Profiles, exposure assessment and expanded screening of PAHs and their derivatives in one petroleum refinery facility of China.

This study investigated environmental distribution and human exposure of polycyclic aromatic hydrocarbons (PAHs) and their derivatives in one Chinese petroleum refinery facility. It was found that, following with high concentrations of 16 EPA PAHs (∑Parent-PAHs) in smelting subarea of studied petroleum refinery facility, total derivatives of PAHs [named as XPAHs, including nitro PAHs (NPAHs), chlorinated PAHs (Cl-PAHs), and brominated PAHs (Br-PAHs)] in gas (mean= 1.57 × 104 ng/m3), total suspended particulate (TSP) (mean= 4.33 × 103 ng/m3) and soil (mean= 4.37 × 103 ng/g) in this subarea had 1.76-6.19 times higher levels than those from other subareas of this facility, surrounding residential areas and reference areas, indicating that petroleum refining processes would lead apparent derivation of PAHs. Especially, compared with those in residential and reference areas, gas samples in the petrochemical areas had higher ∑NPAH/∑PAHs (mean=2.18), but lower ∑Cl-PAH/∑PAHs (mean=1.43 × 10-1) and ∑Br-PAH/∑PAHs ratios (mean=7.49 × 10-2), indicating the richer nitrification of PAHs than chlorination during petrochemical process. The occupational exposure to PAHs and XPAHs in this petroleum refinery facility were 24-343 times higher than non-occupational exposure, and the ILCR (1.04 × 10-4) for petrochemical workers was considered to be potential high risk. Furthermore, one expanded high-resolution screening through GC Orbitrap/MS was performed for soils from petrochemical area, and another 35 PAHs were found, including alkyl-PAHs, phenyl-PAHs and other species, indicating that profiles and risks of PAHs analogs in petrochemical areas deserve further expanded investigation.

Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening.

Non-targeted screening with liquid chromatography coupled to high-resolution mass spectrometry (LC/HRMS) is increasingly leveraging in silico methods, including machine learning, to obtain candidate structures for structural annotation of LC/HRMS features and their further prioritization. Candidate structures are commonly retrieved based on the tandem mass spectral information either from spectral or structural databases; however, the vast majority of the detected LC/HRMS features remain unannotated, constituting what we refer to as a part of the unknown chemical space. Recently, the exploration of this chemical space has become accessible through generative models. Furthermore, the evaluation of the candidate structures benefits from the complementary empirical analytical information such as retention time, collision cross section values, and ionization type. In this critical review, we provide an overview of the current approaches for retrieving and prioritizing candidate structures. These approaches come with their own set of advantages and limitations, as we showcase in the example of structural annotation of ten known and ten unknown LC/HRMS features. We emphasize that these limitations stem from both experimental and computational considerations. Finally, we highlight three key considerations for the future development of in silico methods.

Screening the release of chemicals and microplastic particles from diverse plastic consumer products into water under accelerated UV weathering conditions.

Photodegradation of plastic consumer products is known to accelerate weathering and facilitate the release of chemicals and plastic particles into the aquatic environment. However, these processes are complex. In our presented pilot study, eight plastic consumer products were leached in distilled water under strong ultraviolet (UV) light simulating eight months of Central European climate and compared to their respective dark controls (DCs). The leachates and formed plastic particles were exploratorily characterized using a range of chemical analytical tools to describe degradation and leaching processes. These techniques covered (a) microplastic analysis, showing substantial liberation of plastic particles further increased under UV exposure, (b) non-targeted mass spectrometric characterization of the leachates, revealing several hundreds of chemical features with typically only minor agreement between the UV exposure and the corresponding DCs, (c) target analysis of 71 organic analytes, of which 15 could be detected in at least one sample, and (d) metal(loid) analysis, which revealed substantial release of toxic metal(loid)s further enhanced under UV exposure. A data comparison with the US-EPA's ToxVal and ToxCast databases showed that the detected metals and organic additives might pose substantial health and environmental concerns, requiring further study and comprehensive impact assessments.

