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

Quantification Strategies for Two-Dimensional Liquid Chromatography Datasets Using Regions of Interest and Multivariate Curve Resolution Approaches

“…Regarding previous validation of the ROIMCR strategy for absolute quantification purposes for both LC-MS and GC-MS datasets, similar values to the obtained in the present work were achieved (i.e., relative errors lower than 10% for most of the calibration mixtures and lower than 20% for drug samples) [21,22]. It is important to highlight that these results were obtained with a classical MCR-ALS calibration, and only non-negativity and spectral normalization (i.e., equal height) were selected as constraints.…”

“…Besides, although in this work only the linear concentration range between 10 and 50 ppm was used (due to the low sensitivity of the mass detector employed), other previous applications of MCR-ALS with and without the correlation constraint have demonstrated that it can be used in other applications and concentration ranges, for instance with calibration curves in the ppb range (e.g., from 1 to 400 ppb [22], or from 5 to 25 ppb [24]). This broad applicability is because the detection limit is conditioned by the sensitivity of the detector, rather than by the chemometric approach itself.…”

“…The second quantification strategy proposed in this work is a classic calibration curve obtained with the areas from the MCR-ALS resolved elution profiles. This approach has been previously validated for the quantitative analysis of one-dimensional liquid chromatography coupled to mass spectrometry (LC-MS) and gas chromatography coupled to mass spectrometry (GC-MS) datasets [21,22]. Lastly, the third procedure implies using the area correlation constraint [23][24][25] during the MCR-ALS optimization.…”

Optimization of Parameters for ROI Data Compression for Nontargeted Analyses Using LC–HRMS

“…From a fundamental standpoint, one might be concerned with a fixed signal threshold due to baseline changes throughout a chromatogram; however, the fixed signal threshold allows for simultaneous compression of the entire chromatograms. ROI compression is generally used in time segments, with different thresholds selected based on the number of ROIs extracted, and with chemometric decomposition for obtaining pure spectra and elution profiles to aide quantitation and identification efforts . However, this method requires an initial guess of the number of components to model and is prone to splitting peaks, requiring a time segment overlap and additional processing steps (i.e., removing redundant analyte peaks and augmentation to rebuild chromatograms).…”

Extracting Information and Enhancing the Quality of Separation Data: A Review on Chemometrics-Assisted Analysis of Volatile, Soluble and Colloidal Samples

“…Perin et al [55], for example, studied the anthropogenic contamination of Lake Guaíba, Brazil, analyzing samples with GC-MS, LC-MS and Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Puorasil et al [56] developed a chemometric method based on multivariate curve resolution (MCR) to improve the quality of GC-MS data (overcoming the problems of poor peak alignment and coelution of species) to study both qualitatively and quantitatively the indoor pollution. Mazur et al [57] used GC-MS to identify pollutants in snow samples coming from several areas of Moscow city, and CA and Spearman correlation analysis [58] to evaluate the spatial distribution of pollutants.…”