Time-Resolved Raman Spectroscopy for Fluorescence Separation
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Solution Overview
Problem
Fluorescence background in Raman spectroscopy overwhelms the weaker Raman signal, reducing the signal-to-noise ratio and introducing 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, separating fluorescence background from Raman signal by analyzing the arrival times of photons, thereby generating a Raman spectrum with reduced fluorescence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fluorescence background is present in Raman spectroscopy, then the detection of Raman signals is possible, but the signal-to-noise ratio is reduced and material identification accuracy deteriorates
Solution Approach 1:
The patent segments the spectral data into multiple time bins based on photon arrival times. By dividing the time domain into discrete bins and processing each bin separately through neural networks, the system separates fluorescence signals (which decay rapidly) from Raman signals (which are instantaneous), thereby removing fluorescence background while preserving Raman signals and improving signal-to-noise ratio
Solution Approach 2:
The patent introduces a trained 2D neural network as an intermediary processing layer between the raw spectral data and the final Raman spectrum. This neural network acts as a mediator that learns to distinguish and separate fluorescence background from Raman signals based on temporal patterns, effectively removing harmful fluorescence while preserving useful Raman information
2Measurement precision
If multiple light pulses are used to acquire spectral data, then the separation of fluorescence and Raman signals is improved, but the acquisition time and data processing complexity increase
Solution Approach 1:
The patent employs periodic light pulse irradiation of the sample, where multiple pulses are delivered at regular intervals. This periodic excitation allows the system to capture temporal information about signal decay characteristics, enabling fluorescence separation from Raman signals. The periodic structure provides consistent temporal patterns that neural networks can learn to exploit for signal separation
Solution Approach 2:
The patent performs preliminary acquisition of spectral data across multiple time bins before final processing. By collecting data in advance organized by arrival time bins, the system prepares structured input data that neural networks can process efficiently, reducing the computational burden during analysis while maintaining high separation accuracy
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 removes fluorescence background from Raman signals, preserving the signal-to-noise ratio and enhancing the accuracy of 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
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.