qPeaks: A Linear Regression-Based Asymmetric Peak Model for Parameter-Free Automatized Detection and Characterization of Chromatographic Peaks in Non-Target Screening Data.

We present qPeaks (quality peaks), a novel, user-parameter-free algorithm for peak detection and peak characterization applicable to chromatographic data. The algorithm is based on a linearizable regression model that analyzes asymmetric peaks and estimates the specific uncertainties associated with the peak regression parameters. The uncertainties of the parameters are used to derive a data quality score DQSpeak, rendering low reliability results more transparent during processing and allowing for the prioritization of generated features. High DQSpeak chromatographic peaks have a lower chance of being classified as false-positive and show higher repeatability over multiple measurements. The high efficiency of the algorithm makes it particularly useful for application within processing routines of nontarget screening through chromatography coupled with high-resolution mass spectrometry. qPeaks is integrated into the qAlgorithms nontarget screening processing toolbox and appends a parameter-free chromatographic peak detection and characterization step to it. With qAlgorithms, now high-resolution mass spectra are centroided using the qCentroids algorithms, centroids are clustered to form extracted ion chromatograms (EICs) with the qBinning algorithm, and chromatographic peaks are found on the generated EICs with qPeaks. However, all tools from qAlgorithms can also be used independently.

Non-target screening in water analysis: recent trends of data evaluation, quality assurance, and their future perspectives.

This trend article provides an overview of recent advancements in Non-Target Screening (NTS) for water quality assessment, focusing on new methods in data evaluation, qualification, quantification, and quality assurance (QA/QC). It highlights the evolution in NTS data processing, where open-source platforms address challenges in result comparability and data complexity. Advanced chemometrics and machine learning (ML) are pivotal for trend identification and correlation analysis, with a growing emphasis on automated workflows and robust classification models. The article also discusses the rigorous QA/QC measures essential in NTS, such as internal standards, batch effect monitoring, and matrix effect assessment. It examines the progress in quantitative NTS (qNTS), noting advancements in ionization efficiency-based quantification and predictive modeling despite challenges in sample variability and analytical standards. Selected studies illustrate NTS's role in water analysis, combining high-resolution mass spectrometry with chromatographic techniques for enhanced chemical exposure assessment. The article addresses chemical identification and prioritization challenges, highlighting the integration of database searches and computational tools for efficiency. Finally, the article outlines the future research needs in NTS, including establishing comprehensive guidelines, improving QA/QC measures, and reporting results. It underscores the potential to integrate multivariate chemometrics, AI/ML tools, and multi-way methods into NTS workflows and combine various data sources to understand ecosystem health and protection comprehensively.

qBinning: Data Quality-Based Algorithm for Automized Ion Chromatogram Extraction from High-Resolution Mass Spectrometry.

Due to the complexity and volume of data generated through non-target screening (NTS) using chromatographic couplings with high-resolution mass spectrometry, automized processing routines are necessary. The processing routines usually consist of many individual steps that are user-parameter-dependent and, thus, require labor-intensive optimization. Additionally, the effect of variations in raw data quality on the processing results is unclear and not fully understood. Within this work, we present qBinning, a novel algorithm for constructing extracted ion chromatograms (EICs) based on statistical principles and, thus, without the need to set user parameters. Furthermore, we give the user feedback on the specific qualities of the generated EICs using a scoring system (DQSbin). The DQSbin measures reliability as it correlates with the probability of correct classification of masses into EICs and the degree of overlap between different EIC construction algorithms. This work is a big step forward in understanding the behavior of NTS data and increasing the overall transparency in the results of NTS.

Critical review on data processing algorithms in non-target screening: challenges and opportunities to improve result comparability.

