2019
DOI: 10.1007/s12393-019-09192-1
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Estimation of Safety and Quality Losses of Foods Stored in Residential Refrigerators

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Cited by 7 publications
(2 citation statements)
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“…These dynamic conditions can be evaluated by predictive microbiology models assessing spoilage and pathogen risks without using time‐consuming microbial testing which are also not practical when used for refrigerated foods stored in the homes of consumers. When estimating the shelf life and safety of refrigerated products, predictive microbiology models should not be used following deterministic approaches, that is, relying only on average or extreme values without considering the inherent variability and uncertainty of the model and of the product and environmental conditions (Rodriguez‐Martinez et al., 2019). This leads to results poorly describing the impact of refrigeration conditions lowering the confidence in recommendations on how to manage and store refrigerated products before their final consumption.…”
Section: Introductionmentioning
confidence: 99%
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“…These dynamic conditions can be evaluated by predictive microbiology models assessing spoilage and pathogen risks without using time‐consuming microbial testing which are also not practical when used for refrigerated foods stored in the homes of consumers. When estimating the shelf life and safety of refrigerated products, predictive microbiology models should not be used following deterministic approaches, that is, relying only on average or extreme values without considering the inherent variability and uncertainty of the model and of the product and environmental conditions (Rodriguez‐Martinez et al., 2019). This leads to results poorly describing the impact of refrigeration conditions lowering the confidence in recommendations on how to manage and store refrigerated products before their final consumption.…”
Section: Introductionmentioning
confidence: 99%
“…On the other hand, probabilistic microbial assessment approaches account for the variability and uncertainty present in the microbial response generating more accurate estimations of food safety and shelf life. These approaches generate probability distributions for the assessed product quality and safety indicators by accounting for the relevant sources of uncertainty and variability (Akkermans et al., 2018; Koutsoumanis et al., 2010; Rodriguez‐Martinez et al., 2019).…”
Section: Introductionmentioning
confidence: 99%