X-ray Powder Diffraction Decomposition Using Known Information
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Solution Overview
Problem
Conventional methods for analyzing X-ray powder diffraction profiles, especially when peaks overlap, suffer from reduced accuracy in qualitative analysis due to the lack of effective processing techniques, particularly when applying non-negative matrix factorization without considering known information.
Innovation Solution
The proposed solution involves a processing apparatus and method that applies non-negative matrix factorization to X-ray powder diffraction profiles using known information, such as background or substance shapes, to constrain the decomposition process, thereby improving accuracy even in cases of significant peak overlap.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If non-negative matrix factorization is applied without using known information, then the decomposition can be performed freely, but the accuracy of decomposition and qualitative analysis deteriorates
Solution Approach 1:
The patent applies preliminary action by incorporating known information (such as background profiles or substance characteristics) into the non-negative matrix factorization process before decomposition begins. This pre-constraint approach guides the decomposition algorithm to produce more accurate results by leveraging prior knowledge about the data structure and expected outcomes, thereby resolving the contradiction between flexibility and accuracy.
2Measurement precision
If regularization for primary independence is imposed to increase decomposition accuracy, then the independence between profiles is emphasized, but when peak overlap is large, the likelihood that each profile cannot be correctly decomposed increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the regularization strength based on the degree of peak overlap detected in the data. When peak overlap is large, the regularization term is reduced or removed, allowing the decomposition to rely more on the known information constraints rather than forcing primary independence. This adaptive parameter adjustment resolves the contradiction by balancing the two competing requirements based on the actual data characteristics.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of decomposition and subsequent qualitative analysis by incorporating known information, allowing for more precise identification and quantification of components in mixed samples.
Implementation Method 1
a decomposition section for applying non-negative matrix factorization to the measured profile based on the known information
Implementation Method 2
X-ray powder diffraction is used in various fields. By analyzing the measured profile of the X-ray powder diffraction, for example, it is possible to identify (qualitative analysis) and quantify the constituents of the powder sample
Data Source
AI summary
A processing apparatus, a system, a method and a program for applying non-negative matrix factorization to one or more measured profiles of X-ray powder diffraction based on known information are provided. A processing apparatus for applying non-negative matrix factorization to a measured profile of X-ray powder diffraction comprises a measured profile acquiring section for acquiring one or more measured profiles, a known information acquiring section for acquiring known information including a shape of a predetermined profile corresponding to a background or a predetermined substance included in the measured profile, or a restriction of a coefficient matrix of the predetermined profile, and a decomposition section for applying non-negative matrix factorization to the measured profile based on the known information.