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On-line pre-treatment, separation, and nanoelectrospray mass spectrometric determinations for pesticide metabolites and peptides based on a modular microfluidic platform
In order to address time-consuming sample pre-treatment and separation prior to mass spectrometry (MS) identifications, highly integrated chips were developed, but damage to any functional unit in these chips would result in complete replacement. Herein, we propose a modular microfluidic platform co...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9091297/ https://www.ncbi.nlm.nih.gov/pubmed/35558234 http://dx.doi.org/10.1039/c8ra08276f |
Sumario: | In order to address time-consuming sample pre-treatment and separation prior to mass spectrometry (MS) identifications, highly integrated chips were developed, but damage to any functional unit in these chips would result in complete replacement. Herein, we propose a modular microfluidic platform comprising pre-treatment, liquid chromatography (LC) separation and nanoelectrospray ionization (nESI) chips for on-line enrichment, separation and nESI MS detection of pesticide metabolites and peptides. The pre-treatment chip is applicable in enriching pyridalyl and its metabolites, and it achieves optimal desalination efficiency, 98.5%, for polymerase chain reaction products. Additionally, the LC separation chip was fully characterised, and it demonstrated satisfactory separation efficiency, quantification ability and pressure durability. Finally, the modular microfluidic platform was used to identify the peptides in trypsin-digested casein. Four additional peptides were identified, indicating an improvement in detection ability compared with using off-line zip tips coupled with MS investigations. Because the proposed modular platform can significantly reduce manual work, it would be a potential tool to achieve high throughput and automatic MS identifications with low sample consumptions. |
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