Library Matching Research Articles

Metabolomics of the wild mushroom Gymnopilus imperialis (Agaricomycetes, Basidiomycota) by UHPLC-HRMS/MS analysis and molecular network

Gymnopilus consists in a widely distributed genus of mushroom-forming fungi, especially in tropical regions of the world. Literature on Gymnopilus representatives reports the presence of oligoisoprenoids, and styrylpyrones. Considering the large number of secondary metabolites that basidiomycetes might contain, dereplication tools such as GNPS (Global Natural Products Social Molecular Networking), has become important in prospecting metabolites, saving time and work on isolation and characterization of natural products. Thus, this work identified the wild mushroom Gymnopilus imperialis and dereplicated their extracts with the aid of GNPS to annotate oligoisoprenoids. It was possible to annotate 24 oligoisoprenoids from methanol, dichloromethaneand ethyl acetate extracts of G. imperialis, 4 of them from GNPS spectral library match, and 20 from prediction based on molecular network. Moreover HRMS-ESI-(+) dereplication of the acetate extract annotated bisnoryangonin and hispidin. To the best of our knowledge, this is the first report on the annotation of a series of gymnopilins analogues based on GNPS molecular network. Our findings suggest that GNPS might be an effective, rapid, and open-source device to identify compounds and predict analogues.

Machine learning model for detecting fentanyl analogs from mass spectra

In recent years, fentanyl and its analogs have been increasingly abused, leading to tragic outcomes. One way to tackle this problem is to rapidly detect fentanyl analog compounds and prevent them from reaching people who could be harmed by them. However, the emergence of novel fentanyl analogs that evade detection by classic mass spectral library matching has exacerbated the problem. We propose supervised machine learning classification models as a complementary approach to library matching for detecting fentanyl analogs from mass spectra. To develop and apply such models, we extract two dozen peak-based and similarity-based input features from each spectrum of interest. Using techniques such as random forests, neural networks, and logistic regression, we identify patterns within these features’ values, resulting in strong detection performance. Within a cross-validation framework, we achieve 99% probability of detection and 1% probability of false alarm on a representative set of several thousand mass spectra. These results suggest that machine learning models may offer a robust complement to library matching. Practitioners from diverse fields, including border security, law enforcement, and military may benefit from this capability to detect drugs of abuse.

Low Cell Number Proteomic Analysis Using In-Cell Protease Digests Reveals a Robust Signature for Cell Cycle State Classification

Comprehensive proteome analysis of rare cell phenotypes remains a significant challenge. We report a method for low cell number MS-based proteomics using protease digestion of mildly formaldehyde-fixed cells in cellulo, which we call the “in-cell digest.” We combined this with averaged MS1 precursor library matching to quantitatively characterize proteomes from low cell numbers of human lymphoblasts. About 4500 proteins were detected from 2000 cells, and 2500 proteins were quantitated from 200 lymphoblasts. The ease of sample processing and high sensitivity makes this method exceptionally suited for the proteomic analysis of rare cell states, including immune cell subsets and cell cycle subphases. To demonstrate the method, we characterized the proteome changes across 16 cell cycle states (CCSs) isolated from an asynchronous TK6 cells, avoiding synchronization. States included late mitotic cells present at extremely low frequency. We identified 119 pseudoperiodic proteins that vary across the cell cycle. Clustering of the pseudoperiodic proteins showed abundance patterns consistent with “waves” of protein degradation in late S, at the G2&M border, midmitosis, and at mitotic exit. These clusters were distinguished by significant differences in predicted nuclear localization and interaction with the anaphase-promoting complex/cyclosome. The dataset also identifies putative anaphase-promoting complex/cyclosome substrates in mitosis and the temporal order in which they are targeted for degradation. We demonstrate that a protein signature made of these 119 high-confidence cell cycle–regulated proteins can be used to perform unbiased classification of proteomes into CCSs. We applied this signature to 296 proteomes that encompass a range of quantitation methods, cell types, and experimental conditions. The analysis confidently assigns a CCS for 49 proteomes, including correct classification for proteomes from synchronized cells. We anticipate that this robust cell cycle protein signature will be crucial for classifying cell states in single-cell proteomes.

