The STARTEC Decision Support Tool for Better Tradeoffs between Food Safety, Quality, Nutrition, and Costs in Production of Advanced Ready-to-Eat Foods
Taran Skjerdal,Andras Gefferth,Miroslav Spajic,Edurne Gaston Estanga,Alessandra De Cesare,Silvia Vitali,Frederique Pasquali,Federica Bovo,Gerardo Manfreda,Rocco Mancusi,Marcello Trevisiani,Girum Tadesse Tessema,Tone Mathisen Fagereng,Lena Haugland Moen,Lars Lyshaug,Anastasios Koidis,Gonzalo Delgado-Pando,Alexandros Ch. Stratakos,Marco Boeri,Cecilie From,Hyat Syed,Mirko Muccioli,Roberto Mulazzani,Catherine Halbert +23 more
Reads0
Chats0
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.read more
Citations
More filters
Journal ArticleDOI
Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters.
TL;DR: It is concluded that for accurate planning of tumour resections, the liver organ and its internal structures should be segmented to understand the clear spatial relationship between them, thus allowing for a safer resection.
Journal ArticleDOI
The genome variation and developmental transcriptome maps reveal genetic differentiation of skeletal muscle in pigs.
Yalan Yang,Junyu Yan,Xinhao Fan,Jiaxing Chen,Zishuai Wang,Xiaoqin Liu,Guoqiang Yi,Yuwen Liu,Yongchao Niu,Longchao Zhang,Lixian Wang,Shuai Cheng Li,Kui Li,Zhonglin Tang +13 more
TL;DR: In this paper, the authors conducted transcriptome profiling of skeletal muscle across 27 time points, and performed whole-genome re-sequencing in Landrace (lean type) and Tongcheng (obese-type) pigs.
Journal ArticleDOI
Non-coding RNAs as emerging targets for crop improvement.
TL;DR: The current state of knowledge with respect to lncRNAs and their mechanism(s) of action as well as their regulatory functions are reviewed, specifically within the context of biotic stresses.
Journal ArticleDOI
3,4,5-Tri-O-Caffeoylquinic Acid Promoted Hair Pigmentation Through β-Catenin and Its Target Genes.
TL;DR: TCQA triggered β-catenin activation to enhance the pigmentation during the anagen phase of the hair cycle, suggesting that the Wnt/β-Catenin signaling pathway plays a major role in the development, growth, and proliferation of the melanocytes and the activation of melanogenesis enzymes and the related transcription factor.
Journal ArticleDOI
Immune challenge-induced oxidative damage may be mitigated by biliverdin
TL;DR: In quail, plasma levels of oxidative damage and biliverdin concentration are negatively correlated, consistent with a localized antioxidant function of biliverin, suggesting that pathways associated with biliversdin production may also be associated with aspects of nutrient mobilization.
References
More filters
Journal ArticleDOI
A dynamic approach to predicting bacterial growth in food
József Baranyi,T.A. Roberts +1 more
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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).