Recent trends in image evaluation of HPTLC chromatograms

In the current article, an overview of recent applications and opportunities of image evaluation of high-performance thin-layer chromatograms (HPTLC) in food analysis and natural product research is presented. The article shortly covers the aspects of specialized software packages for image analysis...

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Bibliographic Details
Published in:Journal of liquid chromatography & related technologies 2020-06, Vol.43 (9-10), p.291-299
Main Authors: Ristivojević, Petar, Trifković, Jelena, Andrić, Filip, Milojković-Opsenica, Dušanka
Format: Article
Language:English
Subjects:
Computer programs
Fingerprint
Fingerprinting
Food
Food analysis
Food production
Image acquisition
Image analysis
Image processing
Natural products
Noise reduction
Pattern recognition
Performance evaluation
planar chromatography
quantification
Regression analysis
software
Software packages
Subtraction
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Summary:In the current article, an overview of recent applications and opportunities of image evaluation of high-performance thin-layer chromatograms (HPTLC) in food analysis and natural product research is presented. The article shortly covers the aspects of specialized software packages for image analysis, image post-correction, signal acquisition, and preprocessing. Contemporary aspects of image-based HPTLC fingerprinting, quantification of target compounds, and advanced chemometric modeling were reviewed. Special attention was dedicated to freely available software packages. Advantages and disadvantages of each one were discussed in terms of abilities to obtain chromatographic profiles and perform crucial steps in signal manipulation, such as background subtraction, denoising, and background detrending. The most frequently applied techniques for signal manipulation have been discussed and recommendation provided. In that sense, the article aims to provide a valuable guideline for readers dealing with the application of HPTLC image analysis in food and natural products research, especially in connection with the most frequently used chemometric techniques, in domains of pattern recognition, classification, and regression.
ISSN:1082-6076
1520-572X
DOI:10.1080/10826076.2020.1725555