| dc.description.abstract | High-performance thin-layer chromatography (HPTLC) is an advantageous analytical technique for analysis of complex samples. Combined with multivariate data analysis, it turns out to be a powerful tool for profiling of many samples in parallel. So far, chromatogram analysis has been time-consuming and required the application of at least two software packages to convert HPTLC chromatograms into a numerical data matrix. Hence, this study aimed to develop a powerful, all in one open-source software for user-friendly image processing and multivariate analysis of HPTLC chromatograms. Using the caret package for machine learning, the software was set up in the R programming language with an HTML−user interface created by the shiny package. The newly developed software, called rTLC, is deployed online, and instructions for direct use as a web application and for local installation, if required, are available on GitHub. rTLC was created especially for routine use in planar chromatography. It provides the necessary tools to guide the user in a fast protocol to the statistical data output (e.g., data extraction, preprocessing techniques, variable selection, and data analysis). rTLC offers a standardized procedure and informative visualization tools that allow the user to explore the data in a reproducible and comprehensive way. As proof-of-principle of rTLC, German propolis samples were analyzed using pattern recognition techniques, principal component analysis, hierarchic cluster analysis, and predictive techniques, such as random forest and support vector machines. © 2016 American Chemical Society. | en |
