FT-IR Spectrum Analysis Using Trained Machine Learning Model
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Solution Overview
Problem
Analyzing Fourier Transform Infrared (FT-IR) spectra for compounds with complex structures is challenging, especially for users unfamiliar with the process, as it requires visual comparison with library spectra and can be inaccurate even for experienced users due to the complexity of the spectrum patterns.
Innovation Solution
A data analysis apparatus and method using a trained machine learning model to identify peaks in FT-IR spectra derived from atomic groups containing at least three atoms, allowing for accurate and easy analysis by inputting the FT-IR spectrum and designation information to determine the presence of specific atomic groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If visual comparison with library spectra is used for analysis, then the analysis method is simple and accessible, but the accuracy deteriorates for complex spectrum patterns and inexperienced users
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the raw FT-IR spectrum and the user. The model automatically identifies peaks and determines atomic groups, eliminating the need for users to perform visual comparison while maintaining high accuracy for complex patterns.
Solution Approach 2:
The patent replaces the manual visual comparison process with an automated machine learning-based analysis system. This substitution transforms the analysis from a manual, experience-dependent process to an automated, algorithm-driven process that handles complex patterns objectively and accurately.
2Measurement precision
If machine learning model is used for analysis, then the accuracy improves for complex spectrum patterns, but the device complexity increases
Solution Approach 1:
The patent segments the analysis process into distinct functional modules: a peak identification module that detects peaks in the FT-IR spectrum, and an atomic group determination module that uses machine learning to identify atomic groups based on the detected peaks. This segmentation manages complexity by breaking down the overall system into manageable, specialized components.
Solution Approach 2:
The machine learning model serves as an intelligent intermediary that processes the complex relationship between spectral peaks and atomic groups. By encapsulating the complex pattern recognition logic within the model, the system achieves high accuracy without requiring the user interface or overall system architecture to become overly complex.
3Device complexity
If conventional analysis method is used, then the system remains simple, but the analysis becomes difficult for compounds with complex molecular structures
Solution Approach 1:
The patent replaces the manual visual comparison process with an automated machine learning-based analysis system. This substitution transforms the analysis from a manual, experience-dependent process to an automated, algorithm-driven process that handles complex patterns objectively and accurately.
Solution Approach 2:
The machine learning model performs self-service by automatically identifying peaks and determining atomic groups without requiring user intervention or expertise. The model independently processes the FT-IR spectrum and provides accurate results, eliminating the difficulty associated with manual analysis of complex molecular structures.
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
The trained model enables highly accurate and straightforward analysis of FT-IR spectra, effectively handling complex spectrum patterns by machine learning and reducing the difficulty in identifying atomic groups within the spectra.
Implementation Method 1
The FT-IR photometer subjects a graph (interferogram) of records of detection signals to Fourier transform to thereby obtain a spectrum (which is also referred to as an 'FT-IR spectrum' below)
Implementation Method 2
A Fourier transform infrared spectrophotometer (which is also referred to as an 'FT-IR photometer' below) emits infrared coherent light to a sample and detects reflected light or transmissive light
Data Source
AI summary
A data analysis apparatus, a data analysis method, a method of generating a trained model, a system, and a program that allow highly accurate and easy analysis of an FT-IR spectrum are provided. The data analysis apparatus includes an obtaining unit that obtains an analysis target which is an FT-IR spectrum, a trained model, and an analyzer that inputs the analysis target to the trained model. The trained model is machine-trained so as to output, when the trained model receives input of an FT-IR spectrum, information indicating whether or not the inputted FT-IR spectrum includes a peak derived from a trained atomic group. The trained atomic group includes an atomic group containing at least three atoms.