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• An LC-HRMS method for analysis of phytotoxins in the environment was developed. • Optimized LC-HRMS condition for non-target analysis was suggested. • The detection sensitivity was up to 40 times higher for the optimized method. • More than 30 phytotoxins were detected in an environmental study. Phytotoxins are plant secondary metabolites. They have recently been considered as chemicals of emerging concern (CECs) and there is a growing interest in their environmental fate and potential threat to public health. Dedicated target and non-target screening (NTS) analysis of phytotoxins in environmental samples are sparse, meanwhile phytotoxins are rarely detected in NTS-based analysis due to lack of an efficient methodology. Development of new analytical measurement methods is therefore highly needed. In this study, we for the first time investigated key parameters of reversed phase liquid chromatography-high resolution mass spectrometry (RPLC-HRMS) for five major classes of phytotoxins (alkaloids, steroids, terpenoids, flavonoids and aromatic polyketides) in environmental matrices; the investigation included analytical conditions which have not yet been explored by others, e.g. ionization at alkaline pH above 9. As the outcome we established a new analytical method for target/suspect screening and NTS of phytotoxins in the environment, which significantly improved the detection sensitivity with up to 40 times compared to previous methods, and enabled the discovery of over 30 phytotoxins in a NTS-based environmental study. We also observed that the negative ionization of phenols could be facilitated by the number of hydroxyl groups on the ring rather than their position of substitution. This study is of interest for a better fundamental understanding of the behavior of phytotoxins in LC-MS. Dedicated target/suspect screening and NTS methods will facilitate a better risk characterization of phytotoxins in the environment and stimulate implementation of new public regulation on phytotoxins. [ABSTRACT FROM AUTHOR]

Eight samples were obtained to characterize the chemical loads in water recycled for reuse applications. The sources included stormwater, rooftop runoff, wastewater, mixed water, and drinking water as a comparison. The water was reused for irrigation, cleaning, toilet flushing, and cooling purposes. Large-volume injection (650 μL) high-performance liquid chromatography and quadrupole time-of-flight mass spectrometry were employed to separate and detect features by suspect and non-target screening. The instrumental method had the advantage that no sample extractions were required prior to analysis. Two chromatographic methods were developed to separate positive- and negative-ionizing compounds and retention time models were developed for both. Retention time models provide an additional measure of confidence for probable and tentative identifications. The two models had predictive R2—which indicates how well the models predicts new observations—of 0.87. After data-reduction, the number of features detected in the samples ranged from 304 to 1513. Feature metrics such as the average response-per-feature provided a simple method to characterize similarities and differences between samples. Additionally, a statistical comparison was performed by principal component analysis. Of the 97 suspect-screening compounds, 20 were positively identified. Benzotriazole/benzothiazole-derivatives and per- and poly-fluoroalkyl substances were the most frequently detectedcompounds during suspect screening. Other compounds detected included pharmaceuticals, drug metabolites, and sucralose. Features were prioritized for non-target analysis based on in-house library matches, magnitude of response, and frequency of occurrence. Fifty-five unique compounds were positively identified via non-target analysis. The identified compounds included 17 pharmaceuticals, 17 pesticides, 13 industrial compounds, four personal-use compounds, and four biological compounds. Image 1 • Large-volume injection eliminated the need for sample pre-concentration. • Retention models were developed to support non-target identifications. • Feature metrics and principal component analysis were used to compare samples. • Eighty-two individual contaminants were identified with level 1 or 2 confidence. • Several previously unreported environmental contaminants were identified. [ABSTRACT FROM AUTHOR]

