Hyperspectral data analysis method based on plasma resonance metal film substrate

By using plasma resonant metal thin film substrates and deep learning technology in microscopic hyperspectral imaging technology, the problem of difficult equipment cost and imaging performance is solved, and high sensitivity and high resolution spectral analysis is achieved.

CN120084760APending Publication Date: 2025-06-03GUANGXI UNIVERSITY OF TECHNOLOGY
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Patent Information

Application Number
CN202510092476.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-01-21
Publication Date
2025-06-03

AI Technical Summary

Technical Problem

Existing microscopic hyperspectral imaging technologies are difficult to take into account both equipment cost and imaging performance, especially in reducing background interference and improving signal-to-noise ratio.

Method used

Using a hyperspectral data analysis method based on a plasma resonant metal thin film substrate, a metal nano-film substrate with plasma resonance effect is pre-prepared, the sample to be tested is loaded on the substrate, and spectral testing is performed through a reflective bright-field microscopic hyperspectral imaging system, and feature extraction and analysis are performed in combination with a deep neural network or a support vector machine.

Benefits of technology

It realizes that the spectral characteristic information of the sample is significantly enhanced and the spectral information is interpreted accurately and efficiently under the conditions of low equipment costs. It is suitable for biomedical, materials science and environmental monitoring and other fields.

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Abstract

The invention discloses a hyperspectral data analysis method based on a plasma resonance metal film substrate. According to the metal nano-film substrate, scattering and signal absorption of a sample to be detected in a visible spectrum range can be remarkably enhanced through a plasma resonance effect, and meanwhile, a reflection spectrum from the substrate is effectively inhibited. The enhancement mechanism not only can realize a high contrast effect similar to dark field imaging in a reflection mode, but also does not need to use expensive optical components, so that the equipment cost can be effectively reduced. In addition, the hyperspectral data is efficiently processed and analyzed by combining a deep learning technology, and high-sensitivity and high-resolution microscopic hyperspectral imaging analysis is realized. According to the method disclosed by the invention, the spectral characteristic information of the sample can be remarkably enhanced under the condition of relatively low equipment cost, and the spectral information can be accurately and efficiently interpreted. The method is suitable for the fields of biomedicine, material science and environmental monitoring.
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