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The STARTEC Decision Support Tool for Better Tradeoffs between Food Safety, Quality, Nutrition, and Costs in Production of Advanced Ready-to-Eat Foods

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TLDR
Compared to other decision support tools, the STARTEC-tool is product-specific and multidisciplinary and includes interpretation and targeted recommendations for end-users.
Abstract
A prototype decision support IT-tool for the food industry was developed in the STARTEC project. Typical processes and decision steps were mapped using real life production scenarios of participating food companies manufacturing complex ready-to-eat foods. Companies looked for a more integrated approach when making food safety decisions that would align with existing HACCP systems. The tool was designed with shelf life assessments and data on safety, quality, and costs, using a pasta salad meal as a case product. The process flow chart was used as starting point, with simulation options at each process step. Key parameters like pH, water activity, costs of ingredients and salaries, and default models for calculations of Listeria monocytogenes, quality scores, and vitamin C, were placed in an interactive database. Customization of the models and settings was possible on the user-interface. The simulation module outputs were provided as detailed curves or categorized as "good"; "sufficient"; or "corrective action needed" based on threshold limit values set by the user. Possible corrective actions were suggested by the system. The tool was tested and approved by end-users based on selected ready-to-eat food products. Compared to other decision support tools, the STARTEC-tool is product-specific and multidisciplinary and includes interpretation and targeted recommendations for end-users.

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Assessment of the distribution of load on the lower limbs and balance before and after ankle arthrodesis with the Ilizarov method.

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Children exposure to femtocell in indoor environments estimated by sparse low-rank tensor approximations

TL;DR: The exposure of an 8-year-old child to a femtocell operating at 2600 MHz, both (child and source) freely located in random positions in an indoor environment, was assessed and stochastic dosimetry based on sparse low-rank tensor approximation method (sparse LRA) was used to develop surrogate models of the exposure.
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Insights into the regulatory role of Plexin D1 signalling in cardiovascular development and diseases

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References
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Journal ArticleDOI

A dynamic approach to predicting bacterial growth in food

TL;DR: A new member of the family of growth models described by Baranyi et al. (1993a) is introduced in which the physiological state of the cells is represented by a single variable, and it is shown that the product of the lag parameter and the maximum specific growth rate is a simple transformation of the initial physiological state.
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GInaFiT, a freeware tool to assess non-log-linear microbial survivor curves

TL;DR: GInaFiT (Geeraerd and Van Impe Inactivation Model Fitting Tool) as discussed by the authors is a freeware add-in for Microsoft Excel aiming at bridging the gap between people developing predictive modelling approaches and end-users in the food industry not familiar with or not disposing over advanced non-linear regression analysis tools.

GInaFiT, a freeware tool to assess non-log-linear microbial survivor curves (vol 102, pg 95, 2005)

TL;DR: The GInaFiT (Geeraerd and Van Impe Inactivation Model Fitting Tool), a freeware Add-in for Microsoft Excel aiming at bridging the gap between people developing predictive modelling approaches and end-users in the food industry not familiar with or not disposing over advanced non-linear regression analysis tools, is presented.
Journal ArticleDOI

Convenient Model To Describe the Combined Effects of Temperature and pH on Microbial Growth.

TL;DR: A new model in which the maximum microbial specific growth rate ((mu)(infmax)) is described as a function of pH and temperature is presented and an analysis of this new model with an Escherichia coli O157:H7 data set showed that there was a good correspondence between observed and calculated (mu)infmax values.
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When is simple good enough: a comparison of the Gompertz, Baranyi, and three-phase linear models for fitting bacterial growth curves

TL;DR: In this paper, a three-phase linear model was developed to determine how well growth curves could be described using a simpler model, which divides bacterial growth curves into three phases: the lag and stationary phases where the specific growth rate is zero (gm = 0), and the exponential phase where the logarithm of the bacterial population increases linearly with time (gm=constant).
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