Quantitative Method for Liquid Chromatography–Mass Spectrometry Based on Multi-Sliding Window and Noise Estimation

Abstract

LC-MS/MS uses information on the mass peaks and peak areas of samples to conduct quantitative analysis. However, in the detection of clinical samples, the spectrograms of the compounds are interfered with for different reasons, which makes the identification of chromatographic peaks more difficult. Therefore, to improve the chromatographic interference problem, this paper first proposes a multi-window-based signal-to-noise ratio estimation algorithm, which contains the steps of raw data denoising, peak identification, peak area calculation and curve fitting to obtain accurate quantitative analysis results of the samples. Through the chromatographic peak identification of an extracted ion chromatogram of VD2 in an 80 ng/mL standard and the spectral peak identification of data from an open-source database, the identification results show that the algorithm has a better peak detection performance. The accuracy of the quantitative analysis was verified using the LC-HTQ-2020 triple quadrupole mass spectrometer produced by our group for the application of steroid detection in human serum. The results show that the algorithm proposed in this paper can accurately identify the peak information of LC-MS/MS chromatographic peaks, which can effectively improve the accuracy and reproducibility of steroid detection results and meet the requirements of clinical testing applications such as human steroid hormone detection.