Automated high confidence compound identification of electron ionization mass spectra for nontargeted analysis

Nontargeted analysis based on mass spectrometry is a rising practice in environmental monitoring for identifying contaminants of emerging concern. Nontargeted analysis performed using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC/TOF-MS) generates large numbers of possible analytes. Moreover, the default spectral library similarity score-based search algorithm used by LECO® ChromaTOF® does not ensure that high similarity scores result in correct library matches. Therefore, an additional manual screening is necessary, but leads to human errors especially when dealing with large amounts of data. To improve the speed and accuracy of the chemical identification, we developed CINeMA.py (Classification Is Never Manual Again). This programming suite automates GC×GC/TOF-MS data interpretation by determining the confidence of a match between the observed analyte mass spectrum and the LECO® ChromaTOF® software generated library hit from the NIST Electron Ionization Mass Spectral (NIST EI-MS) library. Our script allows the user to evaluate the confidence of the match using an algorithmic method that mimics the manual curation process and two different machine learning approaches (neural networks and random forest). The script allows the user to adjust various parameters (e.g., similarity threshold) and study their effects on prediction accuracy. To test CINeMA.py, we used data from two different environmental contaminant studies: an EPA study on household dust and a study on stormwater runoff. Using a reference set based on the analysis performed by highly trained users of the ChromaTOF and GC×GC/TOF-MS systems, the random forest model had the highest prediction accuracies of 86% and 83% on the EPA and Stormwater data sets, respectively. The algorithmic approach had the second-best prediction accuracy (82% and 79%), while the neural network accuracy had the lowest (63% and 67%). All the approaches required less than 1 min to classify 986 observed analytes, whereas manual data analysis required hours or days to complete. Our methods were also able to detect high confidence matches missed during the manual review. Overall, CINeMA.py provides users with a powerful suite of tools that should significantly speed-up data analysis while reducing the possibilities of manual errors and discrepancies among users, and can be applicable to other GC/EI-MS instrument based nontargeted analysis.

MS2DeepScore: a novel deep learning similarity measure to compare tandem mass spectra

Mass spectrometry data is one of the key sources of information in many workflows in medicine and across the life sciences. Mass fragmentation spectra are generally considered to be characteristic signatures of the chemical compound they originate from, yet the chemical structure itself usually cannot be easily deduced from the spectrum. Often, spectral similarity measures are used as a proxy for structural similarity but this approach is strongly limited by a generally poor correlation between both metrics. Here, we propose MS2DeepScore: a novel Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra. Using a cleaned dataset of > 100,000 mass spectra of about 15,000 unique known compounds, we trained MS2DeepScore to predict structural similarity scores for spectrum pairs with high accuracy. In addition, sampling different model varieties through Monte-Carlo Dropout is used to further improve the predictions and assess the model’s prediction uncertainty. On 3600 spectra of 500 unseen compounds, MS2DeepScore is able to identify highly-reliable structural matches and to predict Tanimoto scores for pairs of molecules based on their fragment spectra with a root mean squared error of about 0.15. Furthermore, the prediction uncertainty estimate can be used to select a subset of predictions with a root mean squared error of about 0.1. Furthermore, we demonstrate that MS2DeepScore outperforms classical spectral similarity measures in retrieving chemically related compound pairs from large mass spectral datasets, thereby illustrating its potential for spectral library matching. Finally, MS2DeepScore can also be used to create chemically meaningful mass spectral embeddings that could be used to cluster large numbers of spectra. Added to the recently introduced unsupervised Spec2Vec metric, we believe that machine learning-supported mass spectral similarity measures have great potential for a range of metabolomics data processing pipelines.

