Mass spectrometry in the U.S. space program: past, present, and future.

Recent years have witnessed significant progress on the miniaturization of mass spectrometers for a variety of field applications. This article describes the development and application of mass spectrometry (MS) instrumentation to support of goals of the U.S. space program. Its main focus is on the two most common space-related applications of MS: studying the composition of planetary atmospheres and monitoring air quality on manned space missions. Both sets of applications present special requirements in terms of analytical performance (sensitivity, selectivity, speed, etc.), logistical considerations (space, weight, and power requirements), and deployment in perhaps the harshest of all possible environments (space). The MS instruments deployed on the Pioneer Venus and Mars Viking Lander missions are reviewed for the purposes of illustrating the unique features of the sample introduction systems, mass analyzers, and vacuum systems, and for presenting their specifications which are impressive even by today's standards. The various approaches for monitoring volatile organic compounds (VOCs) in cabin atmospheres are also reviewed. In the past, ground-based GC/MS instruments have been used to identify and quantify VOCs in archival samples collected during the Mercury, Apollo, Skylab, Space Shuttle, and Mir missions. Some of the data from the more recent missions are provided to illustrate the composition data obtained and to underscore the need for instrumentation to perform such monitoring in situ. Lastly, the development of two emerging technologies, Direct Sampling Ion Trap Mass Spectrometry (DSITMS) and GC/Ion Mobility Spectrometry (GC/IMS), will be discussed to illustrate their potential utility for future missions.

Generation of substructure identification rules using feature-combinations from tandem mass spectra.

Software to interpret tandem mass spectra, entitled Method for Analyzing Patterns in Spectra (MAPS), has been developed to provide substructure information for an automated compound identification system, This software consists of several program modules which manipulate databases of tandem mass spectra and substructure information, generate substructure identification rules, and apply these rules to the tandem mass spectra of unknown compounds to identify components of their structure. The MAPS rule generation program has been modified to generate rules based on specific combinations of spectral features that occur concertedly. False positives are drastically reduced by searching for "feature-combinations" that have 100% uniqueness with respect to a reference database of compounds. Recall is increased by the determination of multiple feature-combinations indicative of the presence of a given substructure. Strategies were developed in the algorithm for the discovery of feature-combinations that avoid the computation "explosion" that occurs when working with a large number of spectral features. The rules developed have the form: "IF feature-eombination a (FC a) or FC b,..., or FC x, THEN substructure SSn is present."

Optimization of automatically generated rules for predicting the presence and absence of substructures from MS and MS/MS data.

A pattern-recognition/artificial-intelligence program, referred to as MAPS (Method for Analyzing Patterns in Spectra), was recently developed to identify the relationships that exist between substructures and the characteristic features they produce in the spectra from mass spectrometry (MS) and successive mass spectrometry (MS/MS). MAPS has been extended to utilize these relationships to formulate exclusion rules as well as inclusion rules, so that the absence of recognized substructures can be predicted as well as their presence. The potential usefulness of each MS and MS/MS spectral feature in such rule formulation is characterized by correlation and uniqueness factors. The correlation factor expresses the degree of correlation between a feature and a specific substructure; the uniqueness factor expresses the uniqueness of a feature with respect to that substructure. Features with high correlation factors are most use for predicting the absence of substructures, whereas features with high uniqueness factors are most useful for predicting their presence. Feature intensity-data have been found to improve the inclusion-rule performance and degrade the exclusion-rule performance. Criteria for optimizing the predictive abilities of both rule types are discussed.