A structural similarity networking assisted collision cross-section prediction interval filtering strategy for multi-compound identification of complex matrix by ion-mobility mass spectrometry.

Wen, Jia-Hui; Guo, An-Qi; Li, Meng-Ning; Yang, Hua

Wen, Jia-Hui; Guo, An-Qi; Li, Meng-Ning; Yang, Hua.

Afiliação

Wen JH; State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Xiang, Nanjing, 210009, China.
Guo AQ; State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Xiang, Nanjing, 210009, China.
Li MN; State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Xiang, Nanjing, 210009, China. Electronic address: lemon19920712@126.com.
Yang H; State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Xiang, Nanjing, 210009, China. Electronic address: yanghuacpu@126.com.

Anal Chim Acta ; 1278: 341720, 2023 Oct 16.

Article em En | MEDLINE | ID: mdl-37709461

Palavras-chave

Collision cross-section; Ginkgo biloba; Ion-mobility mass spectrometry; Molecular fingerprints; Structural similarity networking

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Anal Chim Acta Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

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