Proof-of-Principle of rTLC, an Open-Source Software Developed for Image Evaluation and Multivariate Analysis of Planar Chromatograms

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.

Investigation of species and environmental effects on rhubarb roots metabolome using 1H NMR combined with high performance thin layer chromatography

“…To reduce technical variations, the intensity of each spot (Rf) was normalized by the intensity of the corresponding Rf value in the QC samples. The normalized data was processed by rTLC (Fichou et al 2016 ) for multivariate data analysis similarly to 1 H NMR data, for which the data obtained from the three systems was combined to a single data matrix.…”

“…The HPTLC data was processed using rTLC (version 1.0) according to the method developed by Fichou et al ( 2016 ). Dimensions used to extract images were the same as those used for the sample application.…”

Metabolic Profiling of Saponin-Rich Ophiopogon japonicus Roots Based on 1H NMR and HPTLC Platforms

“…The rTLC V.1.0 program was used (http://shinyapps. ernaehrung.uni-giessen.de/rtlc/) for data matrix extraction from the HPTLC images [32]. The parameters used for data acquisition were the same as those used for sample application on the plates, with a pixel width of 128.…”

Metabolômica De Plantas: Métodos E Desafios

“…113 Dentre as técnicas mais comuns, destacam-se a cromatografia gasosa e líquida acoplada a espectrometria de massas (CG-EM ou CLAE-EM, respectivamente), a injeção direta em espectrometria de massas (ID-EM) e a ressonância magnética nuclear (RMN). Porém, outras técnicas como a eletroforese capilar acoplada a espectrometria de massas (EC-EM), 114,115 cromatografia em camada delgada de alta eficiência (HPTLC -High performance thin-layer chromatography) [116][117][118] e a cromatografia líquida com detecção por arranjos de diodos (CLAE-DAD) 119,120 também são utilizadas na análise de extratos de plantas, com aplicações metabolômicas significativas. A Tabela 2 mostra algumas das plataformas analíticas em uso nos estudos metabolômicos de plantas.…”