Modern mathematical software and user-friendly interactive programs can simplify and speed up kinetics calculations. They also open the way for new approaches to storage data gathering and analysis. This is demonstrated with a recently introduced simple exponential model that is interchangeable with the Arrhenius equation and endpoints and successive points methods and that estimates chemical degradation kinetics parameters from a small number of isothermal or nonisothermal experimental data. Also presented are a method to determine shelf life using two chemical markers and a global phenomenological model for peaked reactions, such as those encountered in lipid oxidation. Also recently introduced are freely downloadable Wolfram Demonstrations and other interactive software to generate, visualize, examine, and/or compare actual or hypothetical storage scenarios in minutes. They include programs that solve pairs of simultaneous nonlinear algebraic or differential rate equations by passing two reconstructed degradation curves, or a single nonisothermal curve, through two entered experimental points by moving the degradation parameters’ sliders on the screen.


Article metrics loading...

Loading full text...

Full text loading...


Literature Cited

  1. Aragao GMF, Corradini MG, Peleg M. 2008. A phenomenological model of the peroxide value's rise and fall during lipids oxidation. J. Am. Oil Chem. Soc. 85:1143–53 [Google Scholar]
  2. Barsa CS, Normand MD, Peleg M. 2012. On models of the temperature effect on the rate of chemical reactions and biological processes in foods. Food Eng. Rev. 4:191–202 [Google Scholar]
  3. Calligaris S, Manzocco L, Nicoli MC. 2007. Modeling the temperature dependence of oxidation rate in water-in-oil emulsions stored at sub-zero temperatures. Food Chem. 101:1019–24 [Google Scholar]
  4. Cisse M, Vaillant F, Acosta O, Dhuique-Mayer C, Dornier M. 2009. Thermal degradation kinetics of anthocyanins from blood orange, blackberry, and roselle using the Arrhenius, Eyring, and Ball models. J. Agric. Food Chem. 57:6285–91 [Google Scholar]
  5. Corradini MG, Normand MD, Newcomer C, Schaffner DW, Peleg M. 2009. Extracting survival parameters from isothermal, isobaric and “iso-concentration” inactivation experiments by the “Three End Points Method.”. J. Food Sci. 74:R1–11 [Google Scholar]
  6. Corradini MG, Normand MD, Peleg M. 2008. Prediction of an organism's inactivation patterns from three single survival ratios determined at the end of three non-isothermal heat treatments. Int. J. Food Microbiol. 126:98–111 [Google Scholar]
  7. Corradini MG, Peleg M. 2003. A model of microbial survival curves in water treated with a volatile disinfectant. J. Appl. Microbiol. 95:1268–76 [Google Scholar]
  8. Corradini MG, Peleg M. 2004. A model of non-isothermal degradation of nutrients, pigments and enzymes. J. Sci. Food Agric. 84:217–26 [Google Scholar]
  9. Corradini MG, Peleg M. 2006a. Linear and non-linear kinetics in the synthesis and degradation of acrylamide in foods and model systems. Crit. Rev. Food Sci. Nutr. 46:489–517 [Google Scholar]
  10. Corradini MG, Peleg M. 2006b. Prediction of vitamin loss during non-isothermal heat processes and storage with non-linear kinetic models. Trends Food Sci. Technol. 17:24–34 [Google Scholar]
  11. Corradini MG, Peleg M. 2007. Shelf-life estimation from accelerated storage data. Trends Food Sci. Technol. 18:37–47 [Google Scholar]
  12. Frankel EN. 2014. Lipid Oxidation Cambridge, UK: Woodhead Publ.
  13. Hu M, Jacobsen C. 2016. Oxidative Stability and Shelf Life Stability of Foods Containing Oils and Fats Amsterdam, Neth: Elsevier
  14. Huang L, Hwang A, Phillips J. 2010. Effect of temperature on microbial growth rate—mathematical analysis: the Arrhenius and Eyring-Polanyi connection. J. Food Sci. 76:E533–60 [Google Scholar]
