Retention Index Data Research Articles

GCMS-ID: a webserver for identifying compounds from gas chromatography mass spectrometry experiments.

GCMS-ID (Gas Chromatography Mass Spectrometry compound IDentifier) is a webserver designed to enable the identification of compounds from GC-MS experiments. GC-MS instruments produce both electron impact mass spectra (EI-MS) and retention index (RI) data for as few as one, to as many as hundreds of different compounds. Matching the measured EI-MS, RI or EI-MS+RI data to experimentally collected EI-MS and/or RI reference libraries allows facile compound identification. However, the number of available experimental RI and EI-MS reference spectra, especially for metabolomics or exposomics-related studies, is disappointingly small. Using machine learning to accurately predict the EI-MS spectra and/or RIs for millions of metabolomics and/or exposomics-relevant compounds could (partially) solve this spectral matching problem. This computational approach to compound identification is called in silico metabolomics. GCMS-ID brings this concept of in silico metabolomics closer to reality by intelligently integrating two of our previously published webservers: CFM-EI and RIpred. CFM-EI is an EI-MS spectral prediction webserver, and RIpred is a Kovats RI prediction webserver. We have found that GCMS-ID can accurately identify compounds from experimental RI, EI-MS or RI+EI-MS data through matching to its own large library of>1 million predicted RI/EI-MS values generated for metabolomics/exposomics-relevant compounds. GCMS-ID can also predict the RI or EI-MS spectrum from a user-submitted structure or annotate a user-submitted EI-MS spectrum. GCMS-ID is freely available at https://gcms-id.ca/.

Screening Disinfection Byproducts in Arid-Coastal Wastewater: A Workflow Using GC×GC-TOFMS, Passive Sampling, and NMF Deconvolution Algorithm.

Disinfection during tertiary municipal wastewater treatment is a necessary step to control the spread of pathogens; unfortunately, it also gives rise to numerous disinfection byproducts (DBPs), only a few of which are regulated because of the analytical challenges associated with the vast number of potential DBPs. This study utilized polydimethylsiloxane (PDMS) passive samplers, comprehensive two-dimensional gas chromatography (GC×GC) coupled with time-of-flight mass spectrometry (TOFMS), and non-negative matrix factorization (NMF) spectral deconvolution for suspect screening of DBPs in treated wastewater. PDMS samplers were deployed upstream and downstream of the chlorination unit in a municipal wastewater treatment plant located in Abu Dhabi, and their extracts were analyzed using GC×GC-TOFMS. A workflow incorporating a multi-tiered, eight-filter screening process was developed, which successfully enabled the reliable isolation of 22 candidate DBPs from thousands of peaks. The NMF spectral deconvolution improved the match factor score of unknown mass spectra to the reference mass spectra available in the NIST library by 17% and facilitated the identification of seven additional DBPs. The close match of the first-dimension retention index data and the GC×GC elution patterns of DBPs, both predicted using the Abraham solvation model, with their respective experimental counterparts-with the measured data available in the NIST WebBook and the GC×GC elution patterns being those observed for the candidate peaks-significantly enhanced the accuracy of peak assignment. Isotopic pattern analysis revealed a close correspondence for 11 DBPs with clearly visible isotopologues in reference spectra, thereby further strengthening the confidence in the peak assignment of these DBPs. Brominated analogues were prevalent among the detected DBPs, possibly due to seawater intrusion. The fate, behavior, persistence, and toxicity of tentatively identified DBPs were assessed using EPI Suite™ and the CompTox Chemicals Dashboard. This revealed their significant toxicity to aquatic organisms, including developmental, mutagenic, and endocrine-disrupting effects in certain DBPs. Some DBPs also showed activity in various CompTox bioassays, implicating them in adverse molecular pathways. Additionally, 11 DBPs demonstrated high environmental persistence and resistance to biodegradation. This combined approach offers a powerful tool for future research and environmental monitoring, enabling accurate identification and assessment of DBPs and their potential risks.

DrugBank 6.0: the DrugBank Knowledgebase for 2024.

