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Retention time prediction for 653 pesticides on a biphenyl liquid chromatography stationary phase using machine learning

Postery | 2019 | ShimadzuInstrumentace
LC/MS, LC/MS/MS, LC/QQQ
Zaměření
Životní prostředí, Potraviny a zemědělství
Výrobce
Shimadzu
Klíčová slova
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Agilent ASMS 2020 Posters Book
Agilent ASMS 2020 Posters Book
2020|Agilent Technologies|Postery
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ION MOBILITY-ENABLED LC-HRMS FOR THE ANALYSIS OF POLLUTANTS IN INDOOR DUST: IDENTIFICATION AND PREDICTIVE CAPABILITIES
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GCMS
ICPMS
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LabRulez s.r.o. Všechna práva vyhrazena. Obsah dostupný pod licencí CC BY-SA 4.0 Uveďte původ-Zachovejte licenci.