The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
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 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]
Citations
Cites
Identification of mass spectra by computer-searching a file of known spectra
Hertz, H. S., Hites, R. A., & BIEMANN, K. (1971) · Analytical Chemistry · doi:10.1021/ac60301a009 · Cited by 305
Estimating probabilities of correct identification from results of mass spectral library searches
Stein, S. E. (1994) · Journal of the American Society for Mass Spectrometry · doi:10.1016/1044-0305(94)85022-4 · Cited by 231
Self-citationProbability based matching of mass spectra. Rapid identification of specific compounds in mixtures
McLafferty, F. W., Hertel, R. H., & Villwock, R. D. (1974) · Organic Mass Spectrometry · doi:10.1002/oms.1210090710 · Cited by 177
Computer methods in analytical mass spectrometry. Identification of an unknown compound in a catalog
Crawford, L., & Morrison, J. D. (1968) · Analytical Chemistry · doi:10.1021/ac60266a027 · Cited by 119
Computer-Enhanced Analytical Spectroscopy
(1990) · Springer eBooks · doi:10.1007/978-1-4684-1312-0 · Cited by 106
Atwater, B. L., Stauffer, D. B., McLafferty, F. W., & Peterson, D. W. (1985) · Analytical Chemistry · doi:10.1021/ac00281a028 · Cited by 71
The Evaluation of Mass Spectral Search Algorithms
Rasmussen, G. T., & ISENHOUR, T. L. (1979) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci60019a014 · Cited by 69
Retrieval and interpretative computer programs for mass spectrometry
McLafferty, F. W., & Stauffer, D. B. (1985) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00047a021 · Cited by 67
Performance prediction and evaluation of systems for computer identification of spectra
McLafferty, F. W. (1977) · Analytical Chemistry · doi:10.1021/ac50017a036 · Cited by 40
Comparative Evaluations of Mass Spectral Data Bases
McLafferty, F. W., Stauffer, D. B., & Loh, S. Y. (1991) · Journal of the American Society for Mass Spectrometry · doi:10.1016/1044-0305(91)85011-t · Cited by 37
Research works printed but not linked: 3
- Sokolow S., 1978, The Finnigan Library Search Program
- Pellizarri E. D., 1985, Comparative Evaluation of GC/MS Data Analysis Processing
- Pesya G. M., 1975, Computerized Structure Retrieval and Interpretation of Mass Spectra: The Design and Evaluation of a Probability Based Matching System Using a Large Data Base
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Wang, M., Carver, J. J., Phelan, V. V., Sanchez, L. M., Garg, N., Peng, Y., Nguyen, D. D., Watrous, J., Kapono, C. A., Luzzatto-Knaan, T., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3597 · Cited by 4,629
MassBank: a public repository for sharing mass spectral data for life sciences
Horai, H., Arita, M., Kanaya, S., Nihei, Y., Ikeda, T., Suwa, K., Ojima, Y., Tanaka, K., Tanaka, S., Aoshima, K., et al. (2010) · Journal of Mass Spectrometry · doi:10.1002/jms.1777 · Cited by 2,529
Kind, T., Wohlgemuth, G., Lee, D. Y., Lü, Y., Palazoglu, M., Shahbaz, S., & Fiehn, O. (2009) · Analytical Chemistry · doi:10.1021/ac9019522 · Cited by 1,486
LipidBlast in silico tandem mass spectrometry database for lipid identification
Kind, T., Liu, K., Lee, D. Y., DeFelice, B. C., Meissen, J. K., & Fiehn, O. (2013) · Nature Methods · doi:10.1038/nmeth.2551 · Cited by 987
Mass spectral molecular networking of living microbial colonies
Watrous, J. D., Roach, P. J., Alexandrov, T., Heath, B. S., Yang, J. Y., Kersten, R. D., van der Voort, M., Pogliano, K., Gross, H., Raaijmakers, J. M., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1203689109 · Cited by 974
Metadata
- 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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