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.

In this study, non-targeted gas chromatography-Orbitrap-mass spectrometry (GC-Orbitrap-MS) analysis of semi-volatile organic compounds (SVOCs) in indoor environmental dust samples is proposed. High-resolution mass spectrometry (HRMS) provides massive amounts of information-rich mass data which presents storage and processing challenges. Thus, a combination of the regions of interest (ROI) data filtering and mass compression method, together with the multivariate curve resolution-alternating least squares (MCR-ALS) data resolution method (which is called the ROIMCR procedure), is applied to solve huge data analysis challenges. The ROI method assures a significant reduction of the computer storage requirements of mass spectrometry data without any significant loss of spectral resolution nor of accuracy on m/z measures. On the other side, the MCR-ALS method allows the total resolution of the elution and spectral profiles of the different constituents present in the analyzed samples, not requiring their chromatographic peak alignment nor their chromatographic peak shape modelling using natural constraints like non-negativity. Since all the possible species are investigated by the ROIMCR method, it is a powerful tool for non-targeted analysis. In order to check that the sample constituents are correctly recovered and identified by the proposed ROIMCR procedure when is applied to non-targeted GC-Orbitrap-MS analysis, a set of lab-emulated dust samples at different concentration levels were qualitatively and quantitatively analyzed in detail. Then, to evaluate the performance of the proposed ROIMCR procedure, this method was applied to the same type of non-targeted GC-Orbitrap-MS analysis data of two real dust samples with unknown compositions. Many chemical compounds present in the lab-emulated dust samples were correctly identified and quantified in these dust samples. An additional number of extra chemical compounds were resolved in these real dust samples, whose identification as putative constituents of these samples is proposed. The ROIMCR procedure proposed in this work facilitates the simultaneous data processing of complex analytical samples and allows the detection and identification of possible extra sample constituents. As a final conclusion of this work, the combination of the GC-Orbitrap-MS and ROIMCR methods, is shown to be a reliable tool for the non-targeted qualitative and quantitative analysis of complex analytical and environmental samples.

Fast diagnosis of men's fertility using Raman spectroscopy combined with chemometric methods: An experimental study.

BACKGROUND: Idiopathic infertile men suffer from unexplained male infertility; they are infertile despite having a normal semen analysis, a normal history, and physical examination, and when female infertility factor has been ruled out. OBJECTIVE: The present study aimed to develop a metabolic fingerprinting methodology using Raman spectroscopy combined with Chemometrics to detect idiopathic infertile men vs. fertile ones by seminal plasma. MATERIALS AND METHODS: In this experimental study, the seminal plasma of 26 men including 13 fertile and 13 with unexplained infertility who reffered to, Avicenna Infertility Clinic, 2018, Tehran, Iran, have been investigated. The seminal metabolomic fingerprinting was evaluated using Raman spectrometer from 100 to 4250 cm-1. The principal component analysis and discriminate analysis methods were used. RESULTS: The total of 26 samples were divided into 20 training and 6 test sets. The Principal component analysis score plot of the training set showed that the data were perfectly divided into two sides of the plot, which statistically approves the direct effect of semen metabolome changes on the Raman spectra. A classification model was constructed by linear discriminant analysis using the training set and evaluated by the test group which resulted in completely correct classification. While three of the six test samples appeared in the fertile group, the rest appeared in the infertile as expected. CONCLUSION: Metabolic fingerprinting of seminal plasma using Raman spectroscopy combined with chemometric classification methods accurately discriminated between the idiopathic infertile men and the fertile ones and predicted their fertility type.