Method For Microbiological Quasi-Chemical Kinetics Growth-Death Modeling in Food

• US 20110054855A1
• Filed: 09/01/2009
• Published: 03/03/2011
• Est. Priority Date: 09/01/2009
• Status: Active Grant

• Current Assignee: Brandeis University
• Original Assignee: USA As Represented By The Secretary of The Army
• Inventors: Ross, Edward, Feeherry, Florence, Kustin, Kenneth, Doona, Christopher
• Granted Patent: US 10,437,909 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G06F 17/13 Differential equations using digital differential analysers G06F7/64

Abstract

A food safety management tool that utilizes a mathematical model based on differential equations that is generalized for describing the continuous growth-death kinetics of microbial populations in foodstuffs. The method is used to provide a way to control target microorganisms when designing product formulations of minimally processed foodstuffs or when processing foods with high pressures, temperatures, or other lethal agents to achieve effective pasteurization, disinfection, or sterilization of foodstuffs, and includes the use of model parameters to predict food formulations to inhibit the growth of microorganisms and the processing times needed to reduce microbial hazards to levels that ensure consumer safety.

Claims

1. A method for microbiological Quasi-chemical kinetics growth-death-tailing modeling based on differential equations, comprising:

inputting into a computer experimentally measured data pairs;

inputting into the computer initial values of rate constant estimates comprising a vector of numerical values;

storing the input vector of numerical values in memory of the computer as a Quasi-chemical kinetics growth-death-tailing model;

selecting a non-linear curve-fitting routine stored in the memory of the computer;

integrating in a microprocessor of the computer the Quasi-chemical kinetics growth-death-tailing model with rate constants of the vector of numerical values to obtain a calculated population data based on the non-linear curve-fitting routine; and

displaying an informational composite of the curve-fit of the calculated population on a screen of the computer.

2. The method of claim 1, further comprising the step of:

comparing calculated population data to experimentally obtained measured data;

changing specific numerical values of the vector to minimize a difference between compared experimentally measured data pairs and the calculated population data for all data pairs of the vector;

wherein said integrating, comparing and changing are iterated until a predetermined fitting criterion is obtained, said predetermined fitting criterion representing attainment of an optimal curve fit of the calculated population data compared with the experimentally determined population data on the screen of the computer.

3. The method of claim 1, wherein the informational composite comprises a graphical plot of the pre-stored experimentally obtained measured data, a graphical plot of calculated data values at identical times as data points of the pre-stored experimentally obtained measured data, a listing of k-values used to generate a fitted curve, and an estimate of an overall error in the fitting.

4. The method of claim 3, wherein the graphical plot of measured data comprises a log-log or log-time graphical plot of the measured experimental data pairs.

5. The method of claim 3, wherein the graphical plot of calculated values comprises a log-log plot of the calculated values.

6. The method of claim 1, further comprising:

entering an additional command to obtain a smoother curve of the calculated population an the screen of the computer based on a computation of additional population time pairs.

7. The method of claim 1, wherein the vector comprises different times and corresponding population counts.

8. A method for predicting microbial growth or processing conditions to ensure microbial destruction and assure consumer safety, comprising:

inputting into a computer experimentally measured data pairs;

inputting into the computer initial values of rate constant estimates comprising a vector of numerical values;

storing the input vector of numerical values in memory of the computer as a Quasi-chemical kinetics growth-death-tailing model;

selecting a non-linear curve-fitting routine stored in the memory of the computer;

integrating in a microprocessor of the computer the Quasi-chemical kinetics growth-death-tailing model with rate constants of the vector of numerical values to obtain a calculated population data based on the non-linear curve-fitting routine; and

predicting the microbial growth or survival in response to processing conditions based on a displayed informational composite of the curve-fit of the calculated population on a screen of the computer to ensure that the informational composite indicates that microbial destruction will occur.

9. The method of claim 8, further comprising the step of:

comparing calculated population data to experimentally obtained measured data;

changing specific numerical values of the vector to minimize a difference between compared experimentally measured data pairs and the calculated population data for all data pairs of the vector;

wherein said integrating, comparing and changing are repeated until a predetermined fitting criterion is obtained, said predetermined fitting criterion representing attainment of an optimal curve-fit of the calculated population data compared to the experimentally determined population data on the screen of the computer.

10. The method of claim 8, wherein the informational composite comprises a graphical plot of the pre-stored experimentally obtained measured data, a graphical plot of calculated data values at identical times as data points of the pre-stored experimentally obtained measured data, a listing of k-values used to generate a fitted curve, and an estimate of an overall error in the fitting.

11. The method of claim 10, wherein the properties of the fitted curve are evaluated to determine lag times, growth or death rates, or processing times.

12. The method of claim 10, wherein the values of lag times, growth or death rates, and processing times are mathematically interrelated with environmental parameters to allow predictions of microbial growth or survival in response to environmental parameters such as conditions which were not measured experimentally.

13. The method of claim 8, further comprising:

entering an additional command to obtain a smoother curve of the calculated population on the screen of the computer based on a computation of additional population time pairs.

14. The method of claim 8, wherein the vector comprises different times and corresponding population counts.

15. The method of claim 10, wherein the environmental parameters comprise one of pH, water activity, storage temperature and high pressure or high temperature processing conditions.