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.

On the basis of open-source packages, 3D printing of thin silica gel layers is demonstrated as proof-of-principle for use in planar chromatography. A slurry doser was designed to replace the plastic extruder of an open-source Prusa i3 printer. The optimal parameters for 3D printing of layers were studied, and the planar chromatographic separations on these printed layers were successfully demonstrated with a mixture of dyes. The layer printing process was fast. For printing a 0.2 mm layer on a 10 cm × 10 cm format, it took less than 5 min. It was affordable, i.e., the running costs for producing such a plate were less than 0.25 Euro and the investment costs for the modified hardware were 630 Euro. This approach demonstrated not only the potential of the 3D printing environment in planar chromatography but also opened new avenues and new perspectives for tailor-made plates, not only with regard to layer materials and their combinations (gradient plates) but also with regard to different layer shapes and patterns. As such an example, separations on a printed plane layer were compared with those obtained from a printed channeled layer. For the latter, 40 channels were printed in parallel on a 10 cm × 10 cm format for the separation of 40 samples. For producing such a channeled plate, the running costs were below 0.04 Euro and the printing process took only 2 min. All modifications of the device and software were released open-source to encourage reuse and improvements and to stimulate the users to contribute to this technology. By this proof-of-principle, another asset was demonstrated to be integrated into the Office Chromatography concept, in which all relevant steps for online miniaturized planar chromatography are performed by a single device.

This paper presents the ideas for the 2021 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum-CLEF Labs 2021 in Bucharest, Romania. ImageCLEF is an ongoing evaluation initiative (active since 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2021, the 19th edition of ImageCLEF will organize four main tasks: (i) a Medical task addressing visual question answering, a concept annotation and a tuberculosis classification task, (ii) a Coral task addressing the annotation and localisation of substrates in coral reef images, (iii) a DrawnUI task addressing the creation of websites from either a drawing or a screenshot by detecting the different elements present on the design and a new (iv) Aware task addressing the prediction of real-life consequences of online photo sharing. The strong participation in 2020, despite the COVID pandemic,

An artificial neural network (ANN) is presented as a new and superior technique for processing planar chromatography images. Though several algorithms are available for image processing in planar chromatography, the use of ANN has not been explored so far. It simulates how the human brain interprets images, and the intrinsic features of the image were captured on patches of pixels and successfully reconstructed afterward. The obtained high number of observations was a perfect basis for using ANN. As examples, three quite different data sets were processed with this new algorithm to demonstrate its versatility and benefits. Powerful features, which the ANN learned from the image data set, improved the quality of the analytical data. Thus, noise or inhomogeneous background of bioautograms was removed as demonstrated for salvia extracts, improving their bioquantifications. On colorful fluorescence chromatograms of further botanical extracts, the power and benefit of the feature extraction were demonstrated. Using ANN, videodensitometric results were improved. If compared to conventional digital processing, the resolution between two adjacent blue fluorescent bands increased from 0.95 to 1.18 or between two orange fluorescent bands from 0.77 to 1.57. The trueness of the new ANN was successfully verified by comparison with conventional densitometric results of the absorbance of separated tea extracts. The correlation coefficients of epigallocatechin gallate therein improved from 0.9889 with median filter to 0.9959 using this new ANN algorithm. The code was released open-source to the scientific community as a ready-to-use tool to exploit this potential, spread its usage, and boost improvements in planar chromatographic image evaluation.

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