Multivariate curve resolution based chromatographic peak alignment combined with parallel factor analysis to exploit...

Area of Science:

• Analytical Chemistry
• Chemometrics

Background:

• Complex chromatographic data often presents challenges due to overlapping peaks and elution time shifts.
• Existing methods may struggle to accurately identify and quantify target compounds in the presence of interferences.

Purpose of the Study:

• To develop and evaluate a novel chemometric strategy for analyzing complex chromatographic measurements.
• To exploit the second-order advantage for improved data decomposition and analysis.

Main Methods:

• A new combination of multivariate curve resolution-correlation optimized warping (MCR-COW) with trilinear parallel factor analysis (PARAFAC) was developed.
• Data complexity was reduced using MCR-COW, followed by trilinear decomposition with PARAFAC.
• The strategy was validated using simulated and real high-performance liquid chromatography-diode array detection (HPLC-DAD) datasets.

Main Results:

• MCR-COW effectively corrected elution time shifts for overlapped target compounds.
• PARAFAC analysis of aligned data enabled unique decomposition of overlapped peaks.
• The MCR-COW-PARAFAC strategy demonstrated improved performance over traditional methods like PARAFAC, MCR-ALS, and MCR-COW-MCR.

Conclusions:

• The proposed MCR-COW-PARAFAC strategy offers a robust solution for analyzing complex chromatographic data.
• This approach enhances the ability to identify and quantify target compounds in challenging analytical scenarios.
• The method shows significant improvements in accuracy and reliability compared to existing techniques.