Chemical Sensor Array Estimation via Machine Learning Pattern Recognition
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Solution Overview
Problem
Current methods for analyzing chemical substances in complex specimens require multiple processing steps and expensive equipment, making it difficult to quantify specific components quickly and efficiently, especially in field settings where specimens contain multiple chemical substances.
Innovation Solution
A method using machine learning to estimate specific estimation target values from chemical sensor outputs, based on known values from multiple specimens, allowing for the estimation of specific components in unknown specimens without the need for extensive processing or equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If chromatography or isolation methods are used to separate individual components from complicated mixtures for quantification, then measurement precision of specific components is improved, but device complexity and processing time increase significantly
Solution Approach 1:
The patent extracts only the necessary information (specific component concentration) directly from the complex mixture using a sensor array, without performing physical separation. The sensor array responds to multiple components simultaneously, and the system extracts the specific component's contribution through signal processing and pattern recognition, eliminating the need for chromatography equipment.
Solution Approach 2:
The patent replaces the mechanical separation system (chromatography apparatus) with a sensor-based detection system. Instead of physically separating components through mechanical means, the system uses chemical sensors to detect and differentiate components through their distinct sensor response patterns, substituting mechanical complexity with sensory and computational approaches.
2Measurement precision
If chromatography apparatus is used for isolation of components before sensor analysis, then measurement precision is improved, but loss of time and productivity decrease
Solution Approach 1:
The patent extracts the specific component's information directly from the mixed sensor signals through pattern recognition algorithms, eliminating the time-consuming isolation step. The system identifies the specific component's contribution by analyzing the unique response pattern of the sensor array to different components, achieving rapid quantification without physical separation.
Solution Approach 2:
The patent performs preliminary detection of all components simultaneously using the sensor array before any separation or isolation would occur. By capturing the complete sensor response pattern early and using computational methods to deconvolve the individual component contributions, the system achieves both speed and accuracy without sequential processing steps.
3Measurement precision
If multiple processing steps including isolation are performed before sensor analysis, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts the essential measurement information (specific component concentration) directly from the sensor array output through computational analysis, eliminating the need for expensive isolation equipment. The system achieves accurate quantification by recognizing patterns in the sensor responses that are characteristic of the target component, removing the need for costly chromatography apparatus.
Solution Approach 2:
The patent employs relatively simple and inexpensive sensor array elements that can be used without requiring expensive supporting equipment. The sensors themselves are the primary detection tool, eliminating the need for costly isolation systems, making the overall analysis system more affordable and accessible while maintaining measurement precision through smart signal processing.
4Measurement precision
If chromatography or isolation equipment is used for analyzing specimens with multiple components, then measurement precision is improved, but ease of operation and adaptability to field settings decrease
Solution Approach 1:
The patent extracts the specific component's information directly from the sensor array signals through computational methods, eliminating the need for complex isolation procedures. The system automatically identifies and quantifies the target component by analyzing the sensor response pattern, making the operation simple and straightforward without requiring skilled operators to manage complex separation equipment.
Solution Approach 2:
The patent creates a universal sensor array system that can analyze multiple different components and specimen types simultaneously without requiring different equipment or procedures. The same sensor array and computational approach can be applied to various analytical tasks, making the system highly adaptable to different field settings and specimen types while maintaining ease of operation.
Data Source
AI summary
The present invention provides a method and a device for estimating a value to be estimated associated with a specimen, by performing machine learning of a relationship between a value of an estimation object and an output corresponding thereto, based on an output from a chemical sensor with regard to a plurality of specimens for which specific values to be estimated are known, and using the result of the mechanical learning to estimate a specific value to be estimated on the basis of an output from the chemical sensor with regard to a given unknown specimen.