Latest Updates

Here you can read about our latest publications and projects.


Our own Denice has published:

Naive Bayes classification model for isotopologue detection in LC-HRMS data

April 15, 2022

Isotopologue identification or removal is a necessary step to reduce the number of features that need to be identified in samples analyzed with non-targeted analysis. Currently available approaches rely on either predicted isotopic patterns or an arbitrary mass tolerance, requiring information on the molecular formula or instrumental error, respectively. Therefore, a Naive Bayes isotopologue classification model was developed that does not depend on any thresholds or molecular formula information. This classification model uses the elemental mass defects of six elemental ratios and successfully identified isotopologues for both theoretical isotopic patterns and wastewater influent samples, outperforming one of the most commonly used approaches (i.e., 1.0033 ​Da mass difference method - CAMERA). For the theoretical isotopologues, the classification model outperformed an “in-house” mass difference method with a true positive rate (TPr) of 99.0% and false positive rate (FPr) of 1.8% compared to a TPr of 16.2% and an FPr of 0.02%, assuming no error. As for the wastewater influent samples, the classification model, with a TPr of 99.8% and false detection rate (FDr) of 0.5%, again performed better than the mass difference method, with a TPr of 96.3% and FDr of 4.8%. Therefore, it can be concluded that the classification model can be used for isotopologue identification, requiring no thresholds or information on the molecular formula.



Utilization of Machine Learning for the Differentiation of Positional NPS Isomers with Direct Analysis in Real Time Mass Spectrometry

March 17 2022

The differentiation of positional isomers is a well established analytical challenge for forensic laboratories. As more novel psychoactive substances (NPSs) are introduced to the illicit drug market, robust yet efficient methods of isomer identification are needed. Although current literature suggests that Direct Analysis in Real Time–Time-of-Flight mass spectrometry (DART-ToF) with in-source collision induced dissociation (is-CID) can be used to differentiate positional isomers, it is currently unclear whether this capability extends to positional isomers whose only structural difference is the precise location of a single substitution on an aromatic ring. The aim of this work was to determine whether chemometric analysis of DART-ToF data could offer forensic laboratories an alternative rapid and robust method of differentiating NPS positional ring isomers. To test the feasibility of this technique, three positional isomer sets (fluoroamphetamine, fluoromethamphetamine, and methylmethcathinone) were analyzed. Using a linear rail for consistent sample introduction, the three isomers of each type were analyzed 96 times over an eight-week timespan. The classification methods investigated included a univariate approach, the Welch t test at each included ion; a multivariate approach, linear discriminant analysis; and a machine learning approach, the Random Forest classifier. For each method, multiple validation techniques were used including restricting the classifier to data that was only generated on one day. Of these classification methods, the Random Forest algorithm was ultimately the most accurate and robust, consistently achieving out-of-bag error rates below 5%. At an inconclusive rate of approximately 5%, a success rate of 100% was obtained for isomer identification when applied to a randomly selected test set. The model was further tested with data acquired as a part of a different batch. The highest classification success rate was 93.9%, and error rates under 5% were consistently achieved.



Examining the Relevance of the Microplastic-Associated Additive Fraction in Environmental Compartments

February 2nd 2022

Plastic contamination is ubiquitous in the environment and has been related to increasing global plastic usage since the 1950s. Considering the omnipresence of additives in plastics, the risk posed by this contamination is related not only to the physical effects of plastic particles but also to their additive content. Until now, most routine environmental monitoring programs involving additives have not considered the presence of these additives still associated with the plastic they were added to during their production. Understanding environmental additive speciation is essential to address the risk they pose through their bioavailability and plastic-associated transport. Here, we present and apply a theoretical framework for sampling and analytical procedures to characterize the speciation of hydrophobic nonionized additives in environmental compartments. We show that this simple framework can help develop sampling and sample treatment procedures to quantify plastic-associated additives and understand additive distribution between plastics and organic matter. When applied to concrete cases, internal consistency checks with the model allowed for identifying plastic-associated additives in a sample. In other cases, the plastic-organic carbon ratio and additive concentration in the matrix are key factors affecting the ability to identify plastic-associated additives. The effect of additive dissipation through diffusion out of plastic particles is also considered.


Students Writing on Board

Job Alert:
We have an open position for a PhD project on anomaly detection in HRMS data.

December 1 2021

Are you interested in developing new algorithms? The Analytical Chemistry Group of the Van ‘t Hoff Institute for Molecular Sciences is looking for a PhD in High Resolution Mass Spectrometry Anomaly Detection and Process Monitoring.