Expanding Urinary Metabolite Annotation through Integrated Mass Spectral Similarity Networking

The urine metabolome constitutes a rich source of functional information reflecting physiological states that are influenced by distinct conditions and biological stresses, such as responses to drug treatments or disease manifestations. Although global liquid chromatography-mass spectrometry (MS) profiling provides the most comprehensive measurement of metabolites in complex biological samples, annotation remains a challenge, and computational approaches are necessary to translate the molecular composition into biological knowledge. Here, we investigated the use of tandem MS-based enhanced molecular networks (MolNetEnhancer) to improve the metabolite annotation of urine extracts. The samples (n = 10) were analyzed by hydrophilic interaction chromatography-quadrupole time-of-flight mass spectrometry in both electrospray ionization (ESI) modes. Consistent with other common data preprocessing software, the use of Progenesis QI led to the annotation of up to 20 metabolites based on MS2 library searches, showing a high fragmentation score (cosine similarity ≥ 0.7), that is, ∼2% of mass features containing MS2 spectra. Molecular networking based on library matching resulted in the annotation of up to 62 urinary compounds. Using a combination of unsupervised substructure discovery (MS2LDA), the in silico tool network annotation propagation (NAP), and ClassyFire chemical ontology, embedded in a multilayered molecular network by MolNetEnhancer, we were able to expand the chemical characterization to ∼50% of the data set. The integrative approach led to the annotation of 275 compounds at the metabolomics standards initiative (MSI) confidence level 2, as well as 459 and 578 urinary metabolites (MSI level 3) in both negative and positive ESI modes, respectively. The exhaustive MS2-based annotation outperformed similar studies applied to larger cohorts while offering the discovery of metabolites not identified by the MS2 library search. This is the first work that effectively integrates orthogonal annotation methods and MS2-based fragmentation studies to improve metabolite annotation in urine samples.

A New Library-Search Algorithm for Mixture Analysis Using DART-MS.

Forensic analysis of seized drug evidence often involves determining whether the components of an unknown mixture are illicit compounds. One approach to this task is to screen the evidence using direct analysis in real time mass spectrometry (DART-MS) to make presumptive identifications. This manuscript introduces a new library-search algorithm that enhances presumptive identifications of mixture components using a series of in-source collision-induced dissociation mass spectra collected through DART-MS. The multistage search, titled the Inverted Library-Search Algorithm (ILSA), identifies potential components in a mixture by first searching the lowest fragmentation mass spectrum for target peaks, assuming these peaks are protonated molecules, and then scoring each target peak with possible library matches. As a proof of concept, the ILSA is demonstrated through several example searches of model seized drug mixtures of acetyl fentanyl, benzyl fentanyl, amphetamine, and methamphetamine searched against a small library of select compounds and the freely available NIST DART-MS Forensics Database. Discussion of the search results and several open areas of research to further extend the method are provided. This new approach for presumptive identification provides analysts with refined information about mixture components and will be of immediate importance in forensic analysis using DART-MS. A prototype implementation of the ILSA is available at https://github.com/asm3-nist/DART-MS-DST.

Determinant in russian language

Adverbs in language isone of the important issues that linguists have considered. Russian as one of the branches of the Indo-European languages is no exception. A special interest in this category, in Russian language, was prompted after Shviedova's article on determinant, which caused a lot of disagreements and controversies. In this paper, using a library resource analysis method and matching samples taken from resources, a new categorization of determinant is proposed emphasizing on whether the adverb binds up the verb or the sentence. To this end, we first refer to Shviedova's view on the division of the internal elements of sentence and, using Shviedova’s definitions in distinguishing verb adverb and determinant in Russian, some semantic and syntactic criteria are introduced. The examination of the samples taken from the sources show that the determinant is semantically divided into two categories of subject-object determinant and adverb complement determinant. Moreover lexical forms, subordinate clauses that are identified as analyzable sentences, can play the role of determinant since they do not depend on the main clauses.

