Abstract
Thin-layer chromatography (TLC) is an experimental separation technique for multi-compound mixtures widely applied in various fields of industry and research. In contrast to comparable techniques, TLC is straightforward, cost- and time-efficient, and well-applicable in field operations due to its flexibility. In TLC, after applying a mixture sample to the adsorbent layer on the TLC plate, the compounds ascent the plate at different rates due to their individual physicochemical characteristics, whereas separation is eventually achieved.
In this paper, we present novel computational techniques for automated TLC plate photograph profiling and fast TLC profile similarity scoring that allow advanced database accessibility for experimental TLC data. The presented methodology thus provides a toolset for automated comparison of plate profiles with gold standard or baseline profile databases. Impurities or sub-standard deviations can be readily identified. Hence, these techniques can be of great value by supporting the pharmaceutical quality assessment process.
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Notes
- 1.
In most software tools and programming languages, 256 color levels are implemented.
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We are very grateful to Pia Altenhofer, who provided TLC images, test sets and helpful ideas.
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Heinke, F., Beier, R., Bergmann, T., Mixtacki, H., Labudde, D. (2017). Novel Computational Techniques for Thin-Layer Chromatography (TLC) Profiling and TLC Profile Similarity Scoring. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation. BDAS 2017. Communications in Computer and Information Science, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-319-58274-0_30
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