Performance assessment of chemometric resolution methods utilized for extraction of pure components from overlapped signals in gas chromatography-mass spectrometry

摘要

Multivariate resolution technique is a set of mathematical tools that uncovers the underlying profiles from a set of measurements of time evolving chemical systems. This technique was proposed for resolving the overlapping GC-MS peaks into pure chromatogram and mass spectra. In this paper, several common resolution chemometric techniques in GC-MS resolution such as mean field-independent component analysis (MF-ICA), multivariate curve resolution-alternating least squares (MCR-ALS), and multivariate curve resolution-objective function minimization (MCR-FMIN) were investigated. The obtained solutions using chemometric methods are assessed by lack of fit (LOF) and R~2. Results show that all solutions by fulfilling the same constraints have same performance in resolving high overlapping peaks. Also, the differences obtained in each case should be related to the unresolved rotational ambiguity. Among the different ambiguities such as intensity, permutation and rotation in resolution methods, rotational ambiguity is the most difficult and critical one. Because of rotational ambiguity, there is a set of feasible MCR solutions, which explain equally well the observed experimental data, and fulfill sufficiently the imposed constraints of the system. So in these methods, a range of feasible solutions exist. The rotational ambiguities of the profiles are a challenging fact which complicates the development of stable and universal self-modeling curve resolution (SMCR) algorithms. The relative component contribution (RCC) function values for the component profiles obtained by the different methods are calculated by MCR-BANDS. The values of RCC for these three methods are equivalent. Rotational ambiguities of the solutions of SMCR methods can be reduced by applying suitable constraints. The obtained results show, using data sets, which are arranged in a single augmented data matrix could be the best solution for reducing or removing of rotational ambiguity.