Qualitative and quantitative analysis of mixtures of microplastics in the presence of calcium carbonate by pyrolysis-GC/MS

Environmental pollution by microplastics (MPs) has attracted much attention due to possible risks of MPs to human health, and fast and reliable analytical methods are required for identification and quantification of MPs in various matrices. Pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) has advantageous characteristics in that both identification and mass quantification can be easily done by chromatographic separation in combination with mass spectral analysis. Environmental MP samples usually contain several types of polymers and the pyrolysis of mixed polymer particles may cause a secondary reaction between pyrolyzates from different polymers. In this study, effect of the usage of calcium carbonate (CaCO3) as a catalyst on pyrolytic behavior of polymers was examined to obtain catalytic conversion of reactive pyrolyzates to stable compounds. CaCO3 was also utilized as a diluent for insoluble PE and PP powders to assure weighing easiness by forming a homogenous mixture. First, a reference material was prepared to make calibration curves by mixing twelve different types of standard polymers, selected to reflect common polymers in the global production. Identification and quantification of polymers in mixed polymer samples were validated by using model polymer mixtures, after storing Py-GC/MS data of twelve polymers with a given sample amount in a software named as F-Search MPs. Results exceeding the limit of quantitation combined with the high probability of a library match as confirmed by the software, provided assurance of the presence of the identified polymer in the model polymer mixtures. The quantitated values for the model polymer mixtures were compared with the compounded amount. The recovery varied depending on the polymer type, and especially short recovery (ca. 60–70 %) was recognized for PC in the sample from Solution A (SA) and excess recovery (ca. 175 %) for PET in the sample from Solution B (SB). Finally, the developed method was applied to the analysis of ground mixtures of isolated environmental MP particle samples and quick identification and quantification of each polymer in the MP samples were attained.

Discrimination of beef from different origins based on lipidomics: A comparison study of DART-QTOF and LC-ESI-QTOF

The comparison of DART-QTOF and LC-ESI-QTOF was studied on the discrimination of beef from different origins based on lipidomics. Compared to LC-ESI-QTOF (+), DART-QTOF (+) is particularly suitable for smaller compounds analysis, and has better repeatability and stability. The data were screened based on detection rate, RSD value, fold change and P value, and library matching to obtain 852 and 879 raw peaks, 62 and 165 differential peaks and 17 and 25 potential markers in DART-QTOF (+) and LC-ESI-QTOF (+), respectively. Then, support vector machine (SVM) was constructed based on them and LC-ESI-QTOF (+) was superior to DART-QTOF (+) in SVM models, achieving a highest training accuracy of 100% and a validation accuracy of 89.91%. Based on LC-ESI-QTOF (+) and LC-ESI-QTOF (−), 40 potential markers were found, and beef samples from Canada, Argentina and New Zealand shared higher similarity, while beef samples from Brazil, Australia and Uruguay had more similarities. Considering the high efficiency, repeatability and stability of DART-QTOF acquisition, it is necessary to optimize its data acquisition and processing methods, in order to realize the faster assessment for lipidomics as well as food authenticity in the future.

Development and validation of a screening method for simultaneous detection of KBrO3 and KIO3 in baking ingredients and additives using powder XRD

Potassium bromate (KBrO3) and potassium iodate (KIO3) are significant adulterants in the baking industry. Over the years, with growing concerns about their toxic effects, several detection techniques have been explored. The objective of this study was to develop a rapid, non-destructive technique for the determination of these adulterants in bakery ingredients and products. For this purpose, powder x-ray diffraction (PXRD) conditions were varied, and the method for concurrent detection of these adulterants was optimized. X-ray tube combinations of current (25–45 mA), voltage (25–45 kV), and scanning parameters like step size (0.006° to 0.135) and time per step (50–150 s) were experimented on 10−90° angle with Cu Kα energy (λ =1.54060 Å) at 25 °C. The identification and quantification of these phases were performed using the Rietveld method, simultaneously detecting them based on the 2θ of peak position and miller indices (hkl) values. Crystalline phases were confirmed using the search library match databases ICDD [PDF-4]/COD. Depending on the observation, the 45 mA - 45 kV - 151.210 s per step - 0.006 Å step size combination was found to be optimal. The developed PXRD method was applied to identify the KBrO3 and KIO3 phases in real samples, providing new insights on the scope of this novel approach for food adulterant detection applications. This report details the first-of-its-kind PXRD methodology for simultaneous detection of these common adulterants in bakery ingredients and additives.

