AI Spectral Analysis for Rapid Multi-Component Identification
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Solution Overview
Problem
Existing spectroscopic analysis techniques face challenges in accurately and efficiently identifying multiple components in complex biological samples like medical fluids, leading to prolonged analysis times and increased costs due to the need for multiple tests.
Innovation Solution
An AI-based system that utilizes multi-dimensional spectroscopic data from various analysis modalities, including UV, Raman, and IR spectra, combined with patient data, to automatically identify and quantify biological molecules in medical fluids using machine learning and deep learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spectroscopic analysis techniques are used to identify multiple components in complex biological samples, then analysis accuracy can be maintained, but analysis time increases and costs increase due to the need for multiple tests
Solution Approach 1:
The patent combines multiple spectroscopic analysis modalities (UV, Raman, IR) into a single integrated system that simultaneously analyzes multiple components in complex biological samples. This merging of analysis methods allows the system to identify multiple molecules in one test rather than requiring separate tests for each component, thereby reducing analysis time while maintaining identification accuracy through the complementary information provided by each spectroscopic method
Solution Approach 2:
The system employs a universal AI-based analysis platform that can handle multiple types of spectroscopic data (UV, Raman, IR) and analyze various classes of molecules (small molecules, peptides, proteins, nucleic acids) through a single multi-functional system. This universality eliminates the need for multiple specialized tests, reducing both time and cost while maintaining the ability to accurately identify diverse molecular components
2Reliability
If traditional spectroscopic analysis techniques are used to identify multiple components in complex biological samples, then analysis reliability can be maintained, but the number of tests required increases leading to increased costs
Solution Approach 1:
The patent merges multiple spectroscopic techniques and analysis modalities into a single integrated system that performs comprehensive molecular identification in one test. This consolidation reduces the number of separate tests required while maintaining reliability through the use of multiple complementary spectroscopic methods that cross-validate each other's results, thereby eliminating the need for multiple sequential tests
3Productivity
If AI-based analysis is used to analyze spectroscopic data, then analysis speed improves, but data processing complexity increases
Solution Approach 1:
The patent introduces AI-based algorithms as an intermediary between the spectroscopic data acquisition and the final molecular identification. These AI algorithms automatically process the complex multi-dimensional spectroscopic data from UV, Raman, and IR modalities, performing pattern recognition and molecular identification at high speed. The AI intermediary handles the data processing complexity internally, enabling rapid analysis without requiring complex manual processing procedures
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 system provides rapid and accurate identification of multiple components in medical fluids, reducing the need for multiple tests and enabling real-time analysis in healthcare settings.
Implementation Method 1
a spectrometer operable to measure light absorption, scattering, or emission by the sample
Implementation Method 2
a spectrometer operable to measure light absorption, scattering, or emission by the sample
Implementation Method 3
a spectrometer operable to measure light absorption, scattering, or emission by the sample
Data Source
AI summary
A system provides for an ability to automatically identify one or more chemical components of a sample, especially a biological fluid 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 preferably used for spectral techniques such as UV/vis and near-infrared (NIR) spectrophotometry, but is also able to be used for other forms of spectroscopic techniques, including mass spectrometry, infrared (IR) spectrometry, X-ray spectroscopy, Raman spectrometry, or nuclear magnetic resonance (NMR) spectrometry.