Home Environmental Modeling & Computational Mass Spectrometry (EMCMS) This is the official website of the EMCMS group led by Dr. Saer Samanipour


We are a group of Analytical Chemists who are passionate about application of Data Science and High Resolution Mass Spectrometry (HRMS) for unravelling the human and environmental exposome.

We build digital Machine Learning (ML) based tools to process LC/GC-HRMS data generated from complex samples from environmental to biological. These tools are to automize the data processing from the data import to structural elucidation and the assessment of quality of the identification. Additionally, we build models to maximize the level of information extracted from the spectra of structurally unknown chemicals. Our models take advantage the structural information implicitly present in the fragmentation pattern of an organic chemical. These complex models use a combination of chromatographic and mass spectrometric behavior of organic chemicals to infer about their environmental fate and toxicity.

Open Science

All our tools are built following the FAIR Principals and made publicly available through GitHub and Bitbucket with MIT license.


Relevant Publications

Combining a Deconvolution and a Universal Library Search Algorithm for the Nontarget Analysis of Data-Independent Acquisition Mode Liquid Chromatography−High-Resolution Mass Spectrometry Results, Env Sci Technol, 2018.

Naive Bayes classification model for isotopologue detection in LC-HRMS data, Chemometric Intll Lab Sys, 2022.

Self Adjusting Algorithm for the Nontargeted Feature Detection of High Resolution Mass Spectrometry Coupled with Liquid Chromatography Profile Data, Anal Chem, 2019.

Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data, ChemRxiv, 2022.

From descriptors to intrinsic fish toxicity of chemicals: an alternative approach to chemical prioritization, Env Sci Technol, 2022.

InSpectra – A Platform for Identifying Emerging Chemical Threats, ResearchSquare, 2022.