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Total Ion Chromatogram and Total Ion Mass Spectrum as Alternative Tools for Detection and Discrimination (A Review)

期刊

CHEMOSENSORS
卷 10, 期 11, 页码 -

出版社

MDPI
DOI: 10.3390/chemosensors10110465

关键词

gas chromatography; mass spectrometry; total ion chromatogram; total ion mass spectrum; sensors; agri-food; environment; forensic; medical

资金

  1. University of Cadiz [UCA/R155REC/2021]
  2. Catedra Fundacion Cepsa [FPI UCA/TDI-4-19]
  3. Ministry of Universities of the Government of Spain through the European Recovery Instrument Next Gen-eration EU, of the European Union [UNI/551/2021]
  4. Portuguese Foundation for Science and Technology (FCT) [PTDC/ASP-SIL/30619/2017, UIDB/05183/2020, CEECIND/01014/2018]

向作者/读者索取更多资源

Gas chromatography and mass spectrometry are widely used techniques in the analysis of complex mixtures due to their advantages in selectivity, reproducibility, precision, and sensitivity. However, the complex and time-consuming data processing, requiring expertise, can be a drawback in certain fields. Therefore, alternative data processing approaches have been proposed to achieve faster, simpler, and almost automated analysis outcomes.
Gas chromatography (GC) and mass spectrometry (MS) are widely used techniques in the analysis of complex mixtures due to their various advantages, such as high selectivity, reproducibility, precision, and sensitivity. However, the data processing is often complex and time-consuming and requires a great deal of experience, which might be a serious drawback in certain areas, such as quality control, or regarding research in the field of medicine or forensic sciences, where time plays a crucial role. For these reasons, some authors have proposed the use of alternative data processing approaches, such as the total ion chromatogram or total mass spectrum, allowing these techniques to be treated as sensors where each retention time or ratio m/z acts as a sensor collecting total intensities. In this way, the main advantages associated with both techniques are maintained, but the outcomes from the analysis can be reached in a faster, simpler, and an almost automated way. In this review, the main features of the GC- and MS-based analysis methodologies and the ways in which to apply them are highlighted. Moreover, their implementation in different fields, such as agri-food, forensics, environmental sciences, or medicine is discussed, highlighting important advantages as well as limitations.

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