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# ACMD Seminar: Probability Distributions in Peak Matching for DART-MS

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Matthew Roberts*Applied and Computational Mathematics Division, NIST*

Tuesday, March 8, 2022, 3:00 PM EST (1:00 PM MST)

**A video of this talk is available to NIST staff in the Math channel on NISTube, which is accessible from the NIST internal home page.**

**Abstract:** Direct Analysis in Real Time Mass Spectrometry (DART-MS) is a new technology that is a candidate for chemical identification in many applications including forensic science. DART-MS rapidly produces multiple spectral measurements of a compound or analyte at various fragmentation levels. We propose a method of compound identification using DART-MS that uses probability distributions that takes advantage of replicate measurements, one of the desirable characteristics of DART-MS, by identifying peaks with probability distributions. We present results of discriminating structurally similar compounds using our technique and compare it to currently employed methods.

**Bio:** Matthew `Jake' Roberts hails from Michigan where he earned a BA in mathematics from Western Michigan University in 2010 and a PhD in applied mathematics from Michigan Technical University in 2019. His thesis work conducted under the advisement of Mark Gockenbach involved approximations to solutions of linear inverse problems. In 2020 he began his National Research Council fellowship at NIST working with Tony Kearsley.

**Note:**This talk will be recorded to provide access to NIST staff and associates who could not be present to the time of the seminar. The recording will be made available in the Math channel on NISTube, which is accessible only on the NIST internal network. This recording could be released to the public through a Freedom of Information Act (FOIA) request. Do not discuss or visually present any sensitive (CUI/PII/BII) material. Ensure that no inappropriate material or any minors are contained within the background of any recording. (To facilitate this, we request that cameras of attendees are muted except when asking questions.)

Mathematics and statistics

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                -  Stephen Langer  stephen.langer@nist.gov      (301) 975-5423   
                -  Lochi Orr  lochi.orr@nist.gov      (410) 598-6986   

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