No abstract

This paper proposes a new maximum likelihood approach for the deconvolution of identity and quantity of individual compounds based on the multicomponent mass spectra measured by mass spectrometry (MS). Mixture analysis of multicomponent mass spectra is, typically, based on a linear multicomponent mass spectrum model, where the compounds of the measured spectra to be solved are explicitly stated and assumed to be known. In many cases, however, the measured spectrum may contain unknown compounds that are not explicitly stated in the model and a commonly used least square (LS) solution fails. Moreover, a standard improvement over the LS method in these cases, namely the M-estimation (ME) approach, also suffers from this same problem. Our method overcomes the limitations of the LS and ME methods by modeling the effect of the unknown compound(s) to the residual of the linear model. The experimental results presented show that this new approach can separate more robustly the complex multicomponent mass spectra into their individual constituents compared to the LS and ME methods.

A nonlinear asymmetric error function-based least mean square method (NALMS) for the deconvolution of identity and quantity of individual compounds based on the multicomponent mass spectra measured by membrane inlet mass spectrometry (MIMS) was developed and detailed testing results are presented. The method utilizes the complete mass spectra of the compounds present in a reference library during the calculation of a solution for an unknown multicomponent mass spectrum. The results presented here and in our previous publications in which the NALMS method was utilized show that the method can be used to separate complex multicomponent electron ionization mass spectra into their individual constituents, thus considerably improving the selectivity of MIMS.

The aim of this study was to evaluate the benefits of the simultaneous use of two different analytical methods, namely Fourier transform infrared spectroscopy (FTIR) and mass spectrometry (MS), for online analysis of environmental and process samples. A mathematical method (NALMS) that identifies and quantifies all single components from a single multicomponent spectrum was previously developed for MS, and in this study, the same method, named as SPECTACS, was adopted for solving also an FTIR spectrum and a combined FTIR-MS spectrum. The performance of SPECTACS was evaluated by analyzing various gaseous samples, as case studies, containing volatile organic compounds, and the performance was compared with other methods, which are used to identify and quantitate organic compounds from multicomponent spectra. The results obtained show that SPECTACS with optimized noise reduction and solving a combined FTIR-MS spectrum can increase the reliability of identifying components in a single spectrum and also the accuracy in quantitative measurements when compared to the analysis with one analytical technique alone. The reasons for this improvement is evaluated and discussed in detail.