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Window factor analysis (WFA) is a powerful tool in analyzing evolutionary process. However, it was found that window factor analysis is much sensitive to the noise involved in original data matrix. An error analysis was done with the fact that the concentration profiles resolved by the conventional window factor analysis are easily distorted by the noise reserved by the abstract factor analysis (AFA), and a modified algorithm for window factor analysis was proposed. Both simulated and experimental HPLC-DAD data were investigated by the conventional and the improved methods. Results show that the improved method can yield less noise-distorted concentration profiles than the conventional method, and the ability for resolution of noisy data sets can be greatly enhanced.
However, it was found that window factor analysis is much sensitive to the noise involved in original data matrix. An error analysis was done with the fact that the concentration profiles resolved by the conventional window factor analysis are easily distorted by the noise reserved by the abstract factor analysis (AFA), and a modified algorithm for window factor analysis was proposed. Both simulated and experimental HPLC-DAD data were investigated by the conventional and the improved methods. Results show that the improved method can yield less noise-distorted concentration profiles than the conventional method, and the ability for resolution of noisy data sets can be greatly enhanced.