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Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

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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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Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

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Published on: August 22, 2019

Autonomous adaptive data acquisition for scanning hyperspectral imaging.

Elizabeth A Holman¹, Yuan-Sheng Fang^2,3, Liang Chen³

¹Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.

Communications Biology

|November 19, 2020

Summary

This summary is machine-generated.

We developed a novel adaptive sampling method to speed up spectral microscopy, enabling real-time biochemical imaging. This technique significantly reduces data acquisition time for complex biological samples.

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Area of Science:

Biophysics
Chemical Imaging
Microscopy

Background:

Spectral microscopy offers valuable biochemical insights but suffers from long acquisition times, limiting the study of dynamic biological processes.
Current methods generate high-dimensional data, requiring extensive time for image acquisition, hindering real-time analysis.
Observing transient biological events is challenging due to the slow speed of traditional spectromicroscopy.

Purpose of the Study:

To develop an efficient, autonomous, grid-less adaptive sampling method for spectromicroscopy.
To accelerate image acquisition while enhancing data density in critical regions.
To enable real-time spatiochemical imaging for biological applications.

Main Methods:

Implemented a grid-less autonomous adaptive sampling strategy with scanning Fourier Transform infrared spectromicroscopy.
Compared the adaptive sampling method against standard uniform grid sampling.
Utilized performance metrics and multivariate infrared spectral analysis for quantitative and qualitative assessment.

Main Results:

The grid-less adaptive sampling method significantly decreased image acquisition time.
Increased sampling density in areas with steep physico-chemical gradients was achieved.
The method demonstrated superior performance in both a two-component chemical model and a complex biological sample (Caenorhabditis elegans).

Conclusions:

The developed adaptive sampling method enhances the efficiency of spectromicroscopy for spatiochemical imaging.
This approach overcomes the limitations of long acquisition times, paving the way for real-time biological process observation.
The technique shows promise for advancing biochemical analysis in complex biological systems.