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• W4328049183 abstract "Applied sciences have increased focus on omics studies which merge data science with analytical tools. These studies often result in large amounts of data produced and the objective is to generate meaningful interpretations from them. This can sometimes mean combining and integrating different datasets through data fusion techniques. The most strategic course of action when dealing with products of unknown profile is to use exploratory approaches. For omics, this means using untargeted analytical methods and exploratory data analysis techniques. The current study aimed to perform data fusion on untargeted multimodal (negative and positive mode) liquid chromatography–high-resolution mass spectrometry data using multiple factor analysis. The data fusion results were interpreted using agglomerative hierarchical clustering on biplot projections. The study reduced the thousands of spectral signals processed to less than a hundred features (a primary parameter combination of retention time and mass-to-charge ratios, RT_m/z). The correlations between cluster members (samples and features from) were calculated and the top 10% highly correlated features were identified for each cluster. These features were then tentatively identified using secondary parameters (drift time, ion mobility constant and collision cross-section values) from the ion mobility spectra. These ion mobility (secondary) parameters can be used for future studies in wine chemical analysis and added to the growing list of annotated chemical signals in applied sciences." @default.
• W4328049183 created "2023-03-22" @default.
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• W4328049183 date "2023-03-21" @default.
• W4328049183 modified "2023-09-27" @default.
• W4328049183 title "Exploratory data fusion of untargeted multimodal LC-HRMS with annotation by LCMS-TOF-ion mobility: White wine case study" @default.
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• W4328049183 doi "https://doi.org/10.1177/14690667231164096" @default.
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