.
    Hertzmann P (2016) The refrigerator revolution. Gastronomy Symposium, Dublin, IrelandGoogle Scholar
  39. 39.
    Huang L (2008) Growth kinetics of Listeria monocytogenes in broth and beef frankfurters—determination of lag phase duration and exponential growth rate under isothermal conditions. J Food Sci 73:E235–E242CrossRefPubMedGoogle Scholar
  40. 40.
    Huang L (2010) Growth kinetics of Escherichia coli O157:H7 in mechanically-tenderized beef. Int J Food Microbiol 140:40–48CrossRefPubMedGoogle Scholar
  41. 41.
    Huang L (2015) Direct construction of predictive models for describing growth of Salmonella Enteritidis in liquid eggs—a one-step approach. Food Control 57:76–81CrossRefGoogle Scholar
  42. 42.
    Huang L (2016) Mathematical modeling and validation of growth of Salmonella Enteritidis and background microorganisms in potato salad—one-step kinetic analysis and model development. Food Control 68:69–76CrossRefGoogle Scholar
  43. 43.
    Huang L (2017) Dynamic identification of growth and survival kinetic parameters of microorganisms in foods. Curr Opin Food Sci 14:85–92CrossRefGoogle Scholar
  44. 44.
    Huang L, Hwang C-A (2017) Dynamic analysis of growth of Salmonella Enteritidis in liquid egg whites. Food Control 80:125–130CrossRefGoogle Scholar
  45. 45.
    Huis in’t Veld JHJ (1996) Microbial and biochemical spoilage of foods: an overview. Int J Food Microbiol 33:1–18CrossRefGoogle Scholar
  46. 46.
    International Electrotechnical Commission (2015a) Household refrigerating appliances—characteristics and test methods—part 3: energy consuption and volume. SAI Global. https://infostore.saiglobal.com/en-us/Standards/IEC-62552-3-1ED-2015-568190_SAIG_IEC_IEC_1297112/. Accessed 09 Sep 2018
  47. 47.
    International Electrotechnical Commission (2015b) Household refrigerating appliances—characteristics and test methods—part 1: general requirements. Geneva, SwitzerlandGoogle Scholar
  48. 48.
    International Electrotechnical Commission (2017) IEC 60316:1970—safety requirements for the electrical equipment of refrigerators and food freezers for household and similar purposes. https://webstore.iec.ch/publication/14722. Accessed 14 Nov 2017
  49. 49.
    International Organization for Standardization (2007) ISO 15502:2005/Cor1:2007—household refrigerating appliances—characteristics and test methods. https://www.iso.org/standard/27428.html. Accessed 02 Nov 2018
  50. 50.
    Jacxsens L, Devlieghere F, Debevere J (2002) Temperature dependence of shelf-life as affected by microbial proliferation and sensory quality of equilibrium modified atmosphere packaged fresh produce. Postharvest Biol Technol 26:59–73CrossRefGoogle Scholar
  51. 51.
    Jakobsen M, Bertelsen G (2000) Colour stability and lipid oxidation of fresh beef. Development of a response surface model for predicting the effects of temperature, storage time, and modified atmosphere composition. Meat Sci 54:49–57CrossRefPubMedGoogle Scholar
  52. 52.
    Japanese Standards Association (2015) JIS C 9801:2015 household electric refrigerators, refrigerator-freezers and freezers. https://webdesk.jsa.or.jp/books/W11M0090/index/?bunsyo_id=JIS%20C%209607:2015. Accessed 20 Mar 2018
  53. 53.
    Juneja VK, Valenzuela Melendres M, Huang L, Gumudavelli V, Subbiah J, Thippareddi H (2007) Modeling the effect of temperature on growth of Salmonella in chicken. Food Microbiol 24:328–335CrossRefPubMedGoogle Scholar
  54. 54.
