We are going to give a seminar titled “Rethink GC-MS and LC-MS: Analyze Trace Components and Discover Unknown in a Breeze” in NUS on this Friday.
Click here to register
VENUE INFORMATION
Date: Friday, 27 April, 2018
Time: 3:00 pm to 4:00 pm
Venue: EA #06-04
Faculty of Engineering,
National University of Singapore
ABSTRACT
GC-MS and LC-MS are current industry standards for chemical composition analysis of complex mixtures. Despite their extensive histories, the analysis of trace-level components remains a huge challenge due to interferences from coeluting substances and dynamic background noise. Conventional chromatography solutions are abundant but demand extensive experimental and instrumental modifications. Yet, they do not always guarantee good results.
Join us at this talk to learn how a novel chemometric tool is able to meet the complex analytical challenge of detecting and identifying trace-level components with accuracy and confidence. The ability to analyze GC-MS and LC-MS data of all complexity will definitely open up a world of new information and data for your research. In addition, we will also showcase innovative solutions to solve your data analysis, data management and big data analytics needs.
SPEAKER BIOGRAPHY
Chun Kiang Chua
PhD, Scientist
ChemoPower Technology, Singapore
Dr. Chua joined ChemoPower Technology as a Scientist following his PhD and postdoctoral fellowship in Chemistry at Nanyang Technological University. He has 10 years of extensive research experience in analytical chemistry, electrochemistry and materials science. His current research interest focuses on chemical profiling of traditional Chinese herbs with hyphenated techniques. He has co-authored 67 research articles in the various fields.
Mabel Lv Yunbo
PhD, R&D Manager
ChemoPower Technology, Singapore
Dr. Lv Mabel joined ChemoPower Technology as an R&D manager following her PhD in National University of Singapore. She has 8 years of multidisciplinary research experience in chemistry, physics and materials science. Her current research interest focuses on the exploration of in-source fragmentation of ESI-MS as well as the discovery of unknowns with kinetic entropy minimization algorithm with LC-MS.