Spectrum Analysis with Automatic Wavelength Band Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing spectrum analysis methods require specialized knowledge for manual wavelength band selection to avoid noise components, leading to increased analysis time and reduced quantification/identification performance due to potential focus on noise components.
Innovation Solution
A spectrum analysis device with an automatic wavelength selection function that calculates wavelength selection information based on contribution and necessity of each band, allowing for accurate quantification/identification without manual expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual wavelength band selection is performed by experts to avoid noise components, then quantification/identification performance is improved, but analysis time increases and specialized knowledge is required
Solution Approach 1:
The system performs automatic wavelength band selection using computational algorithms that evaluate the contribution of each wavelength band to quantification/identification performance. The arithmetic device automatically identifies and selects effective wavelength bands without requiring expert manual intervention, thereby maintaining high performance while reducing analysis time and eliminating the need for specialized knowledge.
2Ease of operation
If all detection intensities in spectrum data are used as independent variables for sparse estimation or multivariate analysis, then automated wavelength selection is achieved, but quantification/identification performance is reduced due to focus on noise components
Solution Approach 1:
The system extracts only the effective wavelength bands from the complete spectrum data by calculating contribution values for each wavelength band. The arithmetic device identifies and extracts wavelength bands that contribute significantly to quantification/identification performance while excluding wavelength bands that primarily contain noise components, thereby maintaining performance while achieving automation.
Solution Approach 2:
The system applies different evaluation criteria to different wavelength bands based on their individual contribution to quantification/identification. Each wavelength band is assessed locally for its effectiveness, and only those bands with sufficient contribution are selected for analysis, rather than uniformly processing all wavelength bands.
3Measurement precision
If rectangular measurement windows are manually designed or randomly searched for multivariate analysis, then wavelength band limitation is achieved, but specialized knowledge is required and noise components may be selected
Solution Approach 1:
The system automatically determines effective wavelength bands through computational evaluation of contribution values, eliminating the need for manual design of measurement windows or random searching. The arithmetic device self-identifies appropriate wavelength bands based on their contribution to quantification/identification performance, thereby maintaining performance while reducing process complexity and eliminating reliance on specialized knowledge.
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 highly accurate quantification/identification with reduced analysis time by automatically selecting effective wavelength bands, reducing reliance on specialized knowledge.
Implementation Method 1
a fluorescence spectrophotometer measures a three-dimensional fluorescence spectrum called a fluorescence fingerprint. When the sample is irradiated with an excitation light having a specific wavelength, the excited sample emits fluorescence having various wavelengths.
Data Source
Figure 1
Figure 2
Figure 3A~3B
AI summary
A spectrum analysis device for quantifying/identifying a characteristic of a sample based on spectrum data of the sample at one or a plurality of excitation wavelengths is disclosed. The spectrum analysis device includes an arithmetic device and a memory configured to store a program to be executed by the arithmetic device. The arithmetic device receives the spectrum data obtained from the sample as an input, acquires wavelength selection information that indicates a contribution to quantification/identification and/or necessary/unnecessary for each wavelength band of the spectrum data, selects a wavelength band used for quantifying/identifying the characteristic of the sample from the wavelength band of the spectrum data based on the wavelength selection information, and performs quantification/identification of the characteristic of the sample based on a detection intensity corresponding to a selection result of the wavelength band, and outputs a result.