In-vitro Cytotoxic activity of Papaverine compound isolated from Aqueous Ethanolic leaf extract of Erythrina subumbrans (Hassk.) Merr.

Natural compounds that occur either in pure form or as extract after standardization from medicinal plants, provide a limitless chance for new discovery leads because of consummate chemical diversity. The rising demand for compounds that occur naturally during screening studies, that has therapeutic action predominantly on the vegetative part of the plant has grown across the world. In this study, Papaverine compound was isolated from aqueous Ethanolic extract (80%) from vegetative (leaf) part of herbal plant Erythrina subumbrans (Hassk.) Merr. Isolated compound was subjected for characterization studies like Thin layer chromatography (TLC), Mass spectrophotometry (MS), Fourier Transform Infra-Red (FTIR), 1H and 13C Nuclear magnetic resonance spectroscopy. Confirmatory study using the aqueous ethanolic crude extract by MS technique and mass library match confirms the presence of Papaverine compound. Further, in-vitro cytotoxic activity using Human Melanoma cell line (A375) was carried out using the isolated compound (papaverine), Aqueous ethanolic extract and standard Cisplatin. Six different Concentration ranging from 3.125 to 100 µg/ml were prepared. Control samples were prepared without standard and samples solution. Absorbance measured at 570 nm in UV-Vis spectrophotometer using a microplate reader and the % of viability was calculated. IC50 value of Aqueous ethanolic extract of Erythrina subumbrans, isolated compound-Papaverine and standard Cisplatin exhibits value of 82.14µg, 23.43 µg & 4.02 µg respectively.

Future feed control – Tracing banned bovine material in insect meal

In the present study, we assessed if different legacy and novel molecular analyses approaches can detect and trace prohibited bovine material in insects reared to produce processed animal protein (PAP). Newly hatched black soldier fly (BSF) larvae were fed one of the four diets for seven days; a control feeding medium (Ctl), control feed spiked with bovine hemoglobin powder (BvHb) at 1% (wet weight, w/w) (BvHb 1%, w/w), 5% (BvHb 5%, w/w) and 10% (BvHb 10%, w/w). Another dietary group of BSF larvae, namely *BvHb 10%, was first grown on BvHb 10% (w/w), and after seven days separated from the residual material and placed in another container with control diet for seven additional days. Presence of ruminant material in insect feed and in BSF larvae was assessed in five different laboratories using (i) real time-PCR analysis, (ii) multi-target ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS), (iii) protein-centric immunoaffinity-LC-MS/MS, (iv) peptide-centric immunoaffinity-LC-MS/MS, (v) tandem mass spectral library matching (SLM), and (vi) compound specific amino acid analysis (CSIA). All methods investigated detected ruminant DNA or BvHb in specific insect feed media and in BSF larvae, respectively. However, each method assessed, displayed distinct shortcomings, which precluded detection of prohibited material versus non-prohibited ruminant material in some instances. Taken together, these findings indicate that detection of prohibited material in the insect-PAP feed chain requires a tiered combined use of complementary molecular analysis approaches. We therefore advocate the use of a combined multi-tier molecular analysis suite for the detection, differentiation and tracing of prohibited material in insect-PAP based feed chains and endorse ongoing efforts to extend the currently available battery of PAP detection approaches with MS based techniques and possibly δ13CAA fingerprinting.

