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Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
Published on: September 2, 2020
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Trends in artificial intelligence, machine learning, and chemometrics applied to chemical data
Rola Houhou1,2, Thomas Bocklitz1,2
1Institute of Physical Chemistry Friedrich-Schiller-University Jena Jena Germany.
Analytical Science Advances
|May 8, 2024
Summary
Artificial intelligence (AI) methods like chemometrics, machine learning, and deep learning enhance chemical data analysis. This review highlights 2020 trends in AI for chemical and spectroscopic data, maximizing information extraction and application possibilities.
Area of Science:
- Analytical Chemistry
- Data Science
- Computational Chemistry
Background:
- Artificial intelligence (AI) methods, including chemometrics, machine learning (ML), and deep learning (DL), offer powerful tools for data interpretation.
- These AI techniques enable comprehensive data utilization, maximizing insights into processes, interactions, and sample characteristics.
- The application of AI in chemical data analysis dates back to the 1970s, with continuous advancements in recent years.
Purpose of the Study:
- To review recent trends in AI-based methods for chemical and spectroscopic data analysis, focusing on developments in 2020.
- To explore the application of chemometrics, machine learning, and deep learning in extracting information from chemical datasets.
- To discuss the enhanced application possibilities of chemical data through advanced AI techniques.
Main Methods:
- Focus on recent trends in chemometrics, machine learning, and deep learning applied to chemical and spectroscopic data from 2020.
- Discussion of inverse modeling techniques for chemical data analysis.
- Examination of data preprocessing and modeling strategies for spectral and image data across various measurement techniques.
Main Results:
- AI-based methods significantly improve the understanding and utilization of chemical data.
- Recent trends show increased sophistication in ML and DL applications for spectroscopic and image data.
- Effective preprocessing and inverse modeling are crucial for maximizing information from complex chemical datasets.
Conclusions:
- Chemometrics, machine learning, and deep learning are essential for modern chemical data analysis.
- AI tools enable more accurate and automated information extraction, expanding the utility of chemical data.
- Continued research in AI for chemistry promises further breakthroughs in understanding and application.
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