Non-target screening (NTS) is a powerful environmental and analytical chemistry approach for detecting and identifying unknown compounds in complex samples. High-resolution mass spectrometry has enhanced NTS capabilities but created challenges in data analysis, including data preprocessing, peak detection, and feature extraction. This review provides an in-depth understanding of NTS data processing methods, focusing on centroiding, extracted ion chromatogram (XIC) building, chromatographic peak characterization, alignment, componentization, and prioritization of features. We discuss the strengths and weaknesses of various algorithms, the influence of user input parameters on the results, and the need for automated parameter optimization. We address uncertainty and data quality issues, emphasizing the importance of incorporating confidence intervals and raw data quality assessment in data processing workflows. Furthermore, we highlight the need for cross-study comparability and propose potential solutions, such as utilizing standardized statistics and open-access data exchange platforms. In conclusion, we offer future perspectives and recommendations for developers and users of NTS data processing algorithms and workflows. By addressing these challenges and capitalizing on the opportunities presented, the NTS community can advance the field, improve the reliability of results, and enhance data comparability across different studies.

Development of a scoring parameter to characterize data quality of centroids in high-resolution mass spectra.

High-resolution mass spectrometry is widely used in many research fields allowing for accurate mass determinations. In this context, it is pretty standard that high-resolution profile mode mass spectra are reduced to centroided data, which many data processing routines rely on for further evaluation. Yet information on the peak profile quality is not conserved in those approaches; i.e., describing results reliability is almost impossible. Therefore, we overcome this limitation by developing a new statistical parameter called data quality score (DQS). For the DQS calculations, we performed a very fast and robust regression analysis of the individual high-resolution peak profiles and considered error propagation to estimate the uncertainties of the regression coefficients. We successfully validated the new algorithm with the vendor-specific algorithm implemented in Proteowizard's msConvert. Moreover, we show that the DQS is a sum parameter associated with centroid accuracy and precision. We also demonstrate the benefit of the new algorithm in nontarget screenings as the DQS prioritizes signals that are not influenced by non-resolved isobaric ions or isotopic fine structures. The algorithm is implemented in Python, R, and Julia programming languages and supports multi- and cross-platform downstream data handling.

Surface Activation of Polylactic Acid-Based Wood-Plastic Composite by Atmospheric Pressure Plasma Treatment.

Wood-plastic composite (WPC) based on a polylactic acid (PLA) matrix is a promising material since it is biobased, degradable, sustainable, and 3D printable. However, due to its coloring, visible layers after 3D-printing, and small build volumes of these printers, a coating or gluing of parts might be required. This study investigates the influence of a dielectric barrier discharge (DBD) plasma treatment of PLA-based WPC to activate the surface and improve, e.g., coating capabilities. X-ray photoelectron spectroscopy (XPS) measurements showed the oxidation of the surface due to the formation of carbonyl and carboxyl groups. Laser scanning microscopy revealed a surface roughening after the treatment. Contact angles of water and diiodomethane decreased significantly after the plasma treatment and the consecutively calculated surface free energy increased. Finally, two practical adhesion tests revealed an improvement of the applied acrylic dispersion coating's adhesion to the WPC surface: The assigned cross-cut class improved, and the pull-off strength increased from 1.4 to 2.3 N/mm2.

Plasma Treatment of Polypropylene-Based Wood-Plastic Composites (WPC): Influences of Working Gas.

In this study, a polypropylene (PP)-based wood-plastic composite with maleic anhydride-grafted polypropylene (MAPP) as a coupling agent and a wood content of 60% was extruded and specimens were injection molded. The samples were plasma treated utilizing a dielectric barrier discharge (DBD) setup with three different working gases: Ar/O2 (90%/10%), Ar/N2 (90%/10%), and synthetic air. This process aims to improve the coating and gluing properties of the otherwise challenging apolar surface of PP based wood-plastic composites (WPC). Chemical analysis with X-ray photoelectron spectroscopy (XPS) and Fourier-transform infrared spectroscopy (FTIR) showed the formation of oxygen-based functional groups on the surface, independently from the working gas used for the treatment. Laser scanning microscopy (LSM) examined the surface roughness and revealed that the two argon-containing working gases roughened the surface more than synthetic air. However, the contact angle for water was reduced significantly after treatment, revealing measurement artifacts for water and diiodomethane due to the severe changes in surface morphology. The adhesion of acrylic dispersion coating was significantly increased, resulting in a pull-off strength of approximately 4 N/mm2, and cross-cut tests assigned the best adhesion class (0), on a scale from 0 to 5, after plasma treatment with any working gas.

Hydrophobicity-water/air-based enrichment cell for microplastics analysis within environmental samples: A proof of concept.

