Trends in artificial intelligence, machine learning, and chemometrics applied to chemical data

Title data

Houhou, Rola ; Bocklitz, Thomas:
Trends in artificial intelligence, machine learning, and chemometrics applied to chemical data.
In: Analytical Science Advances. Vol. 2 (2021) Issue 3-4 . - pp. 128-141.
ISSN 2628-5452
DOI: https://doi.org/10.1002/ansa.202000162

Official URL:

Abstract in another language

Artificial intelligence-based methods such as chemometrics, machine learning, and deep learning are promising tools that lead to a clearer and better understanding of data. Only with these tools, data can be used to its full extent, and the gained knowledge on processes, interactions, and characteristics of the sample is maximized. Therefore, scientists are developing data science tools mentioned above to automatically and accurately extract information from data and increase the application possibilities of the respective data in various fields. Accordingly, AI-based techniques were utilized for chemical data since the 1970s and this review paper focuses on the recent trends of chemometrics, machine learning, and deep learning for chemical and spectroscopic data in 2020. In this regard, inverse modeling, preprocessing methods, and data modeling applied to spectra and image data for various measurement techniques are discussed.

Further data

Item Type:	Article in a journal
Refereed:	Yes
Keywords:	2D chromatography; atomic force microscope; chemometrics; deep learning; electron microscope; inverse problem; machine learning; mass spectroscopy; nuclear magnetic resonance; preprocessing; vibrational spectroscopy; X-ray spectroscopy
Institutions of the University:	Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz
Result of work at the UBT:	No
DDC Subjects:	000 Computer Science, information, general works > 004 Computer science 500 Science > 530 Physics
Date Deposited:	11 May 2023 12:23
Last Modified:	11 May 2023 12:23
URI:	https://eref.uni-bayreuth.de/id/eprint/76397