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How can you use mathematical modeling to improve food shelf life?

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Mathematical modeling is a powerful tool that can help you understand and optimize the factors that affect the quality and safety of food products. By using mathematical equations and data, you can simulate how different variables, such as temperature, moisture, pH, and microbial growth, influence the shelf life of food. In this article, you will learn how to use mathematical modeling to improve food shelf life in four steps.

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Anant Dave, PhD

Dairy Scientist | Expertise in new product & process development and B2B ingredient commercialization | Over 15 years…
Murtadha Noman

Student at Washington State University

1 Step 1: Define the objective and scope

The first step is to define the objective and scope of your modeling project. What is the specific problem or question that you want to address? What are the relevant parameters and constraints that you need to consider? For example, you may want to determine the optimal storage conditions for a dairy product, or compare the effectiveness of different packaging methods for a meat product. You should also specify the level of detail and accuracy that you need for your model, and the data sources and assumptions that you will use.

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Murtadha Noman

Student at Washington State University
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Setting targets is the first step toward effective food shelf-life modeling. Give detailed information on the food, its shelf life, and the variables that impact it. Select a model type (predictive or descriptive, depending on the complexity level) and decide the data you want. In order to guarantee that your model is impactful, realistic, and focused, take ethical issues and resource limitations into account.

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2 Step 2: Develop the model structure and equations

The second step is to develop the model structure and equations that describe the behavior and interactions of the variables that affect food shelf life. Depending on the complexity and purpose of your model, you may use different types of equations, such as differential, algebraic, or empirical. You should also identify the input and output variables, and the initial and boundary conditions of your model. For example, you may use a differential equation to model the growth of bacteria in a food product, and an empirical equation to model the sensory quality of the product over time.

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Murtadha Noman

Student at Washington State University
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Next, create the equations that make up the model's core. Select the appropriate type (algebraic, differential, etc.) according to the goal and complexity of your model. Establish beginning and boundary conditions, as well as precise definitions of input and output variables, such as bacterial growth or sensory quality, to direct the behavior of the model. Your shelf life projections will be supported by this complex structure.

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3 Step 3: Implement and validate the model

The third step is to implement and validate the model using a suitable software or programming language. You should choose a tool that allows you to solve the equations, manipulate the data, and visualize the results. You should also test the validity and reliability of your model by comparing it with experimental data or other models. You should check the sensitivity and robustness of your model by varying the input parameters and analyzing the output responses. For example, you may use MATLAB or R to implement and validate your model, and plot the curves of microbial growth and sensory quality against time.

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Murtadha Noman

Student at Washington State University
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It's time to give your model some life now. Select a program (MATLAB, R, etc.) that enables you to display those tasty forecasts, evaluate data, and run the equations. However, validate against current models and real-world data before serving. Check its sensitivity and observe how various inputs affect the outcome. This thorough validation guarantees that your model is a trustworthy chef of shelf life information rather than merely a fanciful recipe.

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4 Step 4: Apply and optimize the model

The fourth step is to apply and optimize the model to answer your specific question or problem. You should use your model to simulate different scenarios and evaluate the effects of changing the variables on food shelf life. You should also use your model to optimize the variables and find the best combination that maximizes food shelf life. For example, you may use your model to estimate the shelf life of a food product under different storage temperatures, or to find the optimal packaging material and gas composition that minimizes microbial growth and sensory deterioration.

Mathematical modeling can help you improve food shelf life by providing you with a systematic and quantitative approach to understand and control the factors that influence food quality and safety. By following these four steps, you can use mathematical modeling to design and optimize your food products and processes.

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Murtadha Noman

Student at Washington State University
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You've prepared your shelf life model by creating the engine (step 2) and sketching the blueprint (step 1). It's time to light it up now! In Step 3, you may use software to test-drive your product, compare its forecasts to actual facts, and make sure it's more than just a fun trip. Step 4 finally shows its full potential: it allows you to optimize settings, adjust variables, and simulate scenarios in order to maximize shelf life. Thanks to the science-driven insights of mathematical modeling, your food products and processes may flourish with the help of this toolbox.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Anant Dave, PhD

Dairy Scientist | Expertise in new product & process development and B2B ingredient commercialization | Over 15 years of professional experience in the dairy industry
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In my opinion, decoding shelf-life of foods could not be more complicated. The key drawback of mathematical models is that the models do not predict the mechanisms. This information helps to design formulations. The spoilage of the product can be microbial, chemical or both. Not sure if any model considers all aspects. The model is only as good as the data used to derive it. In real life, food interactions are extremely complex and modelling depends on the food matrix. It is important to know your system and not rely too much on mathematical models. Sure, this is a predictive tool, it does not replace shelf-life studies. Always do a validation of your model by running a full study, unless you have a history for that matrix.

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Murtadha Noman

Student at Washington State University
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Note that the real influence of your shelf life model lies outside of the calculations. Take into account affordability, scalability for practical use, and open lines of communication between practitioners and modelers. Consider the future and use AI for individualized storage suggestions and real-time modifications. Your solution has the potential to completely transform the way we think about food shelf life by addressing food waste with improved supply chains and empowering individuals.

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How can you use mathematical modeling to improve food shelf life?

1

2

3

4

5

1 Step 1: Define the objective and scope

2 Step 2: Develop the model structure and equations

3 Step 3: Implement and validate the model

4 Step 4: Apply and optimize the model

5 Here’s what else to consider

Food Science

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How can you use mathematical modeling to improve food shelf life?

1

2

3

4

5

1 Step 1: Define the objective and scope

2 Step 2: Develop the model structure and equations

3 Step 3: Implement and validate the model

4 Step 4: Apply and optimize the model

5 Here’s what else to consider

Food Science

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