摘要：Differential mobility spectrometry (DMS) is a promising measurement technique. It is used in the detection of chemical warfare agents, explosives, drugs, and volatile organic compounds. The measurement principle is based on separation of gas-phase ions according to their differential mobility in alternating low and high electric fields. The DMS measurement result is a two dimensional spectrum of ion current displayed as a function of separation voltage and compensation voltage. The DMS spectral peaks, in terms of their height, location and width, are affected by gas sample composition, separation field and the gas flow rate. In this work, there is presented the calibration procedure which utilises the univariate and multivariate approach to differential ion mobility spectrum. We demonstrated the possibility of a successful retrieval of quantitative information using partial least squares regression as well as univariate linear regression. However, the multivariate approach outperformed the univariate one in terms of the quality of the model and the concentration prediction accuracy.
其他摘要：Differential mobility spectrometry (DMS) is a promising measurement technique. It is used in the detection of chemical warfare agents, explosives, drugs, and volatile organic compounds. The measurement principle is based on separation of gas-phase ions according to their differential mobility in alternating low and high electric fields. The DMS measurement result is a two dimensional spectrum of ion current displayed as a function of separation voltage and compensation voltage. The DMS spectral peaks, in terms of their height, location and width, are affected by gas sample composition, separation field and the gas flow rate. In this work, there is presented the calibration procedure which utilises the univariate and multivariate approach to differential ion mobility spectrum. We demonstrated the possibility of a successful retrieval of quantitative information using partial least squares regression as well as univariate linear regression. However, the multivariate approach outperformed the univariate one in terms of the quality of the model and the concentration prediction accuracy.
