Novel solutions supporting inter-organisational quality and information management

Special Time Temperature Indicators (TTI) are able to display temperature histories by colors, so that the TTIs are able to provide information of the freshness of specific products. To use TTIs in different steps of the supply chain the following requirements need to be fulfilled: Kinetic models to predict the response of the TTIs as a function of time and temperature have to be available as well as warning and action control ranges at different handover points and the ability to translate the response of the TTIs into management and product information. For an economical use of TTIs, software solutions should be available to support the participants within the supply chain with management and product information. This information results from mathematical models that use the responses of the TTIs as input and it results from knowledge what is stored in databases. The objective of this study was to develop an internet based software solution that uses a TTI kinetic model as a practical tool to use TTIs in dedicated meat supply chains. In this study the OnVu TM TTI was used and a validated model to predict the response of the TTI as a function of time and temperature and that also shows a correlation to the kinematics of the spoilage of meat. By using the model within the internet based software with an interface to the response of the TTIs, the remaining shelf life of dedicated products could be easily measured at each point within the supply chain. Within this study the implementation within a poultry supply chain was investigated.

[1]  Petros Taoukis,et al.  Meat safety, refrigerated storage and transport: modeling and management , 2005 .

[2]  Walter Lang,et al.  Spatial temperature profiling by semi-passive RFID loggers for perishable food transportation , 2009 .

[3]  Karen L. Dodds,et al.  Shelf life extension and microbiological safety of fresh meat — a review , 1991 .

[4]  G. Nychas,et al.  Meat spoilage during distribution. , 2008, Meat science.

[5]  L. Ruiz-Garcia,et al.  A model and prototype implementation for tracking and tracing agricultural batch products along the food chain. , 2010 .

[6]  Theodore P. Labuza,et al.  Reliability of Time‐Temperature Indicators as Food Quality Monitors Under Nonisothermal Conditions , 1989 .

[7]  Ning Wang,et al.  Review: Wireless sensors in agriculture and food industry-Recent development and future perspective , 2006 .

[8]  G. Nychas,et al.  Field evaluation of the application of time temperature integrators for monitoring fish quality in the chill chain. , 2005, International journal of food microbiology.

[9]  K Koutsoumanis,et al.  Development of a Safety Monitoring and Assurance System for chilled food products. , 2005, International journal of food microbiology.

[10]  P. Taoukis,et al.  Applicability of Time‐Temperature Indicators as Shelf Life Monitors of Food Products , 1989 .

[11]  Brigitte Petersen,et al.  Generic model for the prediction of remaining shelf life in support of cold chain management in pork and poultry supply chains , 2008 .

[12]  Theodore P. Labuza,et al.  Considerations For The Application Of Time-Temperature Integrators In Food Distribution , 1992 .

[13]  Viktor Popov,et al.  Mathematical modelling for predicting the growth of Pseudomonas spp. in poultry under variable temperature conditions. , 2008, International journal of food microbiology.

[14]  J. Kreyenschmidt,et al.  A novel photochromic time–temperature indicator to support cold chain management , 2010 .

[15]  Da‐Wen Sun,et al.  Predictive food microbiology for the meat industry: a review. , 1999, International journal of food microbiology.