The analysis of microplastics in sediments, soils or beach samples is commonly paired with a separation step to enrich microplastics or to remove non-plastics, respectively. Those steps are often very time consuming and are performed in presence of high concentrated solvents. The latter are also suspected to corrode or decompose the analyte particles, which hamper further identification processes. This paper describes a new fast and effective microplastics separation apparatus for analytical issues that was based on hydrophobic adhesion of microplastics and fine air bubbles. The presented prototype could successfully enrich over 90 %wt of 30ppmw microplastics in 200 g sand in 20 min. Additionally, it could be demonstrated that the new separation technique was very suitable for further microplastics identification by FTIR microscopy. In this context, a sample with different polymers and matrix components was analyzed and the results were presented within this article. •Microplastics were enriched selectively by hydrophobic adhesion.•No additional chemicals except water and air were used.•Separation took only 20 min and 90 %wtof microplastics were recovered.

Automated rapid & intelligent microplastics mapping by FTIR microscopy: A Python-based workflow.

The analysis of environmental microplastic particles using FTIR microscopy is a challenging task, due to the very high number of individual particles within a single sample. Therefore, automatable, fast and robust approaches are highly requested. Micro particles were commonly enriched on filters, and sub- or the whole filter area was investigated, which took more than 20h and produced millions of data, which had to be evaluated. This paper presents a new approach of such filter area analysis using an intelligent algorithm to measure only those spots on a filter that would produce evaluable FTIR data. Empty spaces or IR absorbers like carbon black particles were not measured which successfully reduced the total analysis time from 50h to 7h. The presented method is based on system independent Python workflow and can easily be implemented on other FTIR systems. •Fast and intelligent FTIR microscopy area mapping without FPA detector•Total time reduction from 50 h to 7 h•Platform independent approach based on Python.

Robust Automatic Identification of Microplastics in Environmental Samples Using FTIR Microscopy.

The analysis of microplastics is mainly performed using Fourier transformation infrared spectroscopy/microscopy (FTIR/ µFTIR). However, in contrast to most aspects of the analysis process, for example, sampling, sample preparation, and measurement, there is less known about data evaluation. This particularly critical step becomes more and more important if a large number of samples has to be handled. In this context, it is concerning that the commonly used library searching is not suitable to identify microplastics from real environmental samples automatically. Therefore, many spectra have to be rechecked by the operator manually, which is very time-consuming. In this study, a new fully automated robust microplastics identification method is presented that assigns over 98% of microplastics correctly. The main concept of this new method is to detect and numerically describe the individual vibrational bands within an FTIR absorbance spectrum by curve fitting, which leads to a very compact and highly characteristic peak list. This list allows very accurate and robust library searching. The developed approach is based on the already published microplastics identification algorithm (µIDENT) and extends and improves the field of application to µFTIR data with a special focus on relevant broad, overlapped, or complex vibrational bands.

A New Chemometric Approach for Automatic Identification of Microplastics from Environmental Compartments Based on FT-IR Spectroscopy.

One key step studying interactions of microplastics with our ecological system is to identify plastics within environmental samples. Aging processes and surface contamination especially with biofilms impede this characterization. A complex and time-consuming cleaning procedure is a common solution for this problem. However, it implies an artificial change of sample composition with a risk of losing important information or even damaging microplastic particles. In the present work, we introduce a new chemometric approach to identify heavily weathered and contaminated microplastics without any cleaning. The main idea of this concept is based on an automated curve fitting of most relevant vibrational bands to calculate a highly characteristic fingerprint that contains all vibrational band area ratios. This new data set will be used to estimate the similarity of samples and reference standards for identification. A total of 300 individual naturally weathered plastic particles were measured with Fourier transformation infrared spectroscopy in attenuated total reflection mode (FT-IR ATR) and identified successfully with the new method. To that end, all samples were compared with a selection of common reference plastics and bio polymers. As it turns out, the accuracy of identification rises significantly from 76% by means of conventional library searching algorithms to 96% by identifying microplastics with our new method. Therefore, the new approach can be a useful tool to compare and describe similarities of FT-IR spectra of microplastics, which may improve further research studies on this topic.