  15. Huang L, Hwang CA, Phillips J. 2011. Evaluating the effect of temperature on microbial growth rate: the Ratkowsky and a Bělehrádek-type models. J. Food Sci. 76:M547–57 [Google Scholar]
  16. Labuza TP. 1984. Application of chemical kinetics to deterioration of foods. J. Chem. Educ. 61:348 [Google Scholar]
  17. Nicoli MC. 2012. Shelf Life Assessment of Food Boca Raton, FL: CRC Press
  18. Normand MD, Lesmes U, Corradini MG, Peleg M. 2010. Wolfram demonstrations: free interactive software for food engineering education and practice. Food Eng. Rev. 2:157–67 [Google Scholar]
  19. Peleg M. 1992. On the use of the WLF model in polymers and foods. Crit. Rev. Food Sci. Nutr. 32:59–66 [Google Scholar]
  20. Peleg M. 2016. A kinetic model and endpoints method for volatiles formation in stored fresh fish. Food Res. Int. 86:156–61 [Google Scholar]
  21. Peleg M, Corradini MG. 2011. Microbial growth curves: what the models tell us and what they cannot. Crit. Rev. Food Sci. Nutr. 51:917–45 [Google Scholar]
  22. Peleg M, Corradini MG, Normand MD. 2009. Isothermal and non-isothermal kinetic models of chemical processes in foods governed by competing mechanisms. J. Agric. Food Chem. 57:7377–86 [Google Scholar]
  23. Peleg M, Engel R, Gonzalez Martinez C, Corradini MG. 2002. Non-Arrhenius and non-WLF kinetics in food systems. J. Sci. Food Agric. 82:1346–55 [Google Scholar]
  24. Peleg M, Kim AD, Normand MD. 2015. Predicting anthocyanins' isothermal and non-isothermal degradation with the endpoints method. Food Chem 187:537–44 [Google Scholar]
  25. Peleg M, Normand MD. 2015a. Predicting chemical degradation during storage from two successive concentration ratios: theoretical investigation. Food Res. Int. 75:174–81 [Google Scholar]
  26. Peleg M, Normand MD. 2015b. Simulating shelf life determination by two simultaneous criteria. Food Res. Int. 78:388–95 [Google Scholar]
  27. Peleg M, Normand MD, Corradini MG. 2012. The Arrhenius equation revisited. Crit. Rev. Food Sci. Nutr. 52:830–51 [Google Scholar]
  28. Peleg M, Normand MD, Corradini MG, van Asselt AJ, de Jong P, Ter Steeg PF. 2008. Estimating the heat resistance parameters of bacterial spores from their survival ratios at the end of UHT and other heat treatments. Crit. Rev. Food Sci. 48:634–48 [Google Scholar]
  29. Peleg M, Normand MD, Goulette TR. 2016. Calculating the degradation kinetic parameters of thiamine by the isothermal version of the endpoints method. Food Res. Int. 79:73–80 [Google Scholar]
  30. Peleg M, Normand MD, Kim AD. 2014. Estimating nutrients' thermal degradation kinetic parameters with the endpoints method. Food Res. Int. 66:313–24 [Google Scholar]
  31. Ratkowsky DA, Olley J, McMeekin TA, Ball A. 1982. Relationship between temperature and growth rate of bacterial cultures. J. Bacteriol. 149:1–5 [Google Scholar]
  32. Roos YH. 2010. Glass transition temperature and its relevance in food processing. Annu. Rev. Food Sci. Technol. 1:469–96 [Google Scholar]
  33. Ross T. 1993. Bělehrádek-type models. J. Ind. Microbiol. Biotechnol. 12:180–89 [Google Scholar]
  34. Slade L, Levine H. 1995. Water and glass transition—dependence of the glass transition on composition and chemical structure: special implications for flour functionality in cookie baking. J. Food Eng. 24:431–509 [Google Scholar]
  35. Spohr J. 1888. Effect of neutral salts in chemical reactions. Z. Physik. Chem. 2:194–212 [Google Scholar]
  36. Steerle R. 2004. Understanding and Measuring Shelf Life of Foods Cambridge, UK: Whitehead Publ.
  37. van Boekel MAJS. 2008. Kinetic modeling of food quality: a critical review. Comp. Rev. Food Sci. Technol. 7:144–58 [Google Scholar]
  38. van Boekel MAJS. 2009. Kinetic Modeling of Reactions in Foods Boca Raton, FL: CRC Press
  39. Verbeyst L, Oey I, van der Plancken I, Hendrickx M, van Loey A. 2010. Kinetic study on the thermal and pressure degradation of anthocyanins in strawberries. Food Chem 123:269–74 [Google Scholar]

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error