First released in 2006, DrugBank (https://go.drugbank.com) has grown to become the 'gold standard' knowledge resource for drug, drug-target and related pharmaceutical information. DrugBank is widely used across many diverse biomedical research and clinical applications, and averages more than 30 million views/year. Since its last update in 2018, we have been actively enhancing the quantity and quality of the drug data in this knowledgebase. In this latest release (DrugBank 6.0), the number of FDA approved drugs has grown from 2646 to 4563 (a 72% increase), the number of investigational drugs has grown from 3394 to 6231 (a 38% increase), the number of drug-drug interactions increased from 365 984 to 1 413 413 (a 300% increase), and the number of drug-food interactions expanded from 1195 to 2475 (a 200% increase). In addition to this notable expansion in database size, we have added thousands of new, colorful, richly annotated pathways depicting drug mechanisms and drug metabolism. Likewise, existing datasets have been significantly improved and expanded, by adding more information on drug indications, drug-drug interactions, drug-food interactions and many other relevant data types for 11891 drugs. We have also added experimental and predicted MS/MS spectra, 1D/2D-NMR spectra, CCS (collision cross section), RT (retention time)and RI (retention index) data for 9464 of DrugBank's 11710 small molecule drugs. These and other improvements should make DrugBank 6.0 even more useful to a much wider research audience ranging from medicinal chemists to metabolomics specialists to pharmacologists.

Determination of The Essential Oil Components of Some Sage (Salvia Sp.) Species Naturally Distributed in The Isparta Province

Aim of the study: This study was performed to determine the essential oil components of Salvia tomentosa Mill., Salvia argentea L. and Salvia bracteata Bank et Sol. Area of study: The study was carried out in two districts (Eğirdir and Şarkikaraağaç) located at Isparta province in Turkey. Material and methods: The isolation of essential oil components was performed from shoots with leaves and flowers. Qualitative analysis of essential oils was carried out by using a Shimadzu 2010 Plus GC-MS device. The identification of the constituents was carried out by comparing the retention index (RI) and mass spectral data (MS) to those reported in the literature. Main results: As a result of the GC-MS analysis, the major components of the essential oil were (-)-caryophyllene oxide (49.56%), β-vatirenene (7.87%), and α-Muurolol (6.78%) in S. tomentosa, sclareol (40.01%), germacrene-D (13.93%) and β-pinene (11.93%) in S. argentea and eucalyptol (1,8-cineole) (16.6%), β-pinene (14.7%) and cembrene (10.88%) in S. bracteata. Sclareol, which was determined at a high concentration in S. argentea in this study, is an economically valuable component that is widely used as flavoring in food and tobacco industry and as a perfume ingredient in the cosmetic industry. Highlights: According to this study, the cultivation of S. argentea can provide high economic returns

Determination of volatile components of Viscum album L. distributed naturally in Eğirdir (Isparta) province, Turkey

This study aims to determining the volatile oil components of naturally distributed Viscum album L. belonging to the Santalaceae family. The study was carried out in villages of Eğirdir province of Isparta in Turkey. The isolation of volatile components was performed from of the leaves of the mistletoe collected from five different tree species. Qualitative analysis of volatile components were carried out by the Head Void-Solid Phase Micro Extraction (HS-SPME) technique combined with gas chromatography/mass spectrometry (GC-MS). The identification of the constituents was carried out by comparing the retention index (RI) and mass spectral data (MS) to those reported in the published literature. As a result of the Head Void-Solid Phase Micro Extraction (HS-SPME) technique combined with gas chromatography/mass spectrometry (GC-MS). 54 different components were determined and (E)-2-Hexenal, Benzaldehyde and Nonanal were found as the main components. It is known that there are limited number of studies on the determination of essential oil and volatile components of the Viscum genus. According to this study, use of mistletoe as a raw material source in different industrial areas have importance

Development of Liquid Chromatographic Retention Index Based on Cocamide Diethanolamine Homologous Series (C(n)-DEA).