    Koseki S, Isobe S (2005) Growth of Listeria monocytogenes on iceberg lettuce and solid media. Int J Food Microbiol 101:217–225CrossRefPubMedGoogle Scholar
  55. 55.
    Koutsoumanis K, Pavlis A, Nychas G-JE, Xanthiakos K (2010) Probabilistic model for Listeria monocytogenes growth during distribution, retail storage, and domestic storage of pasteurized milk. Appl Environ Microbiol 76:2181–2191CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Koutsoumanis K, Taoukis PS, Nychas GJE (2005) Development of a safety monitoring and assurance system for chilled food products. Int J Food Microbiol 100:253–260CrossRefPubMedGoogle Scholar
  57. 57.
    Kovárová-Kovar K, Egli T (1998) Growth kinetics of suspended microbial cells: from single-substrate-controlled growth to mixed-substrate kinetics. Microbiol Mol Biol Rev 62:646–666PubMedPubMedCentralGoogle Scholar
  58. 58.
    Kuan HW, Wong SE (2012) Lessons for integrated household energy conservation policies from an intervention study in Singapore. Energy Policy 47:49–56CrossRefGoogle Scholar
  59. 59.
    Laguerre O, Derens E, Palagos B (2002) Study of domestic refrigerator temperature and analysis of factors affecting temperature: a French survey. Int J Refrig 25:653–659CrossRefGoogle Scholar
  60. 60.
    Lana MM, Tijskens LMM, van Kooten O (2005) Effects of storage temperature and fruit ripening on firmness of fresh cut tomatoes. Postharvest Biol Technol 35:87–95CrossRefGoogle Scholar
  61. 61.
    Lanzavecchia D Refrigerators, freezers and relevant standards. In: Bertoldi P, Ricci A, Wajer BH (eds) Energy efficiency in household appliances: proceedings of the first international conference on energy efficiency in household appliances, Florence, Italy, November 10–12, 1997 1999. Springer Berlin 218–230Google Scholar
  62. 62.
    Lebert I, Lebert A (2006) Quantitative prediction of microbial behaviour during food processing using an integrated modelling approach: a review. Int J Refrig 29:968–984CrossRefGoogle Scholar
  63. 63.
    Li C, Huang L, Hwang C-A, Chen J (2016) Growth of Listeria monocytogenes in salmon roe—a kinetic analysis. Food Control 59:538–545CrossRefGoogle Scholar
  64. 64.
    Li K, Torres JA (1993a) Effects of temperature and solute on the minimum water activity for growth and temperature characteristic of selected mesophiles and psychrotrophs. J Food Process Preserv 17:305–318CrossRefGoogle Scholar
  65. 65.
    Li K, Torres JA (1993b) Microbial growth estimation in liquid media exposed to temperature fluctuations. J Food Sci 58:644–648CrossRefGoogle Scholar
  66. 66.
    Li M, Huang L, Yuan Q (2017) Growth and survival of Salmonella Paratyphi A in roasted marinated chicken during refrigerated storage: effect of temperature abuse and computer simulation for cold chain management. Food Control 74:17–24CrossRefGoogle Scholar
  67. 67.
    Li Y, Brackett RE, Shewfelt RL, Beuchat LR (2001) Changes in appearance and natural microflora on iceberg lettuce treated in warm, chlorinated water and then stored at refrigeration temperature. Food Microbiol 18:299–308CrossRefGoogle Scholar
  68. 68.
    Luo Y, He Q, McEvoy JL (2010) Effect of storage temperature and duration on the behavior of Escherichia coli O157:H7 on packaged fresh-cut salad containing Romaine and Iceberg lettuce. J Food Sci 75:M390–M397CrossRefPubMedGoogle Scholar
  69. 69.
    Lurie S, Crisosto CH (2005) Chilling injury in peach and nectarine. Postharvest Biol Technol 37:195–208CrossRefGoogle Scholar
  70. 70.
