Identification of major carotenoids from green alga Tetraspora sp. CU2551: partial purification and characterization of lutein, canthaxanthin, neochrome, and β-carotene.
Thanaporn MaswannaCherdsak ManeeruttanarungrojPublished in: World journal of microbiology & biotechnology (2022)
The green algae Tetraspora sp. CU2551 was previously identified as a strain with high potential for biohydrogen production; however, its algal biomass characteristics changed from green to reddish orange within 43 days of biohydrogen production. The crude pigments were extracted, partially purified, and characterized by chemical determination. The present study focused on elucidating the carotenoid composition of the selected green alga Tetraspora sp. CU2551. The pigment extract was partially purified and fractionated using thin layer chromatography, and yielded two major and two minor carotenoid bands. The fractions were confirmed by high-performance liquid chromatography with a diode array detector (HPLC-DAD) before being identified and confirmed using Liquid Chromatograph-Quadrupole Time of Flight-Mass Spectrometry (LC-QTOF-MS). The spectral data of these fractions revealed four sub-fractions of interest that were lutein, canthaxanthin, neochrome, and β-carotene, which had percentages in the crude extracts of 30.57%, 25.47%, 7.89%, and 0.71%, respectively. Lutein and canthaxanthin were found to be the major carotenoid pigments present. Our findings in this present study are the first reporting of Tetraspora sp. CU2551 as a potential alternate source for carotenoid pigment production.
Keyphrases
- high performance liquid chromatography
- simultaneous determination
- mass spectrometry
- tandem mass spectrometry
- solid phase extraction
- ms ms
- liquid chromatography
- gas chromatography
- molecularly imprinted
- small cell lung cancer
- metal organic framework
- aqueous solution
- magnetic resonance imaging
- oxidative stress
- multiple sclerosis
- magnetic resonance
- wastewater treatment
- anti inflammatory
- emergency department
- deep learning
- brain metastases