A novel similarity search approach for high‐performance thin‐layer chromatography (HPTLC) fingerprinting of medicinal plants. Issue 4 (19th February 2019)

• Record Type: Journal Article
• Authors: Ebrahimi‐Najafabadi, Heshmatollah; Kazemeini, Seyed Sina; Pasdaran, Ardalan; Hamedi, Azadeh
• Is Part Of: Phytochemical analysis. Volume 30:Issue 4(2019)
• Page Start: 405
• Page End: 414
• Publication Date: 2019-02-19
• Subjects: fingerprint -- HPTLC -- image analysis -- medicinal plant -- software; Plants -- Analysis -- Periodicals; Plants -- chemistry -- Periodicals; 572.2
• DOI: 10.1002/pca.2823
• Languages: English
• ISSNs: 0958-0344

Abstract

Abstract: Introduction: In addition to the development of analytical equipment, another movement has also appeared in the field of computer assisted techniques for metabolite assessment. Although, some studies can be found in the literature there is still not available reliable and user‐friendly software which is coupled with a simple chromatography method for developing a database to identify medicinal plants. Objectives: Developing a novel similarity search approach for high‐performance thin‐layer chromatography (HPTLC) fingerprinting. Methods: Combined HPTLC with image analysis approach was used for similarity assessment of 70 standard medicinal plants. Ethyl acetate–ethyl methyl ketone–formic acid 98%–water (50:30:10:10) were chosen among different examined mobile phases. Liebermann–Burchard and anisaldehyde reagents were chosen for HPTLC derivatisation for visualisation. Image analysis based on Cannys' method was used to determine the spot size of each HPTLC image. A similarity search algorithm based on colour (RGB, HSV and Lab) information alone or together with retardation factor ( R f ) and spot size information calculated with the software was built to assess the fingerprinting of medicinal plants. Results: The software was capable of calculating spots size and R f values. It authenticated unknown samples based on comparing images information, spots size and/or R f in the built database. Similarity values were 75–96% for the selected plants chromatograms with those of the same plant in the database. It presents better results than principal components analysis (PCA), classification and regression trees (CART) and partial least squares discriminant analysis (PLS‐DA). Conclusion: The procedure paves the way for constructing a database of HPTLC images of medicinal plants. Abstract : A novel similarity search approach for high‐performance thin‐layer chromatography (HPTLC) fingerprinting was developed. A similarity search algorithm based on colour (RGB, HSV and Lab) information alone or together with retardation factor (R f ) and spot size information calculated with the software was built to assess the fingerprinting of medicinal plants. Similarity values were 75–96% for the selected plants chromatograms with those of the same plant in the database.