Non-target screening of organic compounds in offshore produced water by GC×GC-MS

Produced water is the largest by-product of oil and gas production. At off-shore installations, the produced water is typically reinjected or discharged into the sea. The water contains a complex mixture of dispersed and dissolved oil, solids and inorganic ions. A better understanding of its composition is fundamental to (1) improve environmental impact assessment tools and (2) develop more efficient water treatment technologies. The objective of the study was to screen produced water sampled from a producing field in the Danish region of the North Sea to identify any containing organic compounds. The samples were taken at a test separator and represent an unfiltered picture of the composition before cleaning procedures. The analytes were isolated by liquid-liquid extraction and derivatized using a silylation reagent to increase the volatility of oxygenated compounds. The final extracts were analyzed by comprehensive multi-dimensional gas chromatography coupled to a high-resolution mass spectrometer. A non-target processing workflow was implemented to extract features and quantify the confidence of library matches by correlation to retention indices and the presence of molecular ions. Approximately 120 unique compounds were identified across nine samples. Of those, 15 were present in all samples. The main types of compounds are aliphatic and aromatic carboxylic acids with a small fraction of hydrocarbons. The findings have implications for developing improved environmental impact assessment tools and water remediation technologies.

The β-carotene–oxygen copolymer: its relationship to apocarotenoids and β-carotene function

β-carotene spontaneously copolymerizes with molecular oxygen to form a β-carotene–oxygen copolymer compound (“copolymer”) as the main product, together with small amounts of many apocarotenoids. Both the addition and scission products are interpreted as being formed during progression through successive free radical β-carotene–oxygen adduct intermediates. The product mixture from full oxidation of β-carotene, lacking both vitamin A and β-carotene, has immunological activities, some of which are derived from the copolymer. However, the copolymer’s chemical makeup is unknown. A chemical breakdown study shows the compound to be moderately stable but nevertheless the latent source of many small apocarotenoids. GC–MS analysis with mass-spectral library matching identified a minimum of 45 structures, while more than 90 others remain unassigned. Newly identified products include various small keto carboxylic acids and dicarboxylic acids, several of which are central metabolic intermediates. Also present are glyoxal and methyl glyoxal dialdehydes, recently reported as β-carotene metabolites in plants. Although both compounds at higher concentrations are known to be toxic, at low concentration, methyl glyoxal has been reported to be potentially capable of activating an immune response against microbial infection. In plants, advantage is taken of the electrophilic reactivity of specific apocarotenoids derived from β-carotene oxidation to activate protective defenses. Given the copolymer occurs naturally and is a major product of non-enzymatic β-carotene oxidation in stored plants, by partially sequestering apocarotenoid metabolites, the copolymer may serve to limit potential toxicity and maintain low cellular apocarotenoid concentrations for signaling purposes. In animals, the copolymer may serve as a systemic source of apocarotenoids.

Spotting isomer mixtures in forensic illicit drug casework with GC–VUV using automated coelution detection and spectral deconvolution

Analysis of isomeric mixtures is a significant analytical challenge. In the forensic field, for example, over 1000 new psychoactive substances (NPSs), comprising of many closely related and often isomeric varieties, entered the drugs-of-abuse market within the last decade. Unambiguous identification of the isomeric form requires advanced spectroscopic techniques, such as GC-Vacuum Ultraviolet Spectroscopy (GC-VUV). The continuous development of NPSs makes the appearance of a novel compound in case samples a realistic scenario. While several analytical solutions have been presented recently to confidently distinguish NPS isomers, the presence of multiple isomers in a single drug sample is typically not considered. Due to their structural similarities it is possible that a novel NPS coelutes with a known isomer and thus remains undetected. This study investigates the capabilities of VUV spectral deconvolution for peak detection and identification in incompletely resolved drug mixtures. To mimic worst case scenarios, severe coelution was deliberately induced at elevated GC temperatures. The deconvolution software was nevertheless able to correctly detect both substances, even in case of near-identical VUV spectra at almost full coelution. As a next step, spectra were subsequently removed from the reference library to simulate the scenario in which a novel substance was encountered for the first time in forensic case work. However, also in this situation the deconvolution software still detected the coelution. This work shows that a VUV library match score below 0.998 may serve as a warning that a novel substance may be present in a street sample.

Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships.

Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm—Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds.

On Selecting a Suitable Spectral Matching Method for Automated Analytical Applications of Raman Spectroscopy.