Assessment of emerging and persistent contaminants in an anthropogenic-impacted watershed: Application using targeted, non-targeted, and in vitro bioassay techniques.

Emerging and persistent contaminants (EPC) pose a significant challenge to water quality monitoring efforts. Effect-based monitoring (EBM) techniques provide an efficient and systematic approach in water quality monitoring, but they tend to be resource intensive. In this study, we investigated the EPC distribution for various land uses using target analysis (TA) and non-target screening (NTS) and in vitro bioassays, both individually and integrated, in the upper Ping River Catchment, northern Thailand. Our findings of NTS showed that urban areas were the most contaminated of all land use types, although agriculture sites had high unexpected pollution levels. We evaluated the reliability of NTS data by comparing it to TA and observed varying inconsistencies likely due to matrix interferences and isobaric compound interferences. Integrating NTS with in vitro bioassays for a thorough analysis posed challenges, primary due to a scarcity of concentration data for key compounds, and potentially additive or non-additive effects of mixture samples that could not be accounted for. While EBM approaches place emphasis on toxic sites, this study demonstrated the importance of considering non-bioactive sites that contain toxic compounds with antagonistic effects that may go undetected by traditional monitoring approaches. The present work emphasizes the importance of improving NTS workflows and ensuring high-quality EBM analyses in future water quality monitoring programs.

Recognition of screening out hierarchical toxic contaminants tuned by quantified pseudo-components from complex engineering co-combustion.

Co-combustion of industrial and municipal solid wastes has emerged as the most promising disposal technology, yet its effect on unknown contaminants generation remains rarely revealed due to waste complexity. Hence, six batches of large-scale engineering experiments were designed in an incinerator of 650 t/d, which overcame the inauthenticity and deviation of laboratory tests. 953-1772 non-targeted compounds were screened in fly ash. Targeting the impact of co-combustion, a pseudo-component matrix model was innovatively integrated to quantitatively extract nine components from complex wastes grouped into biomass and plastic. Thus, the influence was evaluated across eight dimensions, covering molecular characteristics and toxicity. The effect of co-combustion with biomass pseudo-components was insignificant. However, co-combustion with high ratios of plastic pseudo-components induced higher potential risks, significantly promoting the formation of unsaturated hydrocarbons, highly unsaturated compounds (DBE≥15), and cyclic compounds by 19 %- 49 %, 17 %- 31 %, and 7 %- 27 %, respectively. Especially, blending with high ratios of PET plastic pseudo-components produced more species of contaminants. Unique 2 Level I toxicants, bromomethyl benzene and benzofuran-2-carbaldehyde, as well as 4 Level II toxicants, were locked, receiving no concern in previous combustion. The results highlighted risks during high proportion plastics co-combustion, which can help pollution reduction by tuning source wastes to enable healthy co-combustion.

A Support Vector Machine-Assisted Metabolomics Approach for Non-Targeted Screening of Multi-Class Pesticides and Veterinary Drugs in Maize.

The contamination risks of plant-derived foods due to the co-existence of pesticides and veterinary drugs (P&VDs) have not been fully understood. With an increasing number of unexpected P&VDs illegally added to foods, it is essential to develop a non-targeted screening method for P&VDs for their comprehensive risk assessment. In this study, a modified support vector machine (SVM)-assisted metabolomics approach by screening eligible variables to represent marker compounds of 124 multi-class P&VDs in maize was developed based on the results of high-performance liquid chromatography-tandem mass spectrometry. Principal component analysis and orthogonal partial least squares discriminant analysis indicate the existence of variables with obvious inter-group differences, which were further investigated by S-plot plots, permutation tests, and variable importance in projection to obtain eligible variables. Meanwhile, SVM recursive feature elimination under the radial basis function was employed to obtain the weight-squared values of all the variables ranging from large to small for the screening of eligible variables as well. Pairwise t-tests and fold changes of concentration were further employed to confirm these eligible variables to represent marker compounds. The results indicate that 120 out of 124 P&VDs can be identified by the SVM-assisted metabolomics method, while only 109 P&VDs can be found by the metabolomics method alone, implying that SVM can promote the screening accuracy of the metabolomics method. In addition, the method's practicability was validated by the real contaminated maize samples, which provide a bright application prospect in non-targeted screening of contaminants. The limits of detection for 120 P&VDs in maize samples were calculated to be 0.3~1.5 µg/kg.

