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Gas Chromatography: Types of Detectors-II

Gas Chromatography: Types of Detectors-II

In gas chromatography, different detectors are employed to meet specific analytical needs. These detectors are often categorized based on their detection mechanisms and the types of compounds they are best suited to analyze. Thermal Conductivity Detectors (TCD), Flame Ionization Detectors (FID), and Electron Capture Detectors (ECD) represent common categories, each with unique operating principles and applications. However, beyond these, several other detectors are designed for more specialized...

Gas Chromatography: Overview of Detectors

Gas Chromatography: Overview of Detectors

Detectors in gas chromatography (GC) help identify and quantify the components of a mixture by translating chemical properties into measurable signals, which are displayed on a chromatogram. Detectors can be categorized into two main types: destructive and non-destructive.
A non-destructive detector allows a sample to be analyzed without altering or consuming it, meaning the sample can be collected after detection for further analysis. Examples include thermal conductivity detectors and...

Gas Chromatography: Types of Detectors-I

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There are different types of detectors used in gas chromatography, each with its own specific properties that make it suitable for detecting certain types of analytes. The most commonly used detectors in GC are thermal conductivity detector (TCD), flame ionization detector (FID), and electron capture detector (ECD).
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Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing

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Optimization of the Mixed Gas Detection Method Based on Neural Network Algorithm.

Xiulei Li¹, Jiayi Guo¹, Wangping Xu¹

¹Department of Physics & Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan411105, PR China.

|January 26, 2023

Summary

This study introduces a faster, low-cost method for electronic nose (E-nose) mixed gas detection using partial adsorption data. The approach significantly reduces training data and detection time, enhancing E-nose efficiency.

Keywords:

convolutional neural network deep learning electronic nose fast detection gas sensor arrays mixed gas

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Area of Science:

Sensor Technology
Analytical Chemistry

Background:

Real-time mixed gas detection is crucial for electronic nose (E-nose) applications.
Current E-nose methods face challenges with long detection times and extensive training data requirements.

Purpose of the Study:

To develop a low-cost, fast method for mixed gas detection using partial adsorption data.
To reduce the training dataset size and prediction time for E-nose systems.

Main Methods:

Utilizing only a portion of the adsorption process response data as the training set.
Implementing a convolutional neural network (CNN) model for gas analysis.

Main Results:

Achieved new concentration prediction of mixed gas using only the first 10 seconds of response data.
Reduced the training set proportion to 60% while improving prediction accuracy.
Demonstrated that CNN models can achieve higher accuracy with smaller training sets.

Conclusions:

The proposed method significantly enhances the detection efficiency and accuracy of E-noses.
This approach offers a feasible solution for low-cost, rapid mixed gas detection in experimental measurements.