    Mahlia TMI, Saidur R (2010) A review on test procedure, energy efficiency standards and energy labels for room air conditioners and refrigerator–freezers. Renew Sust Energ Rev 14:1888–1900CrossRefGoogle Scholar
  71. 71.
    McDonald K, Sun D-W (1999) Predictive food microbiology for the meat industry: a review. Int J Food Microbiol 52:1–27CrossRefPubMedGoogle Scholar
  72. 72.
    McKellar RC (2001) Development of a dynamic continuous-discrete-continuous model describing the lag phase of individual bacterial cells. J Appl Microbiol 90:407–413CrossRefPubMedGoogle Scholar
  73. 73.
    McKellar RC, Lu X (2003) Modeling microbial responses in food. Contemporary food science. CRC, Boca Raton, FLCrossRefGoogle Scholar
  74. 74.
    McMeekin T, Olley J, Ratkowsky D, Corkrey R, Ross T (2013) Predictive microbiology theory and application: is it all about rates? Food Control 29:290–299CrossRefGoogle Scholar
  75. 75.
    McMeekin TA, Ross T (2002) Predictive microbiology: providing a knowledge-based framework for change management. Int J Food Microbiol 78:133–153CrossRefPubMedGoogle Scholar
  76. 76.
    Ministério do Desenvolvimento Indústria e Comércio Exterior (2015) Portaria n.º 577, ANEXO A – Procedimento para ensaios de avaliação de desempenho dos refrigeradores e assemelhados. Instituto Nacional de Metrologia, Qualidade e Tecnologia -INMETRO. http://www.inmetro.gov.br/legislacao/rtac/pdf/RTAC002335.pdf. Accessed Nov 11 2018
  77. 77.
    Mishra A, Buchanan RL, Schaffner DW, Pradhan AK (2016) Cost, quality, and safety: a nonlinear programming approach to optimize the temperature during supply chain of leafy greens. LWT Food Sci Technol 73:412–418CrossRefGoogle Scholar
  78. 78.
    Montville TJ, Matthews KR (2013) Physiology, growth, and inhibition of microbes in foods. In: Doyle MP, Buchanan RL (eds) Food microbiology: fundamentals and frontiers, 4th edn. ASM Press, Washington, D.C.Google Scholar
  79. 79.
    Nadel S (1997) The future of standards. Energy Buildings 26:119–128CrossRefGoogle Scholar
  80. 80.
    Nagengast B (2004) 100 years of refrigeration: electric refrigerators vital contribution to households. ASHRAE J 46:S11-S15, S18-S19Google Scholar
  81. 81.
    Neumeyer K (1995) Modelling Pseudomonad growth in milk and milk-based products. University of TasmaniaGoogle Scholar
  82. 82.
    Norma Oficial Mexicana (2018) NOM-015-ENER-2018, Eficiencia energética de refrigeradores y congeladores electrodomésticos. Límites, métodos de prueba y etiquetado http://dof.gob.mx/nota_to_doc.php?codnota=5529394. Accessed 08 Sep 2018
  83. 83.
    Nourian F, Ramaswamy HS, Kushalappa AC (2003) Kinetics of quality change associated with potatoes stored at different temperatures. LWT Food Sci Technol 36:49–65CrossRefGoogle Scholar
  84. 84.
    Oscar TP (2007) Predictive models for growth of Salmonella typhimurium DT104 from low and high initial density on ground chicken with a natural microflora. Food Microbiol 24:640–651CrossRefPubMedGoogle Scholar
  85. 85.
    Østergaard NB, Eklöw A, Dalgaard P (2014) Modelling the effect of lactic acid bacteria from starter- and aroma culture on growth of Listeria monocytogenes in cottage cheese. Int J Food Microbiol 188:15–25CrossRefPubMedGoogle Scholar
  86. 86.
    Ozone Secretariat (2009) Handbook for the Vienna convention for the protection of the ozone layer, 8th edn. UNEP-United Nations Environment Programme, Nairobi, KenyaGoogle Scholar
  87. 87.