[Screening of Highly Hazardous Pollutants in Groundwater of Beijing].

Groundwater contaminants pose a great threat to water safety and human health. Therefore, in this study, the traditional hazard assessment method was improved and a comprehensive system covering hazard assessment, screening, and characterization by combining the toxicological priority index (Tox Pi) framework; absorption, distribution, metabolism, and excretory (ADME) analysis; and bipartite network analysis was constructed. Then, the system was applied to hazard assessment and toxic pollutants screening from the 234 hydrophobic organic contaminants (HOCs) identified in the groundwater of Beijing. First, the top 20 pollutants with hazard potential were screened out using the Tox Pi method. Subsequently, 17 high-priority HOCs were further identified based on the ADME property analysis. Then, the molecular targets of these 17 high-priority HOCs were characterized through systematic bipartite network analysis. Finally, ten HOCs with high hazard were screened through correlation and weighted average analysis, and it was revealed that their toxic effects were mainly concentrated in the endocrine-disrupting effect, carcinogenic effect, and genetic toxicity. This study provides technical support for the prevention of regional groundwater contaminants.

Cyanoacetohydrazide as a Novel Derivatization Agent for the Determination of UHPLC-HRMS Steroids in Urine.

The possibility of cyanoacetohydrazide usage as a novel derivatizing agent is demonstrated in the presented article, and a comparison with hydroxylamine as the most commonly used reagent is provided. Optimal conditions for steroid derivatization with cyanoacetohydrazide are provided. According to the collected data, the maximum yield of derivatives was observed at pH 2.8 within 70 min at 40 °C with 5 ng/mL limit of detection for all investigated analytes. It was shown that cyanoacetohydrazide derivatives produces both syn- and anti-forms as well as hydroxylamine, and their ratios were evaluated and shown in presented work. An efficiency enchantment from two to up to five times was achieved with a novel derivatization reagent. Its applicability for qualitative analysis of steroids in urine was presented at real samples. Additionally, the reproducible fragmentation of the derivatizing agent in collision-induced dissociation offers opportunities for simplified non-targeted steroidomic screening. Furthermore, cyanoacetohydrazide increases ionization efficiency in positive mode, which can eliminate the need for redundant high-resolution instrument runs required for both positive and negative mode analyses.

Identification and quantification of per- and polyfluorinated alkyl substances (PFAS) migrating from food contact materials (FCM).

Per- and polyfluorinated alkyl substances (PFAS) can be added to food contact materials (FCM) to increase their water and/or grease repellent properties. Some well-known PFAS are perfluoroalkyl carboxylic acids (PFCA), perfluoroalkyl sulfonic acids (PFSA), and polyfluorinated telomer alcohols (FTOH). Due to the strength of the carbon-fluorine bond, PFAS are chemically very stable and highly resistant to biological degradation, posing a risk to human health and the environment. To examine the presence of PFAS in paper-based FCM, various samples were collected, including popcorn bags, muffin cups, and pizza boxes with high total organic fluorine (TOF) content from the Danish and Spanish markets. The FCM composition was characterised by FTIR. Quantification of some well-known PFAS such as PFCA, PFSA, and FTOH was performed in food simulants using LC-MS/MS, and in addition a non-targeted screening approach was performed by LC-Orbitrap-HRMS. Among analysed samples, the highest concentrations of PFAS were found in a muffin cup made of cellulose (PFCA âˆ¼ 1.41 µg kg-1 food, FTOH âˆ¼ 11.5 µg kg-1 food), and the results were used to estimate dietary exposures to PFAS migrated from this FCM. Compared to measured TOF value in this sample, the fluorine from all quantified PFAS accounted for only 0.6%. Thus, a more powerful analytical approach was used to further investigate PFAS occurrence in this sample. Using non-targeted screening, an additional twenty compounds were identified, among them five with confidence level 1 and ten with confidence level 2. Many of them were either fluorotelomer carboxylic acids or sulfonic acids or ether-containing compounds.

