Optimization and testing of mass spectral library search algorithms for compound identification
Abstract
Five algorithms proposed in the literature for library search identification of unknown compounds from their low resolution mass spectra were optimized and tested by matching test spectra against reference spectra in the NIST-EPA-NIH Mass Spectral Database. The algorithms were probability-based matching (PBM), dot-product, Hertz et al. similarity index, Euclidean distance, and absolute value distance. The test set consisted of 12,592 alternate spectra of about 8000 compounds represented in the database. Most algorithms were optimized by varying their mass weighting and intensity scaling factors. Rank in the list of candidatc compounds was used as the criterion for accuracy. The best performing algorithm (75% accuracy for rank 1) was the dot-product function that measures the cosine of the angle between spectra represented as vectors. Other methods in order of performance were the Euclidean distance (72%), absolute value distance (68%) PBM (65%), and Hertz et al. (64%). Intensity scaling and mass weighting were important in the optimized algorithms with the square root of the intensity scale nearly optimal and the square or cube the best mass weighting power. Several more complex schemes also were tested, but had little effect on the results. A modest improvement in the performance of the dot-product algorithm was made by adding a term that gave additional weight to relative peak intensities for spectra with many peaks in common.
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Matching measured MS/MS to libraries of reference MS/MS spectra with known structures is a common approach within the natural products field.
“Matching measured MS/MS to libraries160 of reference MS/MS spectra with known structures, organized into libraries, is a common approach within the natural products field.”
From Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products (2024) · cites this paper as [160]
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Self-citation
- Authors
- Stein, S. E., & Scott, D. R.
- Affiliations
- Environmental Protection Agency (US)
- National Institute of Standards and Technology (US)
- ORCID
- Stein, Stephen E.
- Venue
- Journal of the American Society for Mass Spectrometry
- Publisher
- American Chemical Society (ACS)
- Published
- 1994-09-01
- Volume
- 5
- Issue
- 9
- Pages
- 859-866
- ISSN
- 1044-0305 (ISSN-L)
- OpenAlex
- W2028421938
- Cited by
- 882
- References
- 15
- Citations by year
- 2026: 36 · 2025: 62 · 2024: 72 · 2023: 73 · 2022: 66 · 2021: 57 · 2020: 39 · 2019: 35 · 2018: 39 · 2017: 47 · 2016: 30 · 2015: 40 · 2014: 33 · 2013: 39 · 2012: 33
- FWCI
- 2.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Society for Mass Spectrometry (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Environmental Protection Agency
- National Institute of Standards and Technology
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Keywords
- mass spectral library search, compound identification, low resolution mass spectra, Euclidean distance, intensity scaling
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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