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Optimum molecular descriptors based on 89 machine learning methods for predicting the recovery rate of pesticides in crops by GC-MS

 

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Poster Reprint ASMS 2019 TP298 Comprehensive machine learning prediction of GC/MS pesticide recovery based on the molecular fingerprinting for food QA/QC Takeshi Serino* 1,2; Sadao Nakamura1; Yoshizumi Takigawa1; Norton Kitagawa3; Shigehiko Kanaya 2 1 Agilent Technologies, Hachioji City, Japan 2…
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Poster Reprint ASMS 2020 WP 165 Classifying the pesticides in foods between GC-amenable and LC-amenable using the prediction model with molecular descriptors Sadao Nakamura 1, Takeshi Serino 1, 2, Takeshi Otsuka 1, Yoshizumi Takigawa 1, Tarun Anumol 3, Shigehiko Kanaya…
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Agilent ASMS 2020 Posters Book
2020|Agilent Technologies|Postery
Poster Reprint ASMS 2020 MP 176 Using ICP-MS/MS with M-Lens for the analysis of high silicon matrix samples Yu Ying1; Xiangcheng Zeng1 1Agilent China Technologies, China, Shanghai, Introduction The expansion of the connected devices and the Internet of Things (IoT)…
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MestReNova Manual
2024|Mestrelab Research|Manuály
MestReNova Manual © 2023 M ESTRELAB RESEARCH Last Revision: 21st Feb 2024 MestReNova 15.0.1 by MESTRELAB RESEARCH This is the manual of MestReNova 15.0.01 MestReNova © 2024 MESTRELAB RESEARCH All rights reserved. No parts of this work may be reproduced…
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mestrenova, mestrenovamnova, mnovayou, youclicking, clickingnmr, nmrmschrom, mschromspectrum, spectrummultiplet, multipletmenu, menuprocessing, processingmultiplets, multipletscan, canspectra, spectraprediction, predictionstacked
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