Insights into the early-life chemical exposome of Nigerian infants and potential correlations with the developing gut microbiome.

Early-life exposure to natural and synthetic chemicals can impact acute and chronic health conditions. Here, a suspect screening workflow anchored on high-resolution mass spectrometry was applied to elucidate xenobiotics in breast milk and matching stool samples collected from Nigerian mother-infant pairs (n = 11) at three time points. Potential correlations between xenobiotic exposure and the developing gut microbiome, as determined by 16S rRNA gene amplicon sequencing, were subsequently explored. Overall, 12,192 and 16,461 features were acquired in the breast milk and stool samples, respectively. Following quality control and suspect screening, 562 and 864 features remained, respectively, with 149 of these features present in both matrices. Taking advantage of 242 authentic reference standards measured for confirmatory purposes of food bio-actives and toxicants, 34 features in breast milk and 68 features in stool were identified and semi-quantified. Moreover, 51 and 78 features were annotated with spectral library matching, as well as 416 and 652 by in silico fragmentation tools in breast milk and stool, respectively. The analytical workflow proved its versatility to simultaneously determine a diverse panel of chemical classes including mycotoxins, endocrine-disrupting chemicals (EDCs), antibiotics, plasticizers, perfluorinated alkylated substances (PFAS), and pesticides, although it was originally optimized for polyphenols. Spearman rank correlation of the identified features revealed significant correlations between chemicals of the same classification such as polyphenols. One-way ANOVA and differential abundance analysis of the data obtained from stool samples revealed that molecules of plant-based origin elevated as complementary foods were introduced to the infants' diets. Annotated compounds in the stool, such as tricetin, positively correlated with the genus Blautia. Moreover, vulgaxanthin negatively correlated with Escherichia-Shigella. Despite the limited sample size, this exploratory study provides high-quality exposure data of matched biospecimens obtained from mother-infant pairs in sub-Saharan Africa and shows potential correlations between the chemical exposome and the gut microbiome.

Composition and release rates of chemicals in inkjet fabrics determined by non-targeted screening and targeted analysis.

Unveiling composition and release rates of chemicals in chemical-intensive products (CIPs) such as inkjet fabrics that are applied extensively in advertising and publicizing industries, is of importance to sound management of chemicals. This study tentatively identified 212 compounds from 69 inkjet fabric samples using gas chromatograph coupled with quadrupole time-of-flight mass spectrometry (GC-QTOF-MS). Contents of six phthalate esters (PAEs) were quantified to range from 3.0 × 102 mg/kg to 3.1 × 105 mg/kg with GC-MS. Bis(2-ethylhexyl) phthalate was predominantly detected to average 96 g/kg. The inkjet fabrics collected from southern China contained fewer non-intentionally added substances (NIASs) than from northern China. Annual mass release rates (RM) of the 6 PAEs from inkjet fabrics to air were estimated to range from 1.4 × 10-2 kg/year to 2.8 × 104 kg/year in China in 2020, and the mean indoor RM was comparable with the outdoor one. Equilibrium partition coefficients of the compounds between the product and air, ambient temperature, and concentrations of chemicals in the product, are key factors leading to RM with the high variance. The findings indicate that contents of the NIASs in the CIPs should be minimized, and the refining concept should be adopted in design of the CIPs, so as to control the release of chemicals from the CIPs.

