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

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…

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…

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)…

Retention time prediction for 653 pesticides on a biphenyl liquid chromatography stationary phase using machine learning Anthony Sullivan1; Leon P Barron2; Alan Barnes3; Neil Loftus3 UK Limited, Milton Keynes, UK; 2Department of Analytical, Environmental & Forensic Sciences, King’s College London,…