Microorganism Counting via Growth Model Extrapolation
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Solution Overview
Problem
Current methods for counting microorganisms in biological samples require lengthy incubation periods, which can lead to significant economic losses due to delayed product marketing and potential withdrawal from the market if results are negative, as they often exceed the shelf life of perishable products.
Innovation Solution
A method and device that use a growth model to extrapolate the number of microorganisms after a standard incubation period by calculating based on a relationship involving decimal logarithms and predetermined parameters, allowing for earlier estimation of microorganism counts, thereby reducing incubation time without compromising reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a long incubation period is used to obtain reliable microorganism counts, then the reliability of the count is improved, but the time required for the process increases significantly
Solution Approach 1:
The method performs preliminary counting of microorganisms at an intermediate stage (after partial incubation) and uses a growth model to predict the final count. This preliminary action allows the system to obtain reliable results without completing the full incubation period, thus reducing time loss while maintaining counting reliability.
Solution Approach 2:
A growth model acts as an intermediary between the intermediate-stage count and the final expected count. The model (log(NSU) = log(NSA) + α + β×T + γ) translates the partial incubation data into a reliable prediction of the final result, eliminating the need to wait for complete incubation while preserving accuracy.
2Measurement precision
If a long incubation period is used to ensure accurate microorganism enumeration, then the measurement precision is improved, but the productivity of the quality control process decreases
Solution Approach 1:
The system performs the counting measurement at an intermediate stage and uses predictive modeling to obtain the final result. This preliminary measurement combined with mathematical prediction maintains measurement precision while dramatically improving productivity by reducing the time required for quality control decisions.
Solution Approach 2:
The method replaces the mechanical/biological waiting process (full incubation) with a computational approach (growth model calculation). The formula log(NSU) = log(NSA) + α + β×T + γ substitutes the physical incubation time with mathematical computation, maintaining precision while boosting productivity.
3Reliability
If the incubation time is extended to obtain reliable counts, then the reliability of product quality assessment is improved, but the economic loss increases due to delayed product marketing
Solution Approach 1:
The method enables preliminary quality assessment by predicting final microorganism counts from intermediate measurements. This allows producers to make marketing decisions earlier with reliable predictions, improving the time-to-market while maintaining assessment reliability through the growth model.
Solution Approach 2:
The growth model serves as an intermediary that bridges intermediate-stage data and final quality assessment requirements. By using the formula log(NSU) = log(NSA) + α + β×T + γ, the system provides reliable quality predictions earlier in the process, reducing economic losses from delayed marketing while maintaining assessment reliability.
Data Source
Figure 1~2
AI summary
A method for counting microorganisms present in a biological sample in contact with a culture medium suitable for the growth of said microorganisms, characterized in that it comprises the steps consisting in: determining at a prior stage the number NSA of microorganisms present in the sample, and calculating the number NSU of microorganisms present at a subsequent stage as a function of the number NSA, wherein the calculation is based on a model of growth of the microorganisms in the culture medium according to the relationship: log(Nsu ) = a × log (NSA) - ß × log(CSA )+ ? where log is the decimal logarithm, NSU is the number of microorganisms calculated, NSA is the number of microorganisms at the prior stage, CSA is the number of microorganisms at the prior stage divided by the volume of the sample, and a, ß and ? are predetermined parameters dependent on the microorganisms, on the culture medium and on the time separating the subsequent stage from the prior stage, a and ß being positive.