Application of zero-phase digital filtering for effective denoising of field asymmetric waveform ion mobility...

Field asymmetric waveform ion mobility spectrometry (FAIMS) has a great potential to become a portable technology for rapid detection of chemical and biological agents. However, the ion current signals, measured at the exit of the planar FAIMS directly, may contain different types of noises. The peak information in the FAIMS spectrum, such as the compensation voltage (CV) value at the maximum peak intensity (CV_P ) and the peak width at half maximum (W_h ), could not be accurately determined under the weak signal condition, which significantly limits the achievable instrument sensitivity, and there are no existing solutions to the problem.

This study analyzed the noise type of FAIMS signal in detail, and three different signal processing algorithms, such as median filtering (MF), discrete wavelet transform (DWT), and zero-phase digital filtering (ZDF), were evaluated for their performance in denoising the FAIMS signal.

The results show that the standard deviation of CV_p obtained from the signal denoised using ZDF algorithm is at least 31.82% smaller as compared to using MF and DWT algorithms. The standard deviation of W_h is at least 45.45% smaller using ZDF algorithm. Moreover, only ZDF algorithm can keep the percentage error for the CV value of the denoised signal to be within 0.50 ± 0.47% of the true CV value, implying the effectiveness of ZDF algorithm in denoising while retaining the integrity of the signal.

The ZDF algorithm greatly reduces the analyte peak extraction error and improves the limit of detection in FAIMS measurements.