ArticleAnalytical science advances2025
Comprehensive Discrimination of Amomi Fructus From Different Origins Using UHPLC-Q-Orbitrap MS, HS-GC-MS/MS, NMR and MIR Technologies Based On Data Fusion Strategies.
Article in Analytical science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Amomi Fructus (SR) is an important edible herb widely used as a spice and traditional Chinese medicine. To comprehensively solve the serious practical problems of origins and species confusion in SR, the systematic characterization methods were established by liquid chromatography-mass spectrometer, gas chromatography-mass spectrometer, nuclear magnetic resonance and infrared spectroscopy. A total of 286 compounds and functional group information were detected. The classification of SR from different origins was performed by data fusion models built using random forest (RF) and other algorithms. A mid-level data fusion model (an RF model established after combining the features selected by RF and RF-RF) performed the best classification. Then 27 differential compounds (including flavonoids, polyphenols and terpenoids) and their functional group information were screened for external verification and could significantly improve the groups' separation effect just by simple principal component analysis. A more comprehensive and accurate means of analysis was found.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.