For more information on the position either go to here or contact Dr. Samanipour (


Publication Alert:
From Centroided to Profile Mode: Machine Learning for Prediction of Peak Width in HRMS Data

November 30 2021

Centroiding is one of the major approaches used for size reduction of the data generated by high-resolution mass spectrometry. During centroiding, performed either during acquisition or as a pre-processing step, the mass profiles are represented by a single value (i.e., the centroid). While being effective in reducing the data size, centroiding also reduces the level of information density present in the mass peak profile. Moreover, each step of the centroiding process and their consequences on the final results may not be completely clear. Here, we present Cent2Prof, a package containing two algorithms that enables the conversion of the centroided data to mass peak profile data and vice versa. The centroiding algorithm uses the resolution-based mass peak width parameter as the first guess and self-adjusts to fit the data. In addition to the m/z values, the centroiding algorithm also generates the measured mass peak widths at half-height, which can be used during the feature detection and identification. The mass peak profile prediction algorithm employs a random-forest model for the prediction of mass peak widths, which is consequently used for mass profile reconstruction. The centroiding results were compared to the outputs of the MZmine-implemented centroiding algorithm. Our algorithm resulted in rates of false detection ≤5% while the MZmine algorithm resulted in 30% rate of false positive and 3% rate of false negative. The error in profile prediction was ≤56% independent of the mass, ionization mode, and intensity, which was 6 times more accurate than the resolution-based estimated values.


Screenshot 2021-09-30 at 09.13.49.png

Denice and Saer are participating in ICNTS 21 in Erding Germany.

September 30 2021

In the recent years, there were several very successful
workshops for Non-Target Screening (NTS), which
started in 2016 at the Non-Target meeting in Ascona,
continuing with ‘SWEMSA 2016’ in Garching, many
SETAC meetings, ACS, RCS and GDCh events, SWEMSA
2019 in Erding and lastly digital 2020 at the ‘Analytica’
as well as at the ‘GC meets NTS’ in early 2021.
Now, Non-Target Screening and its applications are in
This conference will bring together leading international
scientists from various consortia and disciplines. It is the
ideal location for free lab, industrial and academic
researchers to exchange information with other
colleagues internationally, interdisciplinary and inter-
professionally. NTS users from all over the world,
vendors from the field of instrumental analysis and
software developments will present their latest results
and ideas in keynote lectures, lecture sessions and
poster sessions.
The ICNTS 21 is being held as a hybrid conference
(online and on-site in Erding, Germany) between
04. – 07. October 2021.



A novel method for the quantification of tire and polymer-modified bitumen particles in environmental samples by pyrolysis gas chromatography mass spectroscopy

September 3 2021

Tire and road wear particles may constitute the largest source of microplastic particles into the environment. Quantification of these particles are associated with large uncertainties which are in part due to inadequate analytical methods. New methodology is presented in this work to improve the analysis of tire and road wear particles using pyrolysis gas chromatography mass spectrometry. Pyrolysis gas chromatography mass spectrometry of styrene butadiene styrene, a component of polymer-modified bitumen used on road asphalt, produces pyrolysis products identical to those of styrene butadiene rubber and butadiene rubber, which are used in tires. The proposed method uses multiple marker compounds to measure the combined mass of these rubbers in samples and includes an improved step of calculating the amount of tire and road based on the measured rubber content and site-specific traffic data. The method provides good recoveries of 83–92% for a simple matrix (tire) and 88–104% for a complex matrix (road sediment). The validated method was applied to urban snow, road-side soil and gully-pot sediment samples. Concentrations of tire particles in these samples ranged from 0.1 to 17.7 mg/mL (snow) to 0.6–68.3 mg/g (soil/sediment). The concentration of polymer-modified bitumen ranged from 0.03 to 0.42 mg/mL (snow) to 1.3–18.1 mg/g (soil/sediment).

Doi: 10.1016/j.jhazmat.2021.127092


Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis

August 24 2021

Non-target analysis (NTA) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental samples. NTA typically results in large and information-rich datasets that require computer aided (ideally automated) strategies for their processing and interpretation. Such strategies do however raise the challenge of reproducibility between and within different processing workflows. An effective strategy to mitigate such problems is the implementation of inter-laboratory studies (ILS) with the aim to evaluate different workflows and agree on harmonized/standardized quality control procedures. Here we present the data generated during such an ILS. This study was organized through the Norman Network and included 21 participants from 11 countries. A set of samples based on the passive sampling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i.e. in-house) developed method. The data generated represents a valuable resource (i.e. benchmark) for future developments of algorithms and workflows for NTA experiments.