• Application of herbicides influences the catecholamines content in plant tissues. • Selected catecholamines may be considered as markers of plant exposure to RIM. • Non-target analysis enables to identify the source of herbicide stress. Raphanus sativus var. longipinnatus , was exposed under experimental conditions to herbicides: rimsulfuron (RIM), administrated as (1) pure substance, (2) in commercially available formulation (RIMEL), (3) its degradation product: 4,6-dimethoxypyrimidin-2-amine (2ADP), (4) mesotrione (MES), (5) sulcotrione (SUL). Profiling and fingerprinting strategies, conducted by LC-MS/MS-FL, were employed to find markers of plant exposure to herbicide stress. The presence of RIM metabolite in the tissues of plant exposed to this herbicide proved that it is necessary to determine both parent compound and its by-products to obtain reliable information on plant exposure to agrochemicals. A higher content of normetanephrine (NMN) (18–175%) and lower content of tyramine (TYR) (49–75%) and epinephrine (E) (75–83%) was observed in plant tissues exposed to RIM and 2ADP in comparison to blank sample. Therefore, NMN, TRY and E may be considered as markers of plant response to RIM. Non-target analysis enables to recognize the type of herbicide used during cultivation. [ABSTRACT FROM AUTHOR]

Targeting the most relevant organic micropollutants (OMP) in routine analysis appears difficult due to formation of transformation products of unknown concentration or toxicity. Performance assessment of water purification processes is still based upon limited target data. Therefore, we broadened the assessment of the removal efficiencies with combined non-target and effect-directed analysis at four riverbank filtration (RBF) sites in Germany. To assess micropollutant elimination, constancy and formation during different seasons, considering local redox conditions, travel distances and total component number in the river, non-target analysis features were grouped into categories. Furthermore, RBF sites were investigated with four endpoints (baseline toxicity, acetylcholinesterase inhibition, antibiotic effects and estrogenic effects) for thin-layer chromatography - effect-directed analysis for the first time. Results showed elimination or reduction of many features and effects, but also constancy and formation of varying proportions. Fall river samples showed precipitation-caused dilution in both tests. Spring samples showed increased effects only in acetylcholinesterase inhibition and estrogenic effects, probably due to phytoestrogens or algae bloom during vegetation period. Sites were ranked considering the total number of features, group proportions, seasonal variations and intensity and number of effects in abstraction wells. Oxic conditions and low initial component numbers in the river (Ruhr sites) resulted in less effects and fewer formations. Longer travel distances were important for a more efficient reduction of effects and features. Combination of non-target and effect-directed analysis proved to be valuable for a more comprehensive assessment of process performance beyond target analysis as also unknown OMP are observed with both methods. Image 1 • Combined non-target and effect-directed analysis valuable for process assessment. • Low component numbers in river lead to reduced effects in production wells. • First application of effect-directed analysis on riverbank filtration. [ABSTRACT FROM AUTHOR]

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. From a QA/QC perspective, we discuss methods used for each step of analysis: sample preparation, chromatography, mass spectrometry, and data processing. We then finish with a series of recommendations to improve the quality assurance of NTA workflows. • Non-target analysis needs specialised quality assurance approaches. • Current procedures focus mostly on specific parts of the workflow. • Loss of information (i.e. false negatives) occurs during all workflow steps. • Improvement of current quality assurance procedures is required. • Harmonization of the reporting practice of used approaches is essential. [ABSTRACT FROM AUTHOR]

• The honey components in different floral honeys were compared by their NMR spectra. • 8 classes monofloral honeys are distinguished by this new developed NMR-based method. • The geographical origins of two honeys are classified at provincial and town levels. In this paper, we report a newly developed non-target 1H NMR detection associated with chemometrics method to classify the botanical and geographical origins of the monofloral Chinese honey. 1H NMR tests of 218 monofloral honey samples of 8 classes (Acacia, Jujube, Linden, Longan, Orange, Rape, Sunflower, Vitex) collected in 2017–2019 across China were conducted under the optimal sample preparation conditions and NMR acquisition parameters. The whole profiles of NMR spectra instead of individual or partial signals from specific components were processed and extracted, then fed to SIMCA-P to classify the botanical and geographical origins through non-target statistical analysis. For the botanical origins, most of them could be classified clearly according to Principal Component Analysis (PCA) with both R2 and Q2 close to 1. Orthogonal Partial Least Squares Discrimination Analysis (OPLS-DA) model could classify the honey floral types successfully with R2Y and Q2 greater than 0.85. It is found that the integral bin for data extraction has no obvious influence on the classification. For the geographical origins, the classification at different geographical levels (providence and town) could be successfully distinguished by OPLS-DA model. The promising preliminary results with the geographical classification at 40 km level unambiguously demonstrate the application of this NMR-based multi-species non-targeted method for the honey authenticity. Successful result is obtained on a pilot prediction of the geographical classification. Comparing with the methods based on other techniques, the advantages of this reported one are less sample amount needed, simple preparation, short test time, and non-targeted multi-species detection. [ABSTRACT FROM AUTHOR]

