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Details

Autor(en) / Beteiligte
Titel
Direct wine profiling by mass spectrometry (MS): A comparison of different ambient MS approaches
Ist Teil von
  • Microchemical journal, 2022-08, Vol.179, p.107479, Article 107479
Ort / Verlag
Elsevier B.V
Erscheinungsjahr
2022
Link zum Volltext
Quelle
Access via ScienceDirect (Elsevier)
Beschreibungen/Notizen
  • [Display omitted] •Assessment of different ambient ionization sources to obtain fast wine profiling.•Paperspray, low-temperature plasma and dielectric barrier discharge methods tested.•A wide range of organic acids and flavonoids were annotated using the 3 methods.•Paper spray provided better ionization coverage than plasma-based methods tested. Ambient mass spectrometry provides a valuable alternative to classical mass spectrometry, enabling fast, in-situ and high-throughput analyses with no or minimal sample pretreatment. In this work, we have explored the capability of different ambient ionization sources (Paper spray (PS), Dielectric Barrier Discharge Ionization (DBDI) and Low-temperature plasma (LTP)) to allow streamlined profiling methods of untreated raw wine for quality control purposes. The resulting MS profiles -with characteristic ions mainly in the range from m/z 100 to 350- were also compared to those obtained by the standard electrospray MS method. The MS spectra coming from the three different ambient ionization approaches highlighted the capability of the investigated sources for analyzing wine without any treatment, enabling a streamlined method to generate mass spectra to allow classification and differentiation of sets of meaningful samples, based not only on simple organic acids, phenolic acids, and their derivatives but also on low-molecular-mass flavonoids. The main advantage of the two plasma-based methods is the ability to allow solventless analysis and the simplicity of the spectra obtained. PS-MS approach allowed obtaining a richer MS profile showing more compounds with higher signal intensity. The higher complexity of the spectra acquired can be subjected to data analysis to extract meaningful differences between samples of interest.
Sprache
Englisch
Identifikatoren
ISSN: 0026-265X
eISSN: 1095-9149
DOI: 10.1016/j.microc.2022.107479
Titel-ID: cdi_crossref_primary_10_1016_j_microc_2022_107479

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