Imperial/Cell by Date/Specification
Cell by Date: Specifications
Cell by Date in the Cold Chain:
Temperature is considered to be the major factor in beef spoilage and although the meat industry tries to keep temperature low during transportaion temperature conditions higher than 10°C are not unusual during transport3. It is therefore important that our that we look at the performance of our system in isothermal conditions eg. in the cold chain, and dynamic temperature scenarios eg. a break in the cold chain.
Several Technologies already exist which address the problem of monitoring the thermal exposure of products in the cold chain. One particular family are Temperature Time Integrators (TTIs).5
There also seems to be a general rule for beef that when the bacterial count reaches 107 cm-2, off odours and slime production occur and the beef is considered off. 2 For controlled isothermal conditions in a laboratory environment the time taken for beef to reach this spoilage point seems to be at most 7 days.3 This implies that the shelf life of our system needs to be at least 7 days.
Because bacteria are responsible for the spoilage of beef it is unwise to use a bacteria based device eg. by using e.coli/yeast as a chassis as this would further add to the spoilage.
The Gompertz's model is widely used when considering beef spoilage as it has been shown to fit growth data very well. 1 Using the Gompertz model we can get the specific growth rate, Lag phase duration (LPD) and maximum population density (MPD) for bacterial growth at a particular constant temperature. And then using these we can determine the Activation Energy (Ea) for the beef spoilage reaction. For U2 grade Argentinian beef stored in polyethylene and SARAN PVC, the Ea ranged from 80kJ mol-1 to 220kJ mol-1 for a range of bacteria. 4
One contrary value for the Ea of the beef spoilage reactions is given by Leak 2 who calculated Ea = 30kJ mol-1. The difference between Leak's value and that of Giannuzzi 4 probably lies in their packaging methods, which is of importance as the project aims to target aerobically fresh gound beef.
In order for Cell by Date to act as a TTI & accurately report the spoilage rxn of beef, the activation energy of the two rxns needs to be similar. For example a difference between the two Ea's less than 20kJ mol-1 would result in the TTI estimating the thermal history of the beef to be within 1°C of the actual history.6 For Cell by Date we would like our Activation Energy of our system to be 30 +/- 20 kJ mol-1 to yield an accurate thermal history of the most sensitive spoilage rxn found by Leak, which is most likely for aerobically fresh beef.
In addition, to correctly couple the Ea of our system to that of the dominant spoilage reaction in beef, we also have to consider the response time of our sytem. Our system needs to have a rapid response time, so in other words it needs to be able to quickly switch between states eg. low output and high output. This is so our system can capture small variations of temperature in the cold chain and report them in a meangingful way. Specifically, a response time in the order of a few hours would ensure that if there are any problems in the cold chain this will arise in our system very quickly.
- Labuza TP, Fu B. Growth kinetics for shelf-life prediction: theory and practice. J of Industrial Microbiology 1993;12:309-23.
- Leak, F.W. (2000): Quality changes in Ground beef during distribution and storage, and determination of Time- Temperature-Indicator (TTI) charakteristic of ground beef University of Florida Institute of food and Agricultural Sciences Internet: www.vitsab.com, Stand: April 2003
- Koutsoumanis K, Stamatiou A, Skandamis P, Nychas GJ. Development of a Microbial Model for the Combined Effect of Temperature and pH on Spoilage of Ground Meat, and Validation of the Model under Dynamic Temperature Conditions. Appl Environ Microbiol. 2006 Jan;72(1):124-34.
- Giannuzzi L, Pinotti A, Zaritzky N. Mathematical modelling of microbial growth in packaged refrigerated beef stored at different temperatures. Int J Food Microbiol. 1998 Jan 6;39(1-2):101-10.
- Labuza 2006 : Cold Chain-Management II Time-temperature Integrators and the Cold chain: What is next? Bonn Germany 5/8/06
- E. Shimoni, E.M. Anderson, T.P. Labuza (2001) Reliability of Time Temperature Indicators Under Temperature Abuse. Journal of Food Science 66 (9), 1337–1340. doi:10.1111/j.1365-2621.2001.tb15211.x
- Giannakourou MC, Koutsoumanis K, Nychas GJ, Taoukis PS. Field evaluation of the application of time temperature integrators for monitoring fish quality in the chill chain. Int J Food Microbiol. 2005 Jul 25;102(3):323-36.