Food science continually requires the development of novel analytical methods to prevent fraudulent actions and guarantee food authenticity. Greek table olives, one of the most emblematic and valuable Greek national products, are often subjected to economically motivated fraud. In this work, a novel ultra-high-performance liquid chromatography–quadrupole time of flight tandem mass spectrometry (UHPLC-QTOF-MS) analytical method was developed to detect the mislabeling of Greek PDO Kalamata table olives, and thereby establish their authenticity. A non-targeted screening workflow was applied, coupled to advanced chemometric techniques such as Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) in order to fingerprint and accurately discriminate PDO Greek Kalamata olives from Kalamata (or Kalamon) type olives from Egypt and Chile. The method performance was evaluated using a target set of phenolic compounds and several validation parameters were calculated. Overall, 65 table olive samples from Greece, Egypt, and Chile were analyzed and processed for the model development and its accuracy was validated. The robustness of the chemometric model was tested using 11 Greek Kalamon olive samples that were produced during the following crop year, 2018, and they were successfully classified as Greek Kalamon olives from Kalamata. Twenty-six characteristic authenticity markers were indicated to be responsible for the discrimination of Kalamon olives of different geographical origins. [ABSTRACT FROM AUTHOR]

The influence of pesticides on the metabolism of edible plants has not been fully investigated. Moreover, once introduced into the environment, pesticides are degraded to many compounds with undefined bioactivity. In presented work, under experimental conditions, model edible plant (Raphanus sativus var. longipinnatus) was exposed to herbicide stress by application of a herbicide (mesotrione, 2-(4-methanesulfonyl-2-nitrobenzoyl)cyclohexane-1,3-dione, MES) or its degradation products (amino-4-(methylsulfonyl)benzoic acid, AMBA; 4-(methylsulfonyl)-2-nitrobenzoic acid MNBA; cyclohexane-1,3-dione, CHD). Metabolic profiles of plants were employed to estimate the plant's defence response to MES and its metabolites. The intensity of herbicide stress was determined by measuring the changes in chlorophyll and catecholamines concentration formed in the shikimic acid pathway. Non-target analysis was conducted by LC-MS/MS, determination of catecholamines by LC-FL, chlorophyll by spectrophotometry. The highest phytotoxicity is characterized by MES (2000%-fold increase in the content of herbicide stress marker (normetanephrine) compared to a blank), followed by CHD (500%) combined with 15% increase in chlorophyll concentration. AMBA and MNBA as stress factors caused the increase in the content of catecholamines in the plant (86–160%). Simultaneously, an increase in chlorophyll content was observed (26–50%). Such diversity of the organism's defence response, also visible on metabolic profiles, can be associated with the chemical structure of compounds that are stress factors. MES and CHD, in contrast to AMBA and MNBA, have cyclohexano-1,3-moiety in their structure, which seems to be responsible for herbicidal properties. Image 1 • Mesotrione degradation products cause herbicide stress in the edible plant studied. • Metabolic fingerprint makes it possible to specify the stress factor. • Herbicide stress influences catecholamine biosynthesis in shikimic acid pathways. [ABSTRACT FROM AUTHOR]

