X-Ray Diffraction Profile Decomposition With RTV Base Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for analyzing X-ray powder diffraction profiles, particularly when dealing with mixtures or amorphous profiles, suffer from reduced accuracy due to peak overlap and incorrect application of non-negative matrix factorization, leading to deteriorated qualitative and quantitative analysis.
Innovation Solution
Applying non-negative matrix factorization to X-ray powder diffraction profiles, correcting at least some base profiles using relative total variation (RTV) indexes, and classifying them into groups for improved accuracy, especially for amorphous materials, while using corrected base profiles as initial conditions for further decomposition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If non-negative matrix factorization is applied to measured profiles without correction, then decomposition can be performed, but accuracy deteriorates when broad profiles like amorphous materials are present
Solution Approach 1:
The patent implements feedback by calculating RTV indexes from the decomposed base profiles and using these indexes to guide further correction iterations. The base profile correcting section uses the RTV indexes to determine which base profiles need correction and applies correction based on the calculated indexes, creating a closed-loop feedback system that continuously improves decomposition accuracy.
Solution Approach 2:
The patent changes parameters by introducing RTV (relative total variation) indexes as a new parameter to quantify profile unevenness. This parameter is used to classify base profiles into groups and determine correction priorities. The correction process modifies base profile parameters (smoothing, shifting) based on their RTV indexes, enabling adaptive correction that improves accuracy for broad profiles.
2Productivity
If regularization is imposed to search for combinations with high primary independence, then decomposition can be performed, but accuracy decreases when broad profiles are present
Solution Approach 1:
The patent applies local quality by treating different base profiles differently based on their RTV indexes. Instead of uniform processing, the base profile classifying section divides profiles into groups based on their unevenness characteristics, and the correction section applies appropriate correction methods to each group. This localized approach maintains decomposition capability while improving accuracy for specific problematic profiles.
Solution Approach 2:
The patent introduces dynamics by making the correction process adaptive and iterative rather than static. The RTV indexes are calculated from the initial decomposition, used to guide correction, and then the correction results are fed back into further decomposition iterations. This dynamic adjustment allows the system to adapt to the specific characteristics of each sample and improve accuracy over time.
3Ease of operation
If conventional search/match is performed using d-I list without profile processing, then qualitative analysis can be performed, but accuracy deteriorates when many mixtures or amorphous profiles are included
Solution Approach 1:
The patent applies preliminary action by performing non-negative matrix factorization and base profile correction before conducting qualitative analysis. The measured profiles are pre-processed into corrected base profiles with improved characteristics, which then serve as the foundation for subsequent search/match operations. This preliminary processing enhances the quality of data used in qualitative analysis, improving accuracy.
4Measurement precision
If base profiles are corrected using RTV indexes, then decomposition accuracy improves, but processing complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the base profiles into multiple groups based on their RTV indexes and unevenness characteristics. The base profile classifying section segments profiles into distinct groups, and the correction section processes each group separately with appropriate correction methods. This segmentation reduces overall complexity by handling similar profiles together rather than processing all profiles uniformly.
Data Source
AI summary
A processing apparatus for performing non-negative matrix factorization to measured profiles of X-ray powder diffraction includes a measured profile acquiring section for acquiring a plurality of measured profiles; a decomposition section for applying non-negative matrix factorization to the measured profiles and calculating base profiles; an index calculating section for acquiring the base profiles and calculating indexes based on unevenness of the base profiles; a base profile classifying section for classifying the base profiles into a plurality of groups based on the indexes; and a base profile correcting section for performing correction based on the indexes on at least one of the plurality of groups and calculating a corrected base profile.