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Updated: Jul 28, 2026

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
Published on: March 14, 2013
Data-Driven Soft Independent Modeling of Class Analogy in Paper Spray Ionization Mass Spectrometry-Based Metabolomics
Frederico G Pinto1, Iqbal Mahmud2, Vanessa Y Rubio3
1Institute of Chemistry, Federal University of Viçosa, Campus de Rio Paranaíba, Rio Paranaíba, Minas Gerais 36570-900, Brazil.
Abstract:
Sensitive, rapid, and meaningful diagnostic tools for prostate cancer (PC) screening are urgently needed. Paper spray ionization mass spectrometry (PSI-MS) is an emerging rapid technology for detecting biomarker and disease diagnoses. Due to lack of chromatography and difficulties in employing tandem MS, PSI-MS-based untargeted metabolomics often suffers from increased ion suppression and subsequent feature detection, affecting chemometric methods for disease classification. This study first evaluated the data-driven soft independent modeling of class analogy (DD-SIMCA) model to analyze PSI-MS-based global metabolomics of a urine data matrix to classify PC. The efficiency of DD-SIMCA was analyzed based on the sensitivity and specificity parameters that showed 100% correct classification of the training set, based on only PC and test set samples, based on normal and PC. This analytical methodology is easy to interpret and efficient and does not require any prior information from the healthy individual. This new application of DD-SIMCA in PSI-MS-based metabolomics for PC disease classification could also be extended to other diseases and opens a rapid strategy to discriminate against health problems.
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