Raman Spectroscopy With Time-Resolved Neural Fluorescence Removal
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Solution Overview
Problem
Raman spectroscopy is hindered by strong fluorescence background signals that overwhelm the weaker Raman signals, leading to reduced signal-to-noise ratio and errors in material identification and quantitative measurement.
Innovation Solution
Utilizing a trained 2D neural network to process a spectrum series acquired from multiple light pulses, where each spectrum corresponds to a different arrival time, to separate and remove fluorescence background from Raman signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional Raman spectroscopy is used to analyze samples, then material identification and quantitative measurement can be performed, but the strong fluorescence background signals overwhelm the weaker Raman signals, reducing signal-to-noise ratio and causing errors in analysis
Solution Approach 1:
The patent segments the spectral data by recording photons at different arrival times relative to the laser pulse. By dividing the time domain into multiple bins, the system separates early photons (containing Raman signal) from later photons (containing fluorescence background), enabling selective analysis of the Raman component while rejecting the harmful fluorescence signal.
Solution Approach 2:
The patent extracts the Raman signal component from the mixed signal by using time-resolved detection. The system identifies and extracts only the photons arriving within a specific time window after the laser pulse, which correspond to the Raman scattering event, while excluding the fluorescence background that arrives later, thus removing the harmful factor while preserving the useful signal.
2Measurement precision
If time-resolved detection is used to separate Raman and fluorescence signals, then signal-to-noise ratio improves, but device complexity increases due to the need for precise timing electronics and data processing
Solution Approach 1:
The patent replaces complex mechanical or optical filtering systems with electronic time-resolved detection and digital signal processing. Instead of using physical filters to separate Raman and fluorescence signals, the system uses precise timing electronics to distinguish photons based on their arrival time and processes the data computationally, which can be more effective and less complex than optical filtering approaches.
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
Effectively enhances the signal-to-noise ratio by accurately extracting Raman spectra while preserving spectral resolution, enabling precise material identification and quantitative analysis.
Implementation Method 1
Raman spectroscopy is a spectroscopic technique that may be used to characterize and determine composition of a sample
Implementation Method 2
Fluorescence background is a common issue in Raman spectroscopy
Implementation Method 3
acquiring photons from the sample with the detector, wherein acquiring the photons includes recording an arrival time of each photon
Data Source
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AI summary
Spectrum series are generated based on acquired photons responsive to irradiating a location of a sample with multiple light pulses. The spectrum series is processed with a trained 2D neural network to generate Raman spectrum with reduced fluorescence.