Raman spectra are molecular structure-specific and hence are employed in applications requiring chemical identification. The advent of efficient handheld and smartphone-based Raman instruments is promoting widespread applications of the technique, which often involve less trained end users. Software modules that enable spectral library searches based on spectral pattern matching is an essential part of such applications. The Raman spectrum recorded by end users will naturally have varying levels of signal to noise (SN), baseline fluctuations, etc., depending on the sample environment. Further, in biological, forensic, food, pharmaceuticals, etc., fields where a vast amount of Raman spectral data is generated, careful removal of background is often impossible. In other words, a 100% match between the library spectrum and user input cannot be often guaranteed or expected. Often, such influences are discounted upon developing mathematical methods for general applications. In this manuscript, we carefully examine how such effects would determine the results of spectral similarity-based library search. We show that several popular mathematical spectral matching approaches give incorrect results under the influence of small changes in the baseline and/or the noise. We also discuss the points to be carefully considered while generating a spectral library. We believe our results will be a guiding note for developing applications of Raman spectroscopy that uses a standard spectral library and mathematical spectral matching.

A multi-location peak parking approach for calculation of second dimensional retention indices for improved volatile compound identification with cryogen-free comprehensive heart-cut two-dimensional gas chromatography.

Comprehensive heart-cut multidimensional gas chromatography (CH/C MDGC) without a cryogenic trapping device was developed with an established approach for calculation of first and second dimensional retention indices (1I and 2I) for improved compound identification. A first dimensional (1D) DB-1MS column (60 m) and a second dimensional (2D) DB-WAX column (60 m) were applied with a Deans switch (DS) using a constant H/C window of 0.2 min and a periodic multiple heartcut strategy comprising 225H/C throughout the CH/C. 1I was calculated based on comparison of the middle of the heartcut time with the alkane retention times on the 1D column. A multi-location peak parking approach using sixteen sets of automated injections of alkane references was also established with the least square curve fitting method for construction of the alkane isovolatility curves which were applied for 2I calculation. The untargeted compound analysis of a perfume sample was then performed according to comparison with the libraries of mass spectra, 1I and 2I. The CH/C MDGC system with a 25 h analysis time showed a peak capacity (nc) of 9198 and 128 separated peaks with 71 compounds successfully identified according to MS, 1I and 2I library match under the established error approximation criteria. Furthermore, relationship between the analysis time and number of separated peaks was proposed based on the set of 84 identifiable compounds. With the compensation of lower separation performance and greater I errors, the analysis time could be reduced by applying a 2.5 min H/C window with a total analysis time of 2 h and nc of 1134.

Assessment of emerging polar organic pollutants linked to contaminant pathways within an urban estuary using non-targeted analysis.

A comprehensive, non-targeted analysis of polar organic pollutants using high resolution/accurate mass (HR/AM) mass spectrometry approaches has been applied to water samples from San Francisco (SF) Bay, a major urban estuary on the western coast of the United States, to assess occurrence of emerging contaminants and inform future monitoring and management activities. Polar Organic Chemical Integrative Samplers (POCIS) were deployed selectively to evaluate the influence of three contaminant pathways: urban stormwater runoff (San Leandro Bay), wastewater effluent (Coyote Creek, Lower South Bay), and agricultural runoff (Napa River). Grab samples were collected before and after deployment of the passive samplers to provide a quantitative snapshot of contaminants for comparison. Composite samples of wastewater effluent (24 hours) were also collected from several wastewater dischargers. Samples were analyzed using liquid-chromatography coupled to high resolution mass spectrometry. Resulting data were analyzed using a customized workflow designed for high-fidelity detection, prioritization, identification, and semi-quantitation of detected molecular features. Approximately 6350 compounds were detected in the combined data set, with 424 of those compounds tentatively identified through high quality spectral library match scores. Compounds identified included ethoxylated surfactants, pesticide and pharmaceutical transformation products, polymer additives, and rubber vulcanization agents. Compounds identified in samples were reflective of the apparent sources and pathways of organic pollutant inputs, with stormwater-influenced samples dominated by additive chemicals likely derived from plastics and vehicle tires, as well as ethoxylated surfactants.