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From yellow petals of Iceland poppy, besides the known flavonoid gossypitrin, seven kaempferol derivatives were isolated. In addition to kaempferol 3-O-β-sophoroside and kaempferol 3-O-β-sophoroside-7-O-β-glucoside, known from other plants, the mono- and dimalonyl conjugates of the latter were identified by MS and NMR spectroscopy. Structure analyses of a set of co-occurring pigments, the nudicaulins, revealed that they have the identical acylated glycoside moieties attached to a pentacyclic indole alkaloid skeleton for which the structure of 19-(4-hydroxyphenyl)-10H-1,10-ethenochromeno[2,3-b]indole-6,8,18-triol was deduced from MS and NMR as well as chemical and chiroptical methods.
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Searching and mining nuclear magnetic resonance (NMR)-spectra of naturally occurring products is an important task to investigate new potentially useful chemical compounds. We develop a set-based similarity function, which, however, does not sufficiently capture more abstract aspects of similarity. NMR-spectra are like documents, but consists of continuous multi-dimensional points instead of words. Probabilistic semantic indexing (PLSI) is an retrieval method, which learns hidden topics. We develop several mappings from continuous NMR-spectra to discrete text-like data. The new mappings include redundancies into the discrete data, which proofs helpful for the PLSI-model used afterwards. Our experiments show that PLSI, which is designed for text data created by humans, can effectively handle the mapped NMR-data originating from natural products. Additionally, PLSI combined with the new mappings is able to find meaningful ”topics” in the NMR-data.