    Pérez-Rodríguez F, Valero A (2013) Predictive models: foundation, types, and development. In: Predictive microbiology in foods. Springer, New York, NY, pp 25–55CrossRefGoogle Scholar
  88. 88.
    Pham TQ (2014) Refrigeration in food preservation and processing. In: Bhattacharta S (ed) Conventional and advanced food processing technologies. Wiley Blackwell, West Sussex, UK, pp 357–386Google Scholar
  89. 89.
    Pinheiro J, Alegria C, Abreu M, Gonçalves EM, Silva CLM (2013) Kinetics of changes in the physical quality parameters of fresh tomato fruits (Solanum lycopersicum, cv. ‘Zinac’) during storage. J Food Eng 114:338–345CrossRefGoogle Scholar
  90. 90.
    Polydera AC, Stoforos NG, Taoukis PS (2005) Quality degradation kinetics of pasteurised and high pressure processed fresh Navel orange juice: nutritional parameters and shelf life. Innov Food Sci Emerg Technol 6:1–9CrossRefGoogle Scholar
  91. 91.
    Pouillot R, Albert I, Cornu M, Denis J-B (2003) Estimation of uncertainty and variability in bacterial growth using Bayesian inference. Application to Listeria monocytogenes. Int J Food Microbiol 81:87–104CrossRefPubMedGoogle Scholar
  92. 92.
    Raffo A, Nardo N, Tabilio MR, Paoletti F (2008) Effects of cold storage on aroma compounds of white- and yellow-fleshed peaches. Eur Food Res Technol 226:1503–1512CrossRefGoogle Scholar
  93. 93.
    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 154:1222–1226PubMedPubMedCentralGoogle Scholar
  94. 94.
    Ratkowsky DA, Ross T (1995) Modelling the bacterial growth/no growth interface. Lett Appl Microbiol 20:29–33CrossRefGoogle Scholar
  95. 95.
    Rees J (2013) Refrigeration nation: a history of ice, appliances, and enterprise in America. The Johns Hopkins University Press, Baltimore, MDGoogle Scholar
  96. 96.
    Rosso L, Lobry JR, Bajard S, Flandrois JP (1995) Convenient model to describe the combined effects of temperature and pH on microbial growth. Appl Environ Microbiol 61:610–616PubMedPubMedCentralGoogle Scholar
  97. 97.
    Sala JM (1998) Involvement of oxidative stress in chilling injury in cold-stored mandarin fruits. Postharvest Biol Technol 13:255–261CrossRefGoogle Scholar
  98. 98.
    Scott R (1994) The history of the International Energy Agency—the first twenty years 1974–1994: origins and structure vol 1. OECD/IEA, Paris, FranceGoogle Scholar
  99. 99.
    Shin Y, Liu RH, Nock JF, Holliday D, Watkins CB (2007) Temperature and relative humidity effects on quality, total ascorbic acid, phenolics and flavonoid concentrations, and antioxidant activity of strawberry. Postharvest Biol Technol 45:349–357CrossRefGoogle Scholar
  100. 100.
    Singh RP, Heldman DR, Kirk JR (1975) Kinetic analysis of light-induced riboflavin loss in whole milk. J Food Sci 40:164–167CrossRefGoogle Scholar
  101. 101.
    Standards Australia (2018) AS/NZS IEC 62552.2:2018, household refrigerating appliances—characteristics and test methods, part 1: general requirements. https://www.standards.org.au/standards-catalogue/sa-snz/other/el-060/as-slash-nzs%2D%2Diec%2D%2D62552-dot-1-colon-2018. Accessed 16 Mar 2018
  102. 102.
    Swinnen IAM, Bernaerts K, Dens EJJ, Geeraerd AH, Van Impe JF (2004) Predictive modelling of the microbial lag phase: a review. Int J Food Microbiol 94:137–159CrossRefPubMedGoogle Scholar
  103. 103.
