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Automation of fizzy extraction enabled by inexpensive open-source modules

The implementation of most instrumental analysis methods requires a considerable amount of human effort at every step, including sample preparation, detection, and data processing. Automated analytical workflows decrease the amount of required work. However, commercial automated platforms are mainly...

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Detalles Bibliográficos
Autores principales: Yang, Hao-Chun, Chang, Chun-Ming, Urban, Pawel L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6522666/
https://www.ncbi.nlm.nih.gov/pubmed/31193233
http://dx.doi.org/10.1016/j.heliyon.2019.e01639
Descripción
Sumario:The implementation of most instrumental analysis methods requires a considerable amount of human effort at every step, including sample preparation, detection, and data processing. Automated analytical workflows decrease the amount of required work. However, commercial automated platforms are mainly available for well-established sample processing methods. In contrast, newly developed prototypes of analytical instruments are often operated manually, what limits their performance and decreases the chance of their adoption by the broader community. Open-source electronic modules facilitate the prototyping of complex analytical instruments and enable the incorporation of automated functions at the early stage of technique development. Here, we exemplify this advantage of open-source electronics while prototyping an automated analytical device. Fizzy extraction takes advantage of the effervescence phenomenon to extract semi-volatile solutes from the liquid to the gas phase. The entire fizzy extraction process has been automated by using three Arduino-related microcontrollers. The functions of the developed autonomous fizzy extraction device include triggering the analysis by a smartphone app, control of carrier gas pressure in the headspace of the sample chamber, displaying experimental conditions on an LCD screen, acquiring mass spectrometry data in real time, filtering electronic noise, integrating peaks, calculating the analyte concentration in the extracted sample, printing the analysis report, storing the acquired data in non-volatile memory, monitoring the condition of the motor by counting the number of extraction cycles, and cleaning the elements exposed to the sample (to minimize carryover). The performance of this automated system has been evaluated using standards and real samples.