Automatic Tuning Method for Quadrupole Mass Spectrometer Based on Improved Differential Evolution Algorithm.

Yuanqing Zhang, Baolin Xiong, Le Feng, Liang Li, Wenbo Cheng, Yuguo Tang

Abstract read

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Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Yuanqing ZhangSchool of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China.ORCID 0000-0002-6974-8047

Baolin XiongSchool of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China.ORCID 0009-0001-8907-9610

Le FengTianjin Key Laboratory of Medical Mass Spectrometry for Accurate Diagnosis, Tianjin 300399, China.

Liang LiTianjin Key Laboratory of Medical Mass Spectrometry for Accurate Diagnosis, Tianjin 300399, China.

Wenbo ChengSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.

Yuguo TangSchool of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China.

Funding

National Key R&D Program of China 2024YFF0725800Strategic Priority Research Program of the Chinese Academy of Sciences XDC0250200Tianjin Science and Technology Program Project 23YFYSHZ00020Tianjin Science and Technology Program Project 24ZXYXSY00070

6 · The paper itself

Abstract

Quadrupole mass spectrometers are highly sensitive and specific analytical instruments, widely used in pharmaceuticals, clinical diagnostics, and other fields. Their performance depends on a tuning process to optimize key parameters, which has traditionally relied on engineers' expertise or simple univariate search methods. This paper proposes an automatic tuning method using an improved differential evolution algorithm. This algorithm introduces a ranking and subpopulation classification for individuals, enabling distinct mutation strategies. Validation on the CEC-2017 benchmark functions confirms the superiority of the improved algorithm. In automatic tuning experiments, it achieved a 25.3% performance gain over the univariate search method and also surpassed both the classical differential evolution algorithm and standard particle swarm optimization algorithm. This method proves to be an effective approach for enhancing the performance of quadrupole mass spectrometers.

Indexed as

automatic tuning methodimproved differential evolution algorithmmutation strategyquadrupole mass spectrometersubpopulation

Identifiers

PMID41301110

DOI10.3390/bioengineering12111154

PMCPMC12649317

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.