Product Preservation State Prediction with Limited Stability Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining the preservation state of products with evolving quantitative attributes face challenges in precision when using limited experimental stability data, particularly when temperature conditions vary.
Innovation Solution
A method and computer system that utilize a plurality of phenomenological models and multiple estimators to determine the preservation state of products by extrapolating the law of evolution of quantitative attributes as a function of time and temperature, even with limited data, using a computer system with an interface, equation resolution, and estimator tools to select the best fitting model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional extrapolation methods are used with limited experimental stability data, then the determination of preservation state can be performed, but the precision and accuracy of the result deteriorates
Solution Approach 1:
The patent transforms the qualitative problem of limited data into a quantitative parameter optimization problem. By changing the approach from traditional single-model extrapolation to multi-model parameter selection, the system achieves high precision with limited data. The computer system evaluates multiple phenomenological models and selects the best fitting one based on statistical criteria, effectively utilizing every data point to its full potential.
Solution Approach 2:
The patent creates a virtual library of multiple phenomenological models that replicate different possible evolution patterns of quantitative attributes. Instead of relying on a single model that may not fit the limited data well, the system copies multiple model patterns and uses statistical evaluation to identify which pattern best represents the actual product behavior, thereby improving precision despite data limitations.
2Adaptability or versatility
If a single phenomenological model is used for extrapolation, then the method is simple, but the ability to handle variable temperature conditions and select the best fitting model deteriorates
Solution Approach 1:
The patent makes the computer system multi-functional by integrating data collection, multiple model evaluation, statistical analysis, and model selection into a single unified system. The system universally handles various temperature conditions and data patterns through its comprehensive model library and statistical evaluation framework, making it adaptable to different product types and storage conditions without requiring separate specialized tools.
Solution Approach 2:
The patent implements a feedback mechanism where the computer system continuously evaluates multiple phenomenological models against the experimental data using statistical criteria (AIC, BIC, F-test, t-test). The system provides feedback by comparing model performance and automatically selects the best fitting model, creating an iterative optimization process that adapts to variable temperature conditions and data characteristics.
Data Source
Figure 1~2
Figure 3
Figure 4~5
AI summary
The method includes: providing a computer system in which are stored phenomenological models of evolution of a quantitative attribute value, each model having between three and nine parameters, including an initial quantitative attribute value parameter, said computer system including an equation resolution tool for computing an experimental stability data set for finding for each said model best fitting values for its parameters, an estimator production tool for finding for each model estimators including a physico-chemical parameter likelihood estimator and a fit distance, respectively based on a comparison between initial quantitative attribute values in the experimental data set and the best fitting value found for said model for the initial quantitative attribute value parameter, and on a comparison between the values in the experimental data set and the corresponding values given by said model; selecting as law of evolution of the quantitative attribute value a best model amongst said models based on the found estimators; determining said preservation state using the law of evolution.