AI Spectral Analysis for Complex Multi-Component Analytes
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Solution Overview
Problem
Existing spectroscopic methods struggle with accurately analyzing complex analytes containing multiple elements and compounds due to high density of spectral peaks, requiring expensive high-resolution equipment and lengthy analysis times, and often fail to detect faint indicators beyond prominent emission peaks.
Innovation Solution
An AI-based system that utilizes optical emission spectroscopy to generate plasma from analytes, combined with a multi-dimensional dataset analysis and an AI model, allowing for holistic pattern recognition of emission spectra to identify and quantify elements and compounds, even in low-cost setups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spectroscopic methods are used to analyze complex analytes, then spectral data can be collected, but accurate identification of multiple elements and compounds is difficult due to high density of spectral peaks
Solution Approach 1:
The patent introduces an AI model as an intermediary between spectral data collection and analyte identification. The AI model processes the complex spectral patterns, resolving overlapping peaks and identifying multiple elements and compounds simultaneously. This intermediary system transforms the difficult spectral analysis into accurate component identification without requiring higher resolution hardware.
2Measurement precision
If high-resolution spectroscopic equipment is used to resolve spectral peaks, then measurement accuracy improves, but system cost increases
Solution Approach 1:
The patent replaces the mechanical/optical approach of using high-resolution spectroscopic equipment with an computational/AI-based approach. Instead of increasing hardware resolution, the system uses AI algorithms to process and interpret lower-resolution spectral data, achieving accurate component identification without expensive high-resolution instruments.
Solution Approach 2:
The patent creates a computational model that copies and simulates the function of high-resolution spectral analysis through AI processing. The AI model learns from training data to reproduce the analytical capabilities of complex spectroscopic systems, enabling accurate analysis with simpler, lower-cost equipment.
3Measurement precision
If conventional spectroscopic analysis is performed on complex analytes, then analysis can be completed, but analysis time increases due to need for high-resolution equipment and complex processing
Solution Approach 1:
The patent performs preliminary action by training the AI model in advance on extensive spectral data from multiple elements and compounds. This pre-trained model can then rapidly analyze complex analytes without requiring time-consuming high-resolution spectral processing during actual analysis. The heavy computational work is done beforehand, enabling fast real-time analysis.
4Quantity of substance
If traditional spectroscopic methods are used, then prominent emission peaks can be detected, but faint spectral indicators are missed
Solution Approach 1:
The patent creates a universal AI analysis system that simultaneously handles both prominent and faint spectral features, as well as multiple elements and compounds in a single analysis. The AI model is trained to recognize patterns across the entire spectral range, enabling detection of faint indicators alongside strong emission peaks without requiring separate optimization for each detection level.
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
Enables accurate and efficient identification of complex analytes with reduced costs and time, overcoming the limitations of traditional spectroscopy by utilizing AI to detect patterns across the full spectrum and account for faint spectral features.
Implementation Method 1
wherein the at least one reactor is operable to generate plasma from the analyte
Implementation Method 2
wherein the one or more servers receives experimental data for the plasma from a spectrometer
Data Source
AI summary
A system provides for an ability to automatically identify one or more chemical components of a sample, based on analysis of spectral data by at least one artificial intelligence module. The artificial intelligence module is able to be trained on a plurality of spectral data samples having known concentrations of individual chemicals and elements. The system is further operable to calculate the correlations between spectral data samples of varying concentrations and predict the concentration of the one or more chemical components of the sample.