There is a growing need for indexing and harmonizing retention time (tR) data in liquid chromatography derived under different conditions to aid in the identification of compounds in high resolution mass spectrometry (HRMS) based suspect and nontarget screening of environmental samples. In this study, a rigorously tested, inexpensive, and simple system-independent retention index (RI) approach is presented for liquid chromatography (LC), based on the cocamide diethanolamine homologous series (C(n = 0-23)-DEA). The validation of the CDEA based RI system was checked rigorously on eight different instrumentation and LC conditions. The RI values were modeled using molecular descriptor free technique based on structural barcoding and convolutional neural network deep learning. The effect of pH on the elution pattern of more than 402 emerging contaminants were studied under diverse LC settings. The uncertainty associated with the CDEA RI model and the pH effect were addressed and the first RI bank based on CDEA calibrants was developed. The proposed RI system was used to enhance identification confidence in suspect and nontarget screening while facilitating successful comparability of retention index data between various LC settings. The CDEA RI app can be accessed at https://github.com/raalizadeh/RIdea.

Retention indices for naturally-occurring chiral and achiral compounds on common gas chromatography chiral stationary phases

Chiral gas chromatography columns were initially developed for enantiomeric separations, however, they are increasingly being used for routine analyses of natural samples which also include many achiral compounds. Retention index (RI) data have been essential in the identification of natural compounds using standard columns when authentic standards are not available, but scarce RI information exists for chiral columns. In this work, we present linear RI data for 81 compounds (chiral and achiral) commonly found in biological samples for the two most common chiral stationary phases, Cyclodex-B and CycloSil-B, calculated with an alkane hydrocarbon series. In addition, we compared the resolution of various isomer pairs between the two chiral stationary phases and found better overall performance with CycloSil-B. Finally, we tested the applicability of our data set by calculating RI values for numerous compounds in leaf extracts of two Monarda fistulosa chemotypes on both columns and found good agreement. We discuss the relevance of chiral chromatographic retention data in a chemical ecology framework and call for the development of a comprehensive chiral RI database.

Integrated data analysis making use of the total information from gas chromatography and high-resolution time-of-flight mass spectrometry to identify qualitative differences between two whisky samples.

Analysis of complex mixtures with gas chromatography coupled with high-resolution time-of-flight mass spectrometry (GC/HRTOFMS) can produce a large amount of data. A new software program was recently reported that integrates all of the available mass spectrometric information from GC/HRTOFMS analysis into a concise report. New capabilities have now been added to the software to incorporate retention index data and to identify differences between two samples. Two Scotch whisky samples were sampled by solid-phase microextraction (SPME) and analyzed by GC/HRTOFMS. One of the two whisky samples (Sample B) was identical to the other sample (Sample A) except for an additional 6-months storage in sherry casks. Both electron ionization (EI) and chemical ionization (CI) data were obtained using a new GC/TOFMS system (JEOL AccuTOF GC-Alpha) with enhanced resolving power and mass accuracy. Statistical analysis of replicate measurements of the whisky samples was carried out with a new software program (msFineAnalysis version 3.2) to identify and assign differences between the samples. There were 124 peaks detected in the two whiskies. Thirteen compounds were detected with a relative peak area greater than 0.05% that were present in greater amounts in the whisky that had been stored in sherry casks for an additional 6months. Ten of these compounds were identified by the software with high confidence, two were identified as isomers with close retention indices, and one was identified interactively. The software identified small differences between the two samples that resulted from sherry cask aging. Because all of the information available from the hard and soft ionization analyses for each compound is summarized in a concise integrated report, the operator can rapidly determine the level of confidence for each assignment and inspect the information for compounds that are not present in the database.

Machine learning-based quantitative structure–retention relationship models for predicting the retention indices of volatile organic pollutants

