High dynamic range in videodensitometry—a comparative study to classic videoscanning on Gentiana extracts

Videodensitometric approaches in thin-layer chromatography (TLC) can be traced back to the 1980s [1], but the real practical possibility of computer image processing appeared in the 1990s. A typical equipment consisted then of analog CCD (charge-coupled device) camera and a special PCI (peripheral component interconnect) card, which allowed framegrabbing [2, 3]. Papers describing videodensitometry in these years did not dig deeply into the theory or further development of the method. These ideas appeared later, when the image acquisition and processing possibilities increased substantially in a short period of time due to the progress in computer hardware development and the switch to digital photography. Chromatographers became interested in the development of algorithms for background removal [4], quantitative processing [5], to combine them into complete workflows [6, 7], optimization of experimental conditions [8,9,10,11], combine them with bioautography [12], or to separate overlapping spots in multivariate way [13]. As an alternative to camera approach, a classical flatbed scanner was also successfully introduced [14,15,16], even with inkjet printer used as an applicator [17]. A smartphone with dedicated software was also proposed numerous times as a videoscanning equipment [18,19,20,21,22,23], making the whole analysis available to many researchers without the need of buying a special hardware.

The current possibilities of open-source software allow processing of TLC images with advanced chemometric algorithms as well as the development of a new code [7]. The idea of open source is excellent, because the software is available without any cost, with the possibility to adjust for a particular need the source code by researchers knowing the basics of programming. The most often used open-source software is ImageJ, written in Java [24, 25].

In contrast to classical densitometry, there is no possibility to choose any wavelength during videoscanning. A color digital camera records each pixel as three intensities abbreviated as RGB: red, green, and blue visible light, respectively. Image acquisition under 254 nm, 366 nm, and visible light illumination gives nine channels of information, where fluorescence of the analyzed compounds as well as its quenching on fluorescent plate is a main phenomenon responsible for the obtained information [26, 27]. Such multichannel datasets are fantastic targets of advanced pattern recognition algorithms [28, 29].

The standard depth of a digital image is 8 bit, which means that each pixel is stored as three integer numbers of range 0–255 for each RGB channel (16,777,216 possible colors). This is the case for ‘bitmap’ (BMP), Joint Photographic Experts Group (JPG), and ‘portable network graphics’ (PNG) image formats, commonly used in videoscanning and classical digital photography. However, the tonal range for such images is limited and there is no possibility to make one shot of a scene with very bright and very dark areas, preserving details in both regions. A photographer must decide whether it is required to have bright details with totally black shadows or expose dark details with totally white (overexposed) lights.

This problem led to the development of ‘high dynamic range’ (HDR) photography where several shots of one scene are taken with increasing exposures [30]. This allows to combine them to one image with increased tonal range for further processing and save in one file (formats such as ‘tagged image file format’ [TIFF], ‘extended range’ [EXR], HDR are able to store high-density data) [31]. HDR images can be viewed correctly only on special devices, a typical computer monitor can display them only with much decreased contrast or the user must manipulate the exposure to see details in bright or dark areas. There are numerous algorithms to convert them to normal image (called in this context ‘low dynamic range’—LDR) in adaptive way, by varying exposure in different areas, which is called tonemapping [32].

To the best of our knowledge, the HDR approach in videodensitometry is not present in literature. Our practice shows that uneven illumination or vignetting during thin-layer photographing of a plate can visibly change the optimal exposure for various regions of the plate, often resulting in some compromise. Therefore, we have decided to test how HDR can change videoscanning and what can be achieved in this area with an inexpensive equipment.

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