Advances in the application of machine learning in the identification and authentication of synthetic cannabinoids / 中国药科大学学报

@#Abstract: Synthetic cannabinoids (SCs) are synthetic psychoactive substances that can pose a public health risk. The SCs are structurally variable and susceptible to structural modification. The rapid emergence of structurally unknown synthetic cannabinoids has led to new challenges in their identification. In recent years, machine learning has made great progress and has been widely applied to other fields, providing new strategies for the identification of unknown synthetic cannabinoids and the inference of possible sources. This paper describes the principles of commonly used machine learning methods and the application of machine learning techniques to mass spectrometry, Raman spectroscopy, metabolomics and quantitative conformational relationships of synthetic cannabinoids, aiming to provide new ideas for the identification of unknown synthetic cannabinoids.

LC-ESI QToF MS Non-Targeted Screening of Latex Extracts of Euphorbia seguieriana ssp. seguieriana Necker and Euphorbia cyparissias and Determination of Their Potential Anticancer Activity.

Euphorbia seguieriana ssp. seguieriana Necker (ES) and Euphorbia cyparissias (EC) with a habitat in the Deliblato Sands were the subject of this examination. The latexes of these so far insufficiently investigated species of the Euphorbia genus are used in traditional medicine for the treatment of wounds and warts on the skin. To determine their chemical composition, non-targeted screening of the latexes' chloroform extracts was performed using liquid chromatography coupled with quadrupole time-of-flight mass spectrometry employing an electrospray ionization source (LC-ESI QTOF MS). The analysis of the obtained results showed that the latexes of ES and EC represent rich sources of diterpenes, tentatively identified as jatrophanes, ingenanes, tiglianes, myrsinanes, premyrsinanes, and others. Examination of the anticancer activity of the ES and EC latex extracts showed that both extracts significantly inhibited the growth of the non-small cell lung carcinoma NCI-H460 and glioblastoma U87 cell lines as well as of their corresponding multi-drug resistant (MDR) cell lines, NCI-H460/R and U87-TxR. The obtained results also revealed that the ES and EC extracts inhibited the function of P-glycoprotein (P-gp) in MDR cancer cells, whose overexpression is one of the main mechanisms underlying MDR.

Recent Advances in Non-Targeted Screening of Compounds in Plastic-Based/Paper-Based Food Contact Materials.

Ensuring the safety of food contact materials has become a pressing concern in recent times. However, detecting hazardous compounds in such materials can be a complex task, and traditional screening methods may not be sufficient. Non-targeted screening technologies can provide comprehensive information on all detectable compounds, thereby supporting the identification, detection, and risk assessment of food contact materials. Nonetheless, the non-targeted screening of food contact materials remains a challenging issue. This paper presents a detailed review of non-targeted screening technologies relying on high-resolution mass spectrometry for plastic-based and paper-based food contact materials over the past five years. Methods of extracting, separating, concentrating, and enriching compounds, as well as migration experiments related to non-targeted screening, are examined in detail. Furthermore, instruments and devices of high-resolution mass spectrometry used in non-targeted screening technologies for food contact materials are discussed and summarized. The research findings aim to provide a theoretical basis and practical reference for the risk management of food contact materials and the development of relevant regulations and standards.

Research Progress on Machine Learning Assisted Non-Targeted Screening Strategy for Identification of Fentanyl Analogs.