    U.S. Department of Energy (2011) Comparison of real-world energy consumption to models and Department of Energy test procedures. Navigant Consulting, Inc. https://www1.eere.energy.gov/buildings/pdfs/real_world_energy_comparison.pdf. Accessed 10 Nov 2018
  104. 104.
    Uhlich GA, Luchansky JB, Tamplin ML, Molina-Corral FJ, Anandan S, Porto-Fett A (2006) Effect of storage temperature on the growth of Listeria monocytogenes on Queso Blanco slices. J Food Saf 26:202–214CrossRefGoogle Scholar
  105. 105.
    Vaclavik VA, Christian EW (2008) Food preservation and processing. In: Essentials of food science. Springer, New York, NY, pp 425–446CrossRefGoogle Scholar
  106. 106.
    van Boekel MAJS (2008) Kinetic modeling of food quality: a critical review. Compr Rev Food Sci Food Saf 7:144–158CrossRefGoogle Scholar
  107. 107.
    van Derlinden E, Mertens L, van Impe JF (2013) Predictive microbiology. In: Doyle MP, Buchanan RL (eds) Food microbiology. American Society of Microbiology, Washington, DC, pp 997–1022Google Scholar
  108. 108.
    Wang Y, Luo Z, Khan ZU, Mao L, Ying T (2015) Effect of nitric oxide on energy metabolism in postharvest banana fruit in response to chilling stress. Postharvest Biol Technol 108:21–27CrossRefGoogle Scholar
  109. 109.
    Whiting RC, Buchanan CE (1997) Development of a quantitative risk assessment model for Salmonella enteritidis in pasteurized liquid eggs. Int J Food Microbiol 36:111–125CrossRefPubMedGoogle Scholar
  110. 110.
    Whiting RC, Buchanan RL (1993) A classification of models in predictive microbiology—a reply to KR Davey. Food Microbiol 10:175–177CrossRefGoogle Scholar
  111. 111.
    Williams J (2018) The ozone hole: a story of healing and hope. Weatherwise 71:12–17CrossRefGoogle Scholar
  112. 112.
    Wolfram C, Shelef O, Gertler P (2012) How will energy demand develop in the developing world? J Econom Persp 26:119–138CrossRefGoogle Scholar
  113. 113.
    Xanthiakos K, Simos D, Angelidis AS, Nychas GJ-E, Koutsoumanis K (2006) Dynamic modeling of Listeria monocytogenes growth in pasteurized milk. J Appl Micobiol 100:1289–1298CrossRefGoogle Scholar
  114. 114.
    Yang H, Wu F, Cheng J (2011) Reduced chilling injury in cucumber by nitric oxide and the antioxidant response. Food Chem 127:1237–1242CrossRefPubMedGoogle Scholar
  115. 115.
    Zhang L, Li X, Lu W, Shen H, Luo Y (2011) Quality predictive models of grass carp (Ctenopharyngodon idellus) at different temperatures during storage. Food Control 22:1197–1202CrossRefGoogle Scholar
  116. 116.
    Zurera-Cosano G, García-Gimeno RM, Rodríguez-Pérez R, Hervás-Martínez C (2006) Performance of response surface model for prediction of Leuconostoc mesenteroides growth parameters under different experimental conditions. Food Control 17:429–438CrossRefGoogle Scholar
  117. 117.
    Zwietering MH, Jongenburger I, Rombouts FM, van’t Riet K (1990) Modeling of the bacterial growth curve. Appl Environ Microbiol 56:1875–1881PubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Tecnologico de Monterrey, Escuela de Ingeniería y CienciasCentro de Biotecnología-FEMSAMonterreyMexico
  2. 2.Instituto Politécnico NacionalCICATA-IPN unidad QuerétaroSantiago de QuerétaroMexico
  3. 3.Embraco Mexico S de RL de CVApodacaMexico
  4. 4.HoustonUS

Personalised recommendations