Doi: 10.1038/s41597-021-01002-w


Plastic in archived biosolids

June 16 2021

Plastics are ubiquitous contaminants that leak into the environment from multiple pathways including the use of treated sewage sludge (biosolids). Seven common plastics (polymers) were quantified in the solid fraction of archived biosolids samples from Australia and the United Kingdom from between 1950 and 2016. Six plastics were detected, with increasing concentrations observed over time for each plastic. Biosolids plastic concentrations correlated with plastic production estimates, implying a potential link between plastics production, consumption and leakage into the environment. Prior to the 1990s, the leakage of plastics into biosolids was limited except for polystyrene. Increased leakage was observed from the 1990s onwards; potentially driven by increased consumption of polyethylene, polyethylene terephthalate and polyvinyl chloride. We show that looking back in time along specific plastic pollution pathways may help unravel the potential sources of plastics leakage into the environment and provide quantitative evidence to support the development of source control interventions or regulations.



Plastic in rice

April 16 2021

This study investigated mass concentrations of selected plastics in store-bought rice, the staple of more than half the world’s population. Polyethylene, polyethylene terephthalate, poly-(methyl methacrylate), polypropylene, polystyrene and polyvinyl chloride were quantified using pressurized liquid extraction coupled to double-shot pyrolysis gas chromatography/mass spectrometry. Polyethylene, polypropylene and polyethylene terephthalate were quantifiable in the rice samples with polyethylene the most frequently detected (95%). There was no statistical difference between total plastic concentration in paper and plastic packaged rice. Shaking the rice in its packaging had no significant difference on the concentration of plastics. Washing the rice with water significantly reduced plastic contamination. Instant (pre-cooked) rice contained fourfold higher levels of plastics, suggesting that industrial processing potentially increases contamination. A preliminary estimate of the intake of plastic through rice consumption for Australians established 3.7 mg per serve (100 g) if not washed and 2.8 mg if washed.

Annual consumption was estimated around 1 g/person.



Out of sight but not out of mind

March 20 2021

Microplastics contamination has been widely reported in filter feeders yet the < 1 µm size fraction has been largely ignored. In attempt to characterize this sub 1 µm size fraction and better understand the size distribution of microplastics contamination in filter feeders, field deployed oysters were characterised using a combination of size fractionation combined with pyrolysis-gas chromatography-mass spectrometry (Py-GC/MS) as well as Fourier Transform-Infrared Spectroscopy (µFT-IR). Sequential filtration followed by Py-GC/MS identified the 1 to 22 µm fraction to contain the highest total plastic mass concentration (Ʃ31 mg/g), followed by the <1 µm fraction (Ʃ7.7 mg/g) and the >22 µm fraction (Ʃ0.1 mg/g). µFT-IR identified 0.2 particles/g tissue but was limited to particles >150 µm in size. Our results clearly show that an important size fraction of microplastics is being overlooked in almost all studies published to date that rely on FTIR for polymer identification.



Quality assessment of NTA workflows

October 30 2020

The application of non-target analysis (NTA), a comprehensive approach to characterize unknown chemicals, including chemicals of emerging concern has seen a steady increase recently. Given the relative novelty of this type of analysis, robust quality assurance and quality control (QA/QC) measures are imperative to ensure quality and consistency of results obtained using different workflows. Due to fundamental differences to established targeted workflows, new or expanded approaches are necessary; for example to minimize the risk of losing potential substances of interest (i.e. false negatives, Type II error). We present an overview of QA/QC techniques for NTA workflows published to date, specifically focusing

on the analysis of environmental samples using liquid chromatography coupled to HRMS.



Our paper made it to the cover of ES&T Letters 

April 12, 2020


Concentration of plastics in domestic laundry dryers

April 12, 2020

Concentration of plastics in domestic laundry dryers

In this study published in STOTEN the concentration of fibers generated from the domestic laundry dryers and their emission were assessed.



The Economist and HIMS coverage of our publication in Environ. Sci. Technol. Letters

April 12, 2020

Population Socioeconomics Predicted Using Wastewater

The Economist London  and HIMS covered our latest study published (open access) in Environ. Sci. Technol. Lett, where we successfully predicted several socioeconomical parameters across Australia using the mass loads of chemicals in the wastewater.