Employing complementary multivariate methods for a designed nontarget LC-HRMS screening of a wastewater-influenced river

Non-targeted Gas Chromatography Orbitrap Mass Spectrometry qualitative and quantitative analysis of semi-volatile organic compounds in indoor dust using the Regions of Interest Multivariate Curve Resolution chemometrics procedure

“…Multivariate curve resolution-alternating least squares (MCR-ALS) on the other hand, is a powerful chemometric method able to analyze multicomponent systems with strongly overlapping contributions from complex chemical systems, including chromatographic analysis of environmental samples with a large number of chemical constituents [ 15 , 16 ]. The combination of the ROI and MCR-ALS methods is called the ROIMCR procedure and has been already applied in different research papers of liquid chromatography-high-resolution mass spectrometry (LC-HRMS) [17][18][19][20][21][22][23][24][25][26] . The ROIMCR procedure is inherently a non-targeted approach because it does not require any preliminary knowledge or chemical information about the sample constituents.…”

Non-target screening in water analysis: recent trends of data evaluation, quality assurance, and their future perspectives

“…Therefore, data dimensionality can be reduced, decreasing the risk of incomplete componentization and missing compounds that cannot be detected by feature-based peak detection [ 21 ]. Data processing protocols based on multiple samples align well with real-world aquatic NTS advancements and perspectives, such as monitoring pollution pathways in river water, wastewater samples undergoing chemical or biological treatment, or water samples measured under a variety of extraction/instrumental conditions [ 20 , 22 , 23 ].…”

Enhanced efficiency of MS/MS all-ion fragmentation for non-targeted analysis of trace contaminants in surface water using multivariate curve resolution and data fusion

“…Data compression and matrix construction can also be conducted according to searches of regions of interest (ROI), which are regions of data points with a high density ranked by a certain "data void" [ 20 , 21 ]. Following the coupling of ROI with the MCR-ALS method in metabolomics studies [ 22 – 24 ], the method has been utilized for non-target analysis in environmental metabolomics [ 25 ], micropollutant screening in aquatic environments [ 15 , 26 ], wastewater proteomics [ 27 ], polymer degradation in aquatic environments [ 28 ], and recently in the processing of different MS acquisition modes in an non-targeted metabolomics study [ 29 ] . The main strength of employing MCR-ALS in NTA studies is that, unlike most data processing strategies which are based on analyzing each m/z channel (feature) at a time for each sample and require alignment and finally a componentization step, MCR-ALS is based on a bilinear factor decomposition concept.…”