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All the above estimations of bioactive compounds were carried out in triplicate and means were plotted.
Interestingly, the isolation and identification of those flavonoid-based bioactive compounds were carried out on the leaves part extracted using methanol, which is similar to the MEMC used in the present study.
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A multidimensional analysis of machine learning methods performance in the classification of bioactive compounds was carried out.
The isolation of bioactive compounds was carried out for the Fusarium sp. only.
The hierarchical clustering for the 37 bioactive compounds was carried out by using exactly the same algorithm as applied to the initial compound set.
The selected bioactive compounds were screened for their affinities against C-C loop region of EDA employing computer aided drug.
Similarly various bioactive compounds were isolated from A. javanica[17].
Two bioactive compounds were isolated from C. transvaalensis.
Higher contents of bioactive compounds were found in Lentinula edodes.
The structures of bioactive compounds were also elucidated using spectroscopic techniques (1D, 2D NMR spectroscopy, FTIR, UV, mass spectrometry, etc).. Chromatographic fingerprint (HPLC) and spectrophotometric fingerprinting (ATR-FTIR) analyses with reference markers were also carried out on the plant extract.
Bioactive compounds were isolated using various chromatographic techniques (VLC, column chromatography, Prep-TLC, etc).
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