Utilizing Ion-Mobility Data To Estimate Molecular Masses
- Created: Tuesday, 01 January 2008
Potential applications include detecting biochemicals in pharmaceutical settings.
A method is being developed for utilizing readings of an ion-mobility spectrometer (IMS) to estimate molecular masses of ions that have passed through the spectrometer. The method involves the use of (1) some feature-based descriptors of structures of molecules of interest and (2) reduced ion mobilities calculated from IMS readings as inputs to (3) a neural network. This development is part of a larger effort to enable the use of IMSs as relatively inexpensive, robust, lightweight instruments to identify, via molecular masses, individual compounds or groups of compounds (especially organic compounds) that may be present in specific environments or samples. Potential applications include detection of organic molecules as signs of life on remote planets, modeling and detection of biochemicals of interest in the pharmaceutical and agricultural industries, and detection of chemical and biological hazards in industrial, homeland security, and industrial settings.
The following background information is prerequisite to a meaningful summary of the present method.
- An IMS includes a drift tube that has length L and is filled with a drift gas (e.g., N2 or CO2) at a pressure, P, which could be atmospheric or any other suitable pressure. Mixed into the drift gas is a trace amount of ionized molecules from a sample or environment of interest. An electric potential (V) is applied between the ends of the drift tube.
- The mobility (K) of the ions is given by K ≡ L2/Vt, where t is the amount of time taken by the ions to drift along the tube from the inlet to a detector at the outlet.
- The correlation among the mobility, the mass (m) of an ion, the mass (M) of a drift-gas molecule, and the cross section (Ω) for collisions between an ion and a drift-gas molecule is given by
where q is the fundamental unit of electric charge, N is the density of the drift-gas molecules, and k is Boltzmann’s constant.
- The reduced mobility (K0) is given by K0 ≡ KTSP/TPS, where TS denotes standard temperature (≈273 K) and PS is standard atmospheric pressure (represented by a mercury-barometer column height of 760 mm under normal Earth gravitation).
- In a previous study, it was found that there are some correlations between the molecular structure of each compound and the K0 value of ions of that compound in a given drift gas.
This concludes the background information.
0(N2) and K0(N2 )] into an estimate of molecular mass."/>The theoretical basis of the present developmental method can be summarized as the hypotheses that there could be a correlation among molecular structure, collision cross section, and molecular mass, such that it should be possible to estimate the mass of an ion by m = Φ(K0), where Φ is a nonlinear function to be determined, Ω^ is an estimated collision cross section that one strives to make as nearly equal as possible to the observed collision cross section. The estimated collision cross section is expressed as Ω^ = g(W,S), g is another nonlinear function to be determined, W is a vector of weights in a parameter space (e.g., a vector of neural-network weights), and S ≡ (d1,d2,d3,...) is a vector of feature-based numerical descriptors of the molecular structure. In this method, the applicable equations are not solved explicitly; rather, they are solved implicitly by means of a neural network (see figure). For each compound of interest, the inputs to the neural network are (1) a set of six feature-based descriptors extracted from a much larger set of molecular-structure descriptors by means of principal- component analysis and (2) K0 values for that compound in two different drift gases.
In a numerical-simulation test of the method, the neural network was trained by use of descriptors, K0 values, and molecular masses pertaining to 65 organic compounds, then interrogated by use of descriptors and K0 values pertaining to 10 other organic compounds. The molecular masses generated by the neural network were found to differ from the correct values by root-mean-square errors of no more than a few percent.
This work was done by Tuan Duong and Isik Kanik of Caltech for NASA’s Jet Propulsion Laboratory.
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