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Updated: Jun 13, 2025

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
00:07

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

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一种基于深度学习的等离子体图像频谱融合的光谱偏差错误逐步校正方法,用于提高LIBS的性能

Deng Zhang1, Honghua Ma2, Junfei Nie2

  • 1School of Computer and Electronic Information, Nanjing Normal University, Nanjing, Jiangsu, 210023, PR China.

Talanta
|September 14, 2024
PubMed
概括

本研究介绍了一种深度学习方法,用于纠正激光诱导分解光谱 (LIBS) 中的光谱不稳定性. 频谱偏差错误逐步校正方法 (SBESC-PISF) 在具有挑战性的条件下显著提高了定量分析的准确性.

关键词:
深度学习是一种深度学习.激光诱导的分解光谱学图像中的等离子体图像.化等离子体图像频谱它的光谱不稳定性.

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Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis08:22

Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis

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Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
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