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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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The long-term stability of drug products is critical to ensuring their quality, safety, and effectiveness over time. Stability directly influences a product's ability to maintain its intended characteristics, ensuring it performs as expected during its intended shelf life. Key attributes such as drug potency, impurities, dissolution, and other physicochemical measures of performance are tested to assess stability. These parameters indicate how well the product retains its quality over time and...
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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A Multi-Model Approach to Implement a Dynamic Shelf Life Criterion in Meat Supply Chains.

Antonia Albrecht1, Maureen Mittler1, Martin Hebel1

  • 1Institute of Animal Science, University of Bonn, Katzenburgweg 7-9, 53115 Bonn, Germany.

Foods (Basel, Switzerland)
|November 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamic shelf-life criterion (DSLC) for fresh pork, using predictive microbiology and sensory data. This approach accurately predicts pork spoilage in real-time, reducing food waste and improving safety.

Keywords:
dynamic shelf-lifefood waste preventionmeat qualitymeat spoilagepredictive microbiologysensory modeling

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Area of Science:

  • Food Science
  • Microbiology
  • Sensory Analysis

Background:

  • Fresh meat's high perishability leads to significant food waste, exacerbated by fixed best-before dates.
  • Current shelf-life estimations often fail to account for real-time environmental conditions during distribution and storage.

Purpose of the Study:

  • To develop a dynamic shelf-life criterion (DSLC) for fresh pork filets.
  • To integrate predictive microbiology and sensory modeling for real-time shelf-life assessment.
  • To reduce food waste by providing a more accurate measure of pork freshness.

Main Methods:

  • Investigated 647 ma-packed pork loin samples under various storage conditions.
  • Analyzed meat quality parameters (pH, color, texture, sensory) and microbial counts (TVC, Pseudomonas, LAB, B. thermosphacta, Enterobacteriaceae).
  • Employed dynamic modeling using modified Gompertz (microbial) and linear (sensory) models combined with the Arrhenius model.

Main Results:

  • Developed a four-point scale grading system for the DSLC.
  • The DSLC accurately predicts pork product status and shelf-life based on temperature data.
  • Validated the DSLC in a pilot study under real-world supply chain conditions, confirming its predictive accuracy.

Conclusions:

  • The developed DSLC offers a reliable method for real-time shelf-life prediction of fresh pork.
  • This dynamic approach can significantly contribute to reducing food waste in the meat industry.
  • The multi-model approach provides a robust framework for assessing perishable food product quality.