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Comprehensive machine learning prediction of GC/MS pesticide recovery based on the molecular fingerprinting for food QA/QC

 

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Poster Reprint ASMS 2020 ThP 177 Optimum molecular descriptors based on 89 machine learning methods for predicting the recovery rate of pesticides in crops by GC-MS Takeshi Serino 1, 2, Sadao Nakamura 1, Yoshizumi Takigawa 1, Tarun Anumol 3, Md.…
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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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both, bothlearning, learningmachine, machinedescriptor, descriptorpesticides, pesticidesamenable, amenableclassification, classificationatoms, atomspesticide, pesticidetech, techensemble, ensembleexecution, executionmethyl, methyllist, listqspr
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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Molecular Spectroscopy Compendium ensure food quality, production, and safety TABLE OF CONTENTS In this compendium, you’ll find current and emerging applications that will help you identify both target and non-target molecules by applying the very latest techniques for spectral data…
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ftir, ftiratr, atrfood, foodagilent, agilentnest, nestbird, birdflour, flourportable, portableacrylamide, acrylamidespectroscopy, spectroscopyinfrared, infraredmicrolab, microlabmilk, milkpotato, potatospectral
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