In this research, a dataset including 206 volatile organic compounds was used to develop quantitative structure–retention relationship models for predicting the retention indices of volatile organic compounds on DB-5 stationary phase. A total of 141 molecules were put in train set to build models and 65 molecules were put in test set to validate models, externally. By using stepwise-multiple linear regression, two descriptors including X1sol (solvation connectivity index chi-1) and AAC (mean information index on atomic composition) were selected to create linear and nonlinear quantitative structure–retention relationship models. Multiple linear regression, epsilon-support vector regression and deep learning-based artificial neural network were used as modeling techniques. All models were validated by calculating several statistical parameters for both train and test sets that show created models have high predictive power. R2 values for the test set of multiple linear regression, epsilon-support vector regression and deep learning-based artificial neural network models were 0.90, 0.94 and 0.94, respectively. Results show the Van der Waals interactions of molecules with methyl groups in DB-5 stationary phase and the electrostatic interactions of atoms with partial negative charge in molecules with the hydrogen atoms of phenyl groups in DB-5 stationary phase are responsible for the separation of volatile organic compounds in DB-5 stationary phase. Finally, these created models were used to predict the retention indices of 694 volatile organic compounds that had no retention index data on DB-5 stationary phase.

The implementation of K-Means clustering in kovats retention index on gas chromatography

In this study, the retention index data of 146 compounds that are found in coal and petroleum-derived liquid fuels were grouped using the K-means clustering method, and the similarities between each cluster were analyzed. The psycho-chemical properties of each compound in the cluster were identified and compared with other clusters. Each compound’s retention index is grouped based on the similarity between the column polarity and heating rate of one compound to another. Based on the results of tests carried out on nine differentk values, it is known that the grouping with the value of k = 3 is the best determined from the obtained silhouette score = 0.568, where this score is higher than the score obtained on the other k values. The results of clustering with k = 3 obtained three clusters, namely cluster C1, cluster C2, and cluster C3. Cluster C1 and cluster C2 consist of chemical compounds that have a relatively low carbon number and molecular mass, but in cluster C2 the molecular mass of the compound is lower than in cluster C1. In contrast, the C3 cluster consists of chemical compounds that have a relatively high carbon number and molecular mass.

Lithium aluminum hydride reduction of 1-phenylbutane-1,3-dione, and acetylation of the products: NMR and GC-MS analysis

Reduction of ?-diketones with lithium aluminum hydride (LiAlH4, LAH) can lead to different products, depending on the tautomeric equilibrium: the reduction of diketo forms gives the corresponding diols and the reduction of ketoenol forms yields elimination products, saturated and unsaturated ketones and alcohols. Here, we report on the results of LAH reduction of 1-phenylbutane-1,3-dione. The products of reduction were further acetylated and separated by dry flash chromatography. The obtained products, phenylbut(en)ols, phenylbut(en)ones and phenylbut(en)yl acetates, were characterized by spectral (1H and 13C NMR, MS) and retention index (RI) data. It can be concluded that LAH preferentially reduces the carbonyl group more distant from the phenyl group of 1-phenylbutane-1,3-dione. The structure-retention index relationships between isomers were discussed. Proton splitting patterns were resolved by proton NMR simulations.

Calculation of Abraham model L-descriptor and standard molar enthalpies of vaporization for linear C7-C14 alkynes from gas chromatographic retention index data

Abraham model L solute descriptors have been determined for an additional 33 linear C 7 -C 14 alkynes based on published gas chromatographic retention indices for solutes eluted from capillary columns coated with squalane and apiezon L stationary phases. Standard molar enthalpies of vaporization and sublimation at 298 K are calculated for the 33 linear alkynes using the reported solute descriptors and our recently published Abraham model correlations. Calculated vaporization enthalpies derived from the Abraham model compare very favourably with values based on a popular atom-group additivity model.

Grouping of Retention Index on Gas Chromatography using Cluster Analysis

Retention index data consisting of 146 compounds commonly found in fuels are used to analyze the level of a psycho-chemical property similarity with cluster analysis methods. Cluster analysis is applied based on the temperature and column dependencies. The results show that the retention index was divided into four groups. The first group consists of benzene and cycloalkanes. The retention index of the compound shows high-temperature dependence and dependence on small column polarity. The second group consists of aromatic compounds and compounds that contain N, S, O. The retention index of these compounds shows dependence on temperature and polarity of large columns. The third group consists of aromatic compounds and hydrocarbons. The retention index of hydrocarbon compounds shows temperature dependence smaller than N, S, O. compounds and also the dependence on small column polarity. And the fourth group consists of only two compounds namely n-Hentriacontane and chrysene. The retention index of the two compounds shows a significant temperature dependence.