The benchmarks to assess reproducibility are not well defined for non-target analysis. Parameters to evaluate analytical performance, such as accuracy, precision and selectivity, are well defined for target analysis, but remain elusive for non-target screening analysis. In this study, quality control (QC) guidelines are proposed to assure reliable data in non-target screening methodologies using a simple set of standards. Workflow reproducibility was assessed using an in-house QC mixture containing selected compounds with a wide range of polarity that can be detected either by electrospray ionization (ESI) in positive or negative mode. The analysis was performed by online solid phase extraction (SPE) liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS). Data processing was done by a commercially available software, Compound Discoverer v. 3.0 using an environmental working template, which searched a multitude of databases, including Chemspider, EPA Toxcast, MzCloud among others. We have specifically evaluated method specificity, precision, accuracy and reproducibility in terms of peak area and retention time variability, true positive identification rate, intraday (within days) and interday (consecutive days) variations and the use of QC samples to reduce false positives. The method showed a satisfactory accuracy with an identification rate of ≥70% for most of the QC compounds. Precision estimated based on peak area relative standard deviation (RSD) ranged between 30 and 50% for most of the compounds. Data normalization to a single internal standard did not improve peak area variability. Retention time precision showed great repeatability and reproducibility (RSD ≤ 5%). In addition, a simple model of RT vs log K ow was designed based on our QC mixtures to efficiently reduced false positives by an average of 49.1%. Unlabelled Image • Intraday and interday accuracy showed an identification rate of ≥70% for most compounds. • Intraday and interday peak area precision varied largely depending on the compounds. • Retention time precision showed great repeatability and reproducibility. • Data normalization to a single internal standard did not improve variability. • A simple model of RT vs log Kow efficiently reduced false positives. [ABSTRACT FROM AUTHOR]

The fate of malachite green and its main metabolite leucomalachite green during thermal treatment was examined in seafood (brook trout and white shrimp) using non-target analysis. Samples were extracted using QuEChERS and analyzed using liquid chromatography coupled with quadruple time of flight mass spectrometry. Malachite green levels were reduced in meat during boiling (∼40%), microwaving (64%), and canning (96%). Only microwaving was successful in significantly decreasing leucomalachite green levels in brook trout. The reduction percentages of the two target analytes were not significantly different in shrimp (mean fat content = 0.8 ± 0.3%) and in brook trout (mean fat content = 3.5 ± 1.7%), suggesting that a higher fat content may not affect the reduction of the more lipophilic leucomalachite green in these two matrices. Three transformation products were tentatively identified in the cooked tissues, resulting from the cleavage of the conjugated structure or through demethylation. Further research is needed to determine possible adverse health effects. The findings of this study show how non-target analysis can complement targeted methodologies in identifying and evaluating risks to human health., Competing Interests: The authors report no conflict of interest for the following manuscript submitted for your review (Original Article):, (© 2021 The Authors.)

Bioretention cells can effectively infiltrate stormwater runoff and partly remove conventional water contaminants. A field tracer injection experiment in a conventionally designed bioretention cell was used to investigate the fate of benzotriazole, a model trace organic contaminant, during and between runoff events. Moderate (29%) benzotriazole load reductions were measured during the 6 h long injection experiment. The detection of 1-methyl benzotriazole, hydroxy benzotriazole, and methoxy benzotriazole provided in situ evidence of some rapid benzotriazole microbial transformation during the tracer test and more importantly between the events. The detection of benzotriazole alanine and benzotriazole acetyl alanine also showed fast benzotriazole phytotransformation to amino acid conjugates during the tracer test and suggests further transformation of phytotransformation products between events. These data provide conclusive full-scale evidence of benzotriazole microbial and phytotransformation in bioretention cells. Non-target chemical analysis revealed the presence of a diverse range of trace organic contaminants in urban runoff and exiting the bioretention cell, including pesticides and industrial, household, and pharmaceutical compounds. We have demonstrated the in situ potential of urban green infrastructure such as bioretention cells to eliminate polar trace organic contaminants from stormwater. However, targeted design and operation strategies, for example, hydraulic control and the use of soil amendments, should be incorporated for improved bioretention cell performance for such compounds.

