Flexible Model Expression for Liquid Chromatography Analysis
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Solution Overview
Problem
Conventional regression analysis methods fail to accurately model relationships between analysis conditions and results in liquid chromatography due to limitations in predetermined model expressions, restricting the degree of freedom and accuracy of derived approximate expressions.
Innovation Solution
A data analysis system and method that allow users to set parameters as variables and optionally structure model expressions, using techniques like Bayesian inference, to create more flexible and accurate approximate expressions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a predetermined model expression is used for regression analysis, then the analysis process is simplified and can be executed efficiently, but the degree of freedom in constructing the model expression is restricted and the accuracy of the approximate expression decreases
Solution Approach 1:
The patent applies the Dynamics principle by making the model expression structure adjustable and flexible rather than fixed. The system allows users to dynamically select from multiple model expression types (linear, polynomial, logarithmic, exponential) and freely set parameters such as the number of terms, degree of polynomial, and specific variables included in the model. This dynamic configurability enables the model to adapt to different analysis scenarios and data characteristics, thereby improving approximation accuracy while maintaining efficient execution through automated selection and regression analysis.
2Ease of operation
If the model expression structure is fixed in advance, then the analysis process becomes standardized and easier to operate, but the adaptability to different analysis conditions and relationships is reduced
Solution Approach 1:
The patent implements theUniversality principle by designing a multi-functional model expression system that can accommodate various types of relationships between analysis conditions and results. The system provides a universal framework that supports multiple model expression types (linear, polynomial, logarithmic, exponential) and allows flexible configuration of parameters. This universal approach enables the same system to handle diverse analysis scenarios—whether the relationship is linear, curved, or involves interaction effects—thereby improving both adaptability and ease of operation through a unified, configurable interface.
Data Source
AI summary
A data storage part (2) that stores responses, which are a plurality of analysis results obtained by a plurality of analyses executed under a plurality of analysis conditions, and factors, which are a plurality of parameters included in the analysis conditions, in a manner that the responses and the factors are associated with each other, a data processor (4) configured to use at least one of the factors as a variable and to create an approximate expression indicating a relationship between the variable and the responses, and an information input device (6) for a user to input information to the data processor (4). The data processor (4) is configured to execute a variable setting step of causing the user to set at least one of the factors to be the variable, a structure setting step of causing the user to optionally set a structure of a model expression that is a basis of the approximate expression using the variable set in the variable setting step, a model expression determination step of determining the model expression based on the structure set by the user in the structure setting step, and an approximate expression determination step of determining a coefficient of each term constituting the model expression determined in the model expression determination step by regression analysis, and thereby determining the approximate expression.