Automated Component Number Determination via Iterative MCR

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Solution Overview

Problem

Current multivariate curve resolution methods require pre-setting the number of components to be analyzed, making it difficult to accurately determine the number of components in unknown samples, especially when small peaks are involved, and different estimation methods yield varying results.

Innovation Solution

An automatic analysis method that repeatedly adjusts the number of components in multivariate curve resolution based on border values from matched and mismatched spectral data, using a provisional number of components until a consistent result is obtained, and employs a library of spectral data for component identification and analysis.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If the number of components is set in advance for multivariate curve resolution, then the analysis process can be completed, but the accuracy of determining the actual number of components in unknown samples deteriorates

Engineering Contradiction:

Improveaccuracy of component number determinationVSAvoidcomplexity of setting number of components

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system performs self-service by automatically determining the number of components through iterative multivariate curve resolution analysis. The control section repeatedly executes MCR with different component numbers and automatically identifies the optimal number based on spectral matching criteria, eliminating the need for manual presetting by analysts.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements feedback by comparing resolution spectral data across multiple MCR analyses with different component numbers. The control section uses the matching degree of spectra as feedback to determine whether to continue increasing the component number or to finalize the determination, creating a closed-loop optimization process.

Inventive Principle:

Principle #23Feedback

2Ease of operation

If manual spectrum peak checking is used to estimate the number of components, then the analyst can identify components, but the ease of operation deteriorates due to requiring advanced knowledge and difficulty in specifying small peaks

Engineering Contradiction:

Improveease of component number estimationVSAvoidaccuracy of component identification

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The system replaces the mechanical/manual process of spectrum peak checking with an automated computational system. The control section automatically performs multivariate curve resolution, generates resolution spectral data, and compares spectra to determine component numbers, substituting human analyst effort with automated algorithmic processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system introduces an intermediary computational process between the raw spectral data and the final component number determination. The multivariate curve resolution algorithm acts as an intermediary that transforms complex spectral data into resolved component spectra, which are then automatically compared to determine the optimal component number.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If different estimation methods are used to determine the number of components, then various approaches can be tried, but the reliability deteriorates because estimated values vary depending on the method adopted

Engineering Contradiction:

Improveconsistency of component number estimationVSAvoidflexibility of estimation methods

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system achieves universality by implementing a standardized multivariate curve resolution-based estimation method that can handle different sample types and spectral data. The same MCR process and spectral comparison criteria are applied universally across different samples, ensuring consistent and reliable component number determination regardless of the specific sample characteristics.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentEP2894460B1Method for automated analysis, automated analyzer, and program for automated analyzers

Publication Date: 2021.06.16 SHIMADZU CORP

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AI summary

Provided are an automatic analysis method, an automatic analysis apparatus, and a program for the automatic analysis apparatus capable of discriminating a number of components included in a sample more accurately and easily. Components are discriminated based on respective pieces of resolution spectral data obtained by multivariate curve resolution (MCR) using a provisional number of components k, and the number of components included in the sample is determined based on a discriminated result. At this time, the multivariate curve resolution is repeated (steps S103 to S108) until a border value between the provisional number of components k in a case where the components are discriminated as being different in all the pieces of the resolution spectral data obtained by the multivariate curve resolution and the provisional number of components k in a case where the components are discriminated as being matched in at least two pieces of the resolution spectral data in all the pieces of the resolution spectral data obtained by the multivariate curve resolution is obtained. As a result, the number of components included in the sample can be discriminated more accurately and easily based on the obtained border value (step S113).