Hydrolysis characteristics of a novel 3,4-dichloroisothiazole based fungicide with activating plant defense responses as a candidate plant-activator LY5-24-2 were investigated under different conditions (pH and temperature) using ultra-performance liquid chromatography (UPLC) and quadrupole Time-of-Flight (Q-TOF). The hydrolysis case complied with the first-order kinetic model, with half-lives ranging from 4.8 h to 3.2 days at pH 4, 7, 9 and temperature at 25 and 50℃. One of the hydrolysis metabolite 3,4-dichloroisothiazole-5-carboxylic acid (metabolite 1, M1) was determined and quantified using authentic standard. The other hydrolysate 3-chloro-5-(trifluoromethyl) pyridin-2-amine (metabolite 2, M2) was determined and identified according to accurate mass information, fragmentation patterns and principle component analysis (PCA). By utilizing high-resolution mass spectrometry and multivariate statistical analysis, hydrolysis dynamic of the metabolites was characterized and figured out. This research provided a non-target screening method to analyze hydrolysis metabolites of a new plant-activator and to find its degradation products in aqueous solution.

Mass spectrometry based non-target analysis is increasingly adopted in environmental sciences to screen and identify numerous chemicals simultaneously in highly complex samples. However, current data processing software either lack functionality for environmental sciences, solve only part of the workflow, are not openly available and/or are restricted in input data formats. In this paper we present patRoon, a new R based open-source software platform, which provides comprehensive, fully tailored and straightforward non-target analysis workflows. This platform makes the use, evaluation and mixing of well-tested algorithms seamless by harmonizing various common (primarily open) software tools under a consistent interface. In addition, patRoon offers various functionality and strategies to simplify and perform automated processing of complex (environmental) data effectively. patRoon implements several effective optimization strategies to significantly reduce computational times. The ability of patRoon to perform time-efficient and automated non-target data annotation of environmental samples is demonstrated with a simple and reproducible workflow using open-access data of spiked samples from a drinking water treatment plant study. In addition, the ability to easily use, combine and evaluate different algorithms was demonstrated for three commonly used feature finding algorithms. This article, combined with already published works, demonstrate that patRoon helps make comprehensive (environmental) non-target analysis readily accessible to a wider community of researchers.

Artificial insemination is common practice in mass livestock farming. Recently, it was shown that chemicals leaching from multilayer plastic bags affect the fertility of boars, although common quality tests did not show any impact on the sperm. It is not clear whether this incidence was a single case or whether it could be a systematic problem. Therefore, we studied six multilayer plastic bags. A total of 49 compounds were found, but most of them were at very low intensity. Nonylphenols in the range of 19-99 μg/g plastic were found. Migration tests using water and 10% ethanol as simulants, to mimic the behavior of semen with the extender, were performed. The most interesting migrants in terms of potential reprotoxicity were identified as nonylphenols. The identification in depth demonstrated the presence of 10 isomers of nonylphenol with a total concentration range between 16 to 58 μg/Kg simulant, among other migrants at lower concentration. The influence of these nonylphenols and their maximum tolerable concentration in direct contact with semen from boars was studied. Motility, viability, mitochondrial activity and acrosomes reacted were significantly affected at 10 mg/Kg of nonylphenols in contact with the sperm, but in vitro penetration rate was significantly decreased with only 2 mg/Kg. Insight into the mode of action is also provided.

A non-target screening strategy was developed for the safety evaluation of potentially hazardous chemicals in paper food contact materials (FCMs). A tentative list of suspect analytes was generated using publicly available FCM substance inventories, the presence of contaminants in paper straws was confirmed by high-resolution Orbitrap mass spectrometry. Data-independent and data-dependent MS and MS/MS results for candidate compounds were processed using a workflow including peak detection by deconvolution, blank subtraction, retention time alignment, formula assignment and fragmentation spectra search against spectral libraries followed by in silico generated spectra annotation. This workflow allowed for the identification of 74 suspect compounds, of which 40 were assigned a high confidence level of detection. A tentative in silico toxicity evaluation for mutagenic and carcinogenic activities was carried out. Using quantitative structure-activity relationship (QSAR) models it was found that two of the detected compounds tested positive for mutagenicity and three for carcinogenicity.