In recent years, the types and quantities of fentanyl analogs have increased rapidly. It has become a hotspot in the illicit drug control field of how to quickly identify novel fentanyl analogs and to shorten the blank regulatory period. At present, the identification methods of fentanyl analogs that have been developed mostly rely on reference materials to target fentanyl analogs or their metabolites with known chemical structures, but these methods face challenges when analyzing new compounds with unknown structures. In recent years, emerging machine learning technology can quickly and automatically extract valuable features from massive data, which provides inspiration for the non-targeted screening of fentanyl analogs. For example, the wide application of instruments like Raman spectroscopy, nuclear magnetic resonance spectroscopy, high resolution mass spectrometry, and other instruments can maximize the mining of the characteristic data related to fentanyl analogs in samples. Combining this data with an appropriate machine learning model, researchers may create a variety of high-performance non-targeted fentanyl identification methods. This paper reviews the recent research on the application of machine learning assisted non-targeted screening strategy for the identification of fentanyl analogs, and looks forward to the future development trend in this field.

Chemometrics-based Discrimination of Virgin and Recycled Acrylonitrile-Butadiene-Styrene Plastics Toys via Non-targeted Screening of Volatile Substances.

Owing to the growing emphasis on child safety, it is greatly urgent to identify and assess the unknown compounds and discriminate the recycled materials for plastic toys. In this study, gas chromatography mass spectrometry coupled with static headspace has been optimized by response surface methodology for non-targeted screening of unknown volatiles in acrylonitrile-butadiene-styrene (ABS) plastic toys. Optimum conditions for static headspace were 120 °C for extraction temperature and 48 min for extraction time. A total of 83 volatiles in 11 categories were qualitatively identified by matching the NIST database library, retention index and standard materials. Considering high positive rate and potential toxicity, high-risk volatiles in ABS plastic toys were listed and traced for safety pre-warning. Moreover, the differential volatiles between virgin and recycled ABS plastics were screened out by orthogonal partial least-squares discrimination analysis. Principal component analysis, hierarchical cluster analysis and linear discrimination analysis were employed to successfully discriminate recycled ABS plastic toys based on the differential volatiles. The proposed strategy represents an effective and promising analytical method for non-targeted screening and risk assessment of unknown volatiles and discrimination of recycled materials combining with various chemometric techniques for children's plastic products to safeguard children's health.

Targeted and non-targeted screening of flame retardants in rural and urban honey.

Flame retardants (FRs) are often added to commercial products to achieve flammability resistance, but they are not chemically bonded to the materials, so, they can be easily released into the environment during the production and disposal processes. When honeybees travel to collect nectar during the pollination process, they are prone to be contaminated by chemicals in the air. Therefore, honey contamination has been proposed as an indicator of the pollution status in a particular region. To date, the occurrence of flame retardants in urban honey has yet to be explored. In this study, a direct injection method was used, coupled with LC-QTOF-MS, to analyze honey samples. This method was applied to urban (n = 100) and rural (n = 100) honey samples from the Quebec province (Canada), and the levels of flame retardants in urban and rural honey samples were not significantly different. In the targeted approach, two of the target FRs, tris(2-butoxyethyl) phosphate (TBOEP) and triphenyl phosphate (TPHP), were detected and confirmed at an average trace concentration (<1 ng mL-1). Additionally, a non-targeted screening workflow with an in-house-built library was developed and validated to screen for flame retardants in honey. Tris (2-chloropropyl) phosphate (TCIPP) was identified in honey using the non-targeted screening workflow and confirmed using a pure analytical standard, but there are other compounds detected in the non-targeted analysis that have yet to be validated. This study was the first to report FR compounds based on a direct injection method, coupled with a non-targeted screening workflow, at a trace level in a honey matrix. It also showed that a non-targeted workflow was effective to detect and identify unknown compounds present in the honey sample; hence, this provided a novel angle for the occurrence of FRs in air, with honey as a bio-indicator.

Ultrahigh performance LC/FT-MS non-targeted screening for biomass burning organic aerosol with MZmine2 and MFAssignR.

