Value the Unique Merit of HPTLC Image Analysis and Extending its Performance by Digitalization for Herbal Medicines Quality Control

Abstract

It is well known that High-Performance Thin Layer-Chromatography (HPTLC), the off-line planar chromatographic technique, has been employed in the pharmacopoeias of many countries for identification by virtue of its low-cost, less dependent on expensive equipment, flexible mobile phase composition and easy post-derivatization. In particular, the unique merit of HPTLC is able to provide the picture-like images of multiple samples in parallel on the same plate for instant viewing. In addition to the two dimension parameters (migration distance (R f value), integration peak value), the attractive color image, as the third dimension parameter, enhances the potency of HPTLC identification. The bioactivity of herbal medicines released from its compound bioactive ingredients in a holistic manner. Therefore the detected total chemical composition in the image should be reasonably more meaningful to assessing the inherent quality than only any selected single marker constituent of the given herbal drug. A vivid colorful picture-like HPTLC image could be easily recognized but not described properly by words. Hence extending the plate spot/band capacity on the plate for involving the bioactive components as much as possible. Combining digitizing the HPTLC image, the acquired infographic (HPTLC image, the digital scanning profile and the integrated parameters) can be used for establishing the fingerprint common pattern of the given species. It can be coupled with chemometrics analysis for more effectively quantifiable assessing the inherit quality of the herbal drugs. In this paper we reported methodically some Chinese herbal drugs analysis via various levels for demonstration of the practical application in QC.

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

“…Though increasing, there are still a limited number of research papers on the combination of HPTLC with multivariate data analysis. Most of these are based on the investigation of propolis, herbal samples, biopolymers and microalgae [6][7][8][9][10][11][12][13][14][15][16] . Although propolis is one of the most investigated honeybee product, the separation of its complex phenolic compound composition is still challenging analysts.…”

“…Various software, toolboxes and algorithms have been applied for image processing and multivariate analysis of HPTLC chromatograms so far (Table 1) [6][7][8][9][10][11][12][13][14][15][16] . Such packages lack in domain-specific functionality, which results in a manual, lumbering and time-consuming pipeline of the data handling.…”

“…Such packages lack in domain-specific functionality, which results in a manual, lumbering and time-consuming pipeline of the data handling. The user is forced to open, process and save the data through different software packages and toolboxes to perform the analysis [6][7][8][9][10][11][12][13][14][15][16] .…”