MCR-ALS Spectrum Separation for Matrix Interference Removal
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Solution Overview
Problem
Existing methods for eliminating matrix interference in Raman spectroscopy often distort the characteristic peaks of target substances, are difficult to determine appropriate coefficient values, and involve complex measurement and calculation processes.
Innovation Solution
An MCR-ALS-based mixture system matrix spectrum removal method that includes baseline correction, normalization, iterative MCR-ALS algorithm with non-negativity constraints, and spectral angular distance evaluation to separate matrix and target substance spectra.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If spectral subtraction method is used to remove matrix spectrum, then matrix interference is reduced, but characteristic peaks of target substance may be distorted or destroyed
Solution Approach 1:
The patent extracts and removes only the matrix spectrum component from the mixed spectrum using MCR-ALS algorithm, separating it from the target substance spectrum. This selective extraction allows the matrix interference to be eliminated while preserving the integrity of the target substance's characteristic peaks, as the algorithm specifically targets and removes only the matrix component without affecting other substances.
Solution Approach 2:
The MCR-ALS algorithm acts as an intermediary between the mixed spectrum and the pure target substance spectrum. It processes the mixed spectrum through iterative optimization to separate the matrix component, serving as a mediating step that removes interference while maintaining the original spectral information of the target substance intact.
2Object-affected harmful factors
If background spectrum is subtracted with a coefficient, then background signal removal effect is improved, but appropriate coefficient value is difficult to determine and may destroy Raman signal of target substances
Solution Approach 1:
The MCR-ALS algorithm performs self-service by automatically determining the optimal separation without requiring manual coefficient selection. The algorithm iteratively optimizes the decomposition of the mixed spectrum into pure component spectra and their corresponding concentrations, automatically adapting to the specific spectral characteristics of the sample and matrix without external intervention for coefficient tuning.
Solution Approach 2:
The patent changes the approach from using a single coefficient parameter to using multiple concentration parameters for each component. Instead of multiplying the background spectrum by a single coefficient, the MCR-ALS algorithm determines separate concentration profiles for each substance, allowing flexible and accurate removal of background signal while preserving target substance signals.
3Measurement precision
If multiple spectrum measurements are performed for unfolding algorithm, then spectrum decomposition accuracy is improved, but measurement and calculation process becomes complex
Solution Approach 1:
The patent performs preliminary action by acquiring both the mixed spectrum and the pure matrix spectrum before applying the MCR-ALS algorithm. This preliminary acquisition of the matrix spectrum allows the algorithm to use it as an initial reference for decomposition, improving accuracy while avoiding the need for multiple complex measurements during the analysis process.
Data Source
AI summary
Disclosed is an MCR-ALS-based mixture system matrix spectrum removal method, including: S1) acquiring an original mixed spectrum including a target substance and a matrix and an original matrix spectrum including only the matrix; S2) performing baseline correction and normalization processing to obtain a pre-processed mixed spectrum D and a pre-processed matrix spectrum B; S3) resolving, via the MCR-ALS algorithm through iterative optimization, a pure component spectral matrix S and a weight matrix C corresponding to the matrix and the target substance; S4) reducing the target substance spectrum and the matrix spectrum according to the matrices S and C; S5) matching the decomposed matrix spectrum with a known standard matrix spectrum, and performing qualitative identification; and S6) calculating an interpretation variance of the generated spectrum from the original spectrum. The present disclosure can completely remove the matrix substance spectrum in the mixture spectrum, and has less influence on a characteristic peak of the target substance spectrum; and therefore, the influence of the matrix spectrum on the characteristic peak of the target substance spectrum is reduced, and the subsequent quantitative and qualitative analysis is facilitated.