In recent years, ultrahigh performance liquid chromatography Fourier transform mass spectrometry (LC/FT-MS) based non-targeted screening (NTS) methods have become increasingly popular for comprehensive analysis of complex organic mixtures. However, applying these methods for environmental complex mixture analysis is challenging due to the extreme complexity of natural samples and a lack of standard samples or surrogates for environmental complex mixtures. Furthermore, limited molecular markers in the databases and insufficient data processing software workflows make the application of these methods more challenging for environmental complex mixtures. In this work, we implement a new NTS data processing workflow to process data collected from ultrahigh performance liquid chromatography and Fourier transform Orbitrap Elite Mass Spectrometry (LC/FT-MS) by combining MZmine2 and MFAssignR, two opensource data processing tools and commercial Mesquite liquid smoke as a surrogate for biomass burning organic aerosol. MZmine2.53 data extraction followed MFAssignR molecular formula assignment offered noise free and highly accurate 1733 individual molecular formulas presented in liquid smoke with 4906 molecular species, including isomers. The results of this new approach were consistent with the results of direct infusion FT-MS analysis confirming its reliability. Over 90% of the molecular formulas presented in mesquite liquid smoke were matched with the molecular formulas of ambient biomass burning organic aerosol. This suggests the potential use of commercial liquid smoke is an acceptable surrogate for biomass burning organic aerosol research. The presented method significantly improves the identification of the molecular composition of biomass burning organic aerosol by successfully addressing some of the limitations related to the data analysis and giving a semi quantitative insight into the analysis.

ATR-FTIR combined with machine learning for the fast non-targeted screening of new psychoactive substances.

Due to the diversity and fast evolution of new psychoactive substances (NPS), both public health and safety are threatened around the world. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR), which serves as a simple and rapid technique for targeted NPS screening, is challenging with the rapid structural modifications of NPS. To achieve the fast non-targeted screening of NPS, six machine learning (ML) models were constructed to classify eight categories of NPS, including synthetic cannabinoids, synthetic cathinones, phenethylamines, fentanyl analogues, tryptamines, phencyclidine types, benzodiazepines, and "other substances" based on the 1099 IR spectra data items of 362 types of NPS collected by one desktop ATR-FTIR and two portable FTIR spectrometers. All these six ML classification models, including k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), extra trees (ET), voting, and artificial neural networks (ANNs) were trained through cross validation, and f1-scores of 0.87-1.00 were achieved. In addition, hierarchical cluster analysis (HCA) was performed on 100 synthetic cannabinoids with the most complex structural variation to investigate the structure-spectral property relationship, which leads to a summary of eight synthetic cannabinoid sub-categories with different "linked groups". ML models were also constructed to classify eight synthetic cannabinoid sub-categories. For the first time, this study developed six ML models, which were suitable for both desktop and portable spectrometers, to classify eight categories of NPS and eight synthetic cannabinoids sub-categories. These models can be applied for the fast, accurate, cost-effective, and on-site non-targeted screening of newly emerging NPS with no reference data available.

Non-targeted screening of emerging contaminants in drinking water based on high-resolution mass spectrometry / 上海预防医学

ObjectiveTo establish a non-targeted screening method for emerging contaminants in drinking water based on high-resolution mass spectrometry and apply it to actual water samples. MethodsA total of 9 drinking water samples collected from 3 reservoirs in Shanghai were purified and concentrated by HLB solid phase extraction column， then separated and analyzed by liquid chromatography high-resolution mass spectrometer and gas chromatography high⁃resolution mass spectrometer. The acquired data were analyzed by Thermo Tracefinder， Excel and other software combined with mzCloud and NIST databases. The methodology was verified with representative compound standards. Pesticide and perfluorinated compounds were taken as examples to analyze their pollution status. ResultsA non-targeted analysis strategy based on liquid chromatography and gas chromatography tandem high-resolution mass spectrometry was established. The pollution level of 20 kinds of pesticides and 4 kinds of perfluorinated compounds identified in 9 drinking water samples were higher in the Huangpu River than in the Yangtze River estuary. ConclusionThe established non-targeted screening method by high-resolution mass spectrometry can detect potential emerging contaminants in drinking water without relying on the standards， which provides a powerful technical means for water quality monitoring and risk assessment.