Nonnegative principal component analysis in thin layer fingerprint screening: A case of Gentiana extracts from in vitro cultures. Issue 15 (2nd October 2021)

• Record Type: Journal Article
• Authors: Gadowski, Sebastian; Tomiczak, Karolina; Komsta, Łukasz
• Is Part Of: Journal of liquid chromatography & related technologies. Volume 44:Issue 15/16(2021)
• Page Start: 820
• Page End: 828
• Publication Date: 2021-10-02
• Subjects: Chemometrics -- fingerprinting -- Gentiana -- nonnegative PCA -- principal component analysis; Liquid chromatography -- Periodicals; Chromatographic analysis -- Periodicals; Separation (Technology) -- Periodicals; Biotechnology -- Periodicals; Chromatography, Liquid -- Periodicals; 543.8405
• DOI: 10.1080/10826076.2022.2089998
• Languages: English
• ISSNs: 1082-6076

Abstract

Abstract: Twenty-one species of Gentiana L. were successfully grown in vitro in the same conditions and 72 samples of various cultures of these species (root, shoots, cotyledon callus, hypocotyl callus, and root callus) were obtained. The investigated species were as follows: G. affinis, G. andrewsii, G. bhutanica, G. burseri, G. cachemirica, G. capitata, G. crassicaulis, G. dahurica, G. decumbens, G. freyniana, G. frigida, G. gelida, G. grossheimii, G. kurroo, G. macrophylla, G. paradoxa, G. robusta, G. scabra, G. septemfida, G. siphonantha, and G. tianschanica . The obtained samples were extracted with methanol–acetone–water (3:1:1) mixture, evaporated to dryness, and subjected to TLC on silica gel with mobile phase ethyl acetate–methanol–water (8:2:2) in sandwich mode. Data fusion of two densitometric modes: extinction at 254 nm and fluorescence at 312 nm (emission above 370 nm) were used as a fingerprint of each sample. Densitograms were denoised, subjected to baseline removal, warped, and analyzed with principal component analysis (PCA), hierarchical cluster analysis and—the novel proposal—nonnegative PCA. This allowed to split negatively intercorrelated fingerprint features to subsequent principal components (PCs), which increases the number of PCs required to see the same information, but these PCs are much more informative. Graphical Abstract: UF0001