Advancements in hardware have enabled high-resolution mass spectrometric (MS) detection, increasing confidence in molecular identifications they provide, but also causing new challenges for efficient data management and processing. The current state of the art for two-dimensional gas chromatography (GCxGC) batch non-targeted analysis (NTA) data processing is a two-step process of data exploration followed by retention time and mass spectral refinement to find features of interest across a batch. Difficulty or failure occurs for ~40% of features during this process for two major reasons. First, the mass spectral complexity results in deconvolution errors for low-level features leading to missing peaks or time-consuming manual re-integration. Second, the samples are of sufficient chromatographic complexity that data exploration results in a significant rate of divergent identifications leaving data too complex to be reduced in a reasonable timeframe.<br/>Our team overcomes this complexity by combining iterative processing of high-resolution GCxGC-MS data with machine learning (ML) to allow detection of low-level compounds otherwise missed by traditional peak finding algorithms. We leverage the information emergent from the batch to overcome the challenge of relying on peak finding and deconvolution for complex, high-resolution MS data. Our novel methodology finds compounds with an increase in both sensitivity of detection and specificity of identification compared to state-of-the-art commercial software’s peak finding and deconvolution methods.<br/>We first apply ML to automatically rank spectral signals into a quality hierarchy from the list of signals identified, by first-pass peak finding. Next, a representative signal is selected according to its quality at each chromatographic retention time in each sample. The high-resolution data is then exploited by identifying the mass spectral fingerprint of each high-quality molecular feature. This mass spectral fingerprint is leveraged in a second iteration of processing to extract quantitative information across the batch of samples by searching for specific ion signatures.<br/>Our implemented method enables high-throughput, comprehensive NTA, and keeping up with hardware advances in high resolution GCxGC-MS.

Automated low level signal capture in high resolution GCxGC-MS

ACS Spring 2022

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