Combining linear retention index and electron ionization mass spectrometry for a reliable identification in nano liquid chromatography

The present research is focused on the object to improve identification capability in liquid chromatography (LC), by creating a system as similar as possible to gas chromatography (GC), where the combination/complementarity of Linear Retention Index (LRI) and Electron Ionization Mass Spectrometry (EI-MS) data makes the identification process easy, automatic and reliable. Conversely, in LC the untargeted characterization of real-world samples is still a challenge, due to the not repeatable and poorly informative nature of typical atmospheric pressure ionization mass spectrometry, normally hyphenated to LC. In the last decades the miniaturization of LC instrumentation together with the considerable progresses in MS vacuum pump capability has made the LC-EI-MS hyphenation more feasible. In the present work, a prototypal nanoLC-EI-MS system was used for the determination of typically LC-amenable compunds, such as coumarins, furocoumarins and polymethoxyflavones in citrus essential oils. All the compounds provided high quality EI-MS spectra, evaluated by the comparison with thousands of spectra present in commercial EI-MS libraries. Spectral similarities major than 80% were achieved.Furthermore, an LRI system, based on the use of an alkyl aryl ketone homologue reference series, was proposed as additional filter to achieve a univocal identification. Then, a novel dual-filter LRI/EI-MS library was built and resulted very helpful in the case of isomeric compounds characterized by identical EI-MS spectrum, but different retention behaviour. The very low inter-day variability attained for each LRI value, together with the satisfactory chromatographic resolution of the developed method, led to a 100% reliability of the identification process based on LRI.

Unusual Regularity in GC Retention of Simple Amino Acid Derivatives.

Application of simple regularities and general principles along with direct use of reference gas chromatography retention index data for reliable structure determination of compounds can be enhanced by determination of new regularities that are specific to certain structural elements. Revelation and interpretation of an anomaly in the elution order of alkyl esters of alkoxycarbonyl derivatives of glycine and alanine on standard and semi-standard non-polar phases. Preliminary derivatization of amino acids to alkyl esters of N-alkoxycarbonyl analogs and interpretation of their gas chromatographic characteristics. Alkyl esters of N-alkoxycarbonyl derivatives of alanine (Alkyl = C2H5, n- and iso-C3H7) elute prior to the same derivatives of glycine, despite the presence of an additional methyl group at C(2) in the molecule. Elution order is reversed for methyl esters of N-methoxycarbonyl derivatives. It is established that the peculiar behavior of alkyl esters of N-alkoxycarbonyl derivatives of glycine and alanine agrees with the concepts of gas chromatography and the known retention index regularities of organic compounds. A decrease of retention index values is a result of an introduction of an additional methyl group to a carbon atom connected to two polar fragments in a molecule like CH2XY. The dependence of the difference of retention index values for homologs of the types of CH3-CHXY and CH2XY vs. the total mass of fragments (X + Y) is similar to those for other sub-groups of analytes.

Detailed qualitative analysis of honeybush tea (Cyclopia spp.) volatiles by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry and relation with sensory data

The volatile composition of honeybush (Cyclopia) species was studied by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOF-MS). Headspace-solid phase micro-extraction (HS-SPME) was used to extract the volatile compounds from tea infusions prepared from the three species C. genistoides, C. maculata and C. subternata. A total of 287 compounds were identified, 101 of which were confirmed using reference standards, while the remainder were tentatively identified using mass spectral and retention index (RI) data. The identification power of TOF-MS enabled the tentative identification of 147 compounds for the first time in honeybush tea. The majority of the compounds identified were common to all three Cyclopia species, although there were differences in their relative abundances, and some compounds were unique to each of the species. In C. genistoides, C. maculata and C. subternata 265, 257 and 238 compounds were identified, respectively. Noteworthy was the tentative identification of cinnamaldehyde in particular C. maculata samples, which points to the likely contribution of this compound to their distinct sensory profiles. This study emphasises the complexity of honeybush tea volatile composition and confirms the power of GC×GC combined with TOF-