Laboratory experiments investigating the biotransformation of per- and polyfluoroalkyl substances (PFASs) require unique analytical considerations due to the physicochemical properties of highly fluorinated molecules. Here, we review experiments performed using microbial systems, in vivo animal models or in vitro systems that search for novel transformation products. Researchers have made analytical decisions regarding volatility, polarity, purity and isomeric profiles of test compounds in the design of experiments, sampling procedures and analytical measurements. Identification of transformation products has been predominantly performed using ultra high-performance liquid chromatography- or gas chromatography-mass spectrometry, 19F nuclear magnetic resonance and ion chromatography. In this review we will discuss attempts to close the mass balance in these experiments, and how future experiments can improve the comprehensive understanding of PFAS biotransformation. • PFASs have unique physicochemical properties compared to their hydrogenated counterparts. • Lab studies of PFAS degradation include: microbial systems, in vivo animal models or in vitro systems. • Experimental setup and sampling must be optimized for the PFASs studied and hypothesized metabolites. • Novel metabolites are identified by mass spectrometry or NMR. • Mass balance analysis should be performed for all studies. [ABSTRACT FROM AUTHOR]

Industrial wastewater is characterised by a complex composition of trace organic compounds (TrOC) in a difficult matrix. The identification of unknown pollutants is of high interest. On the one hand to ensure protection of the environment by elucidating contaminations and on the other hand to protect the biological treatment step in the wastewater treatment plant (WWTP). Due to the high variability of the matrix, the identification of compounds of interest is very time consuming and often unsuccessful. To overcome this limitation, a prioritisation method was developed to identify so called ' known unknowns ', i.e. compounds frequently detected but not identified, as prioritised compounds in industrial wastewater. The method based on an offline two-dimensional (offline 2D) liquid chromatography (LC) approach with ultra violet (UV) detection in the first and high-resolution mass spectrometry (HRMS) in the second dimension. As a proof of concept, an identification process of one ' known unknown ' is described. The compound was identified as a dichlorodinitrophenol isomer by retention time in two dimensions, UV spectrum, exact mass, mass fragmentation and 1H- NMR. As prioritisation method, the offline 2D LC in combination with non-target analysis provides a powerful workflow to determine tentative structures of unknown organic compounds in industrial wastewater. Unlabelled Image • Frequently observed contaminants named ' known unknowns ' serve as prioritised compounds. • 2D offline LC-UV/LC-HRMS was used for identification. • 1st dimension provides clean-up of interfering signals and UV absorption. • 2nd dimension provides HRMS for elucidation of unknown wastewater compounds. [ABSTRACT FROM AUTHOR]

Per- and polyfluoroalkyl substances (PFASs) are a group of anthropogenic organic fluorinated compounds that have been detected widely. To discover unknown PFASs, previous researchers have applied high-resolution mass spectrometer using fragmentation flagging approach with common fragment ion at the same retention time as the flags. However, it was difficult to search for their molecular ion from co-eluting peaks in the full-scan spectrum at a specific retention time. Here, we attempted to utilize drift time acquired by ion mobility spectrometry for making linkages between fragment ions and their molecular ions. For validating the process, standard solution spiked with PFASs were analyzed by liquid chromatography/ion mobility – quadrupole time-of-flight mass spectrometry (LC/IM-QTOF-MS). Fluorinated fragment ions (fragmentation flags) were categorized into three classes: Class 1 (120 types of [C x F y ]−), Class 2 (123 types of [C x F y O]−), Class 3 (131 types of [C x F y O 3 S]−) and all overlapping fragmentation flags detected at an identical retention time were bundled together as a "flag set". Injecting standard mixed solution of 20 types of PFASs resulted in picking up 20 flag sets by fragmentation flagging. All the fragmentation flags were detected within a designated range of drift time, and their molecular ion was confirmed as a PFAS spiked in the standard solution even when co-eluting compounds were found at almost same retention time. This method was applied to a household fire extinguisher liquid, resulting in finding out nine molecular ions. Therefore, the new linking method achieved rapid searching for the prospective molecular ions using LC/IM-QTOF-MS. Image 1 • High resolution ion mobility mass spectrometry was used to search for unknown PFASs. • Fluorinated fragment ions as fragmentation flags were categorized into 3 classes. • Linking fragmentation flags with their molecular ions by drift time was confirmed. • The method achieved in identifying 20 suspected molecular ions with spiked 20 PFASs. • Nine molecular ions were observed from a household fire extinguisher liquid. [ABSTRACT FROM AUTHOR]

• Deep learning was applied to prediction of gas chromatographic retention indices. • Huge retention data sets are considered. • The developed model outperforms existing retention prediction models. • Median percentage error <2.2% for non-polar (polysiloxanes) stationary phases. • Usability of the model for mass spectral identification is demonstrated. A deep convolutional neural network was used for the estimation of gas chromatographic retention indices on non-polar (polydimethylsiloxane and polydimethyl(5%-phenyl) siloxane) stationary phases. The neural network can be used for candidate ranking while searching a mass spectral database. A linear representation (SMILES notation) of the molecule structure was used as an input for the model. The input line was converted to a one-hot matrix and then directly processed by the neural network. The calculation of any common molecular descriptors is avoided, following the modern tendency in machine learning: to allow the neural network to find the most preferable features by itself instead of using hard-coded features. The model has two 1D-convolutional layers with 120 neurons each followed by a pooling layer and a fully-connected layer with 200 hidden neurons. The model was compared with state-of-the-art models for prediction of gas chromatographic indices based on molecular descriptors and on functional groups contributions. On different data sets better accuracy is shown together with greater versatility. The applicability to diverse sets of flavors and fragrances, essential oils, metabolites is shown. The possibility of using the model for improvement of mass spectral identification (without reference retention index) is demonstrated. The median absolute error and the median percentage error are in the range of 17.3 (0.93%) to 38.1 (2.15%) depending on used test data set. Ready-to-use neural network parameters are provided. [ABSTRACT FROM AUTHOR]

Owing to biotransformation, xenobiotics are often found in conjugated form in biological samples such as urine and plasma. Liquid chromatography coupled with accurate mass spectrometry with multistage collision-induced dissociation provides spectral information concerning these metabolites in complex materials. Unfortunately, compound databases typically do not contain a sufficient number of records for such conjugates. We report here on the development of a novel protocol, referred to as ChemProphet, to annotate compounds, including conjugates, using compound databases such as PubChem and ChemSpider. The annotation of conjugates involves three steps: 1. Recognition of the type and number of conjugates in the sample; 2. Compound search and annotation of the deconjugated form; and 3. In silico evaluation of the candidate conjugate. ChemProphet assigns a spectrum to each candidate by automatically exploring the substructures corresponding to the observed product ion spectrum. When finished, it annotates the candidates assigning a rank for each candidate based on the calculated score that ranks its relative likelihood. We assessed our protocol by annotating a benchmark dataset by including the product ion spectra for 102 compounds, annotating the commercially available standard for quercetin 3-glucuronide, and by conducting a model experiment using urine from mice that had been administered a green tea extract. The results show that by using the ChemProphet approach, it is possible to annotate not only the deconjugated molecules but also the conjugated molecules using an automatic interpretation method based on deconjugation that involves multistage collision-induced dissociation and in silico calculated conjugation.

The techniques and measurement methods developed in the Environmental Survey and Monitoring of Chemicals by Japan's Ministry of the Environment, as well as a large amount of knowledge archived in the survey, have led to the advancement of environmental analysis. Recently, technologies such as non-target liquid chromatography/high resolution mass spectrometry and liquid chromatography with micro bore column have further developed the field. Here, the general strategy of a method developed for the liquid chromatography/mass spectrometry (LC/MS) analysis of environmental chemicals with a brief description is presented. Also, a non-target analysis for the identification of environmental pollutants using a provisional fragment database and "MsMsFilter," an elemental composition elucidation tool, is presented. This analytical method is shown to be highly effective in the identification of a model chemical, the pesticide Bendiocarb. Our improved micro-liquid chromatography injection system showed substantially enhanced sensitivity to perfluoroalkyl substances, with peak areas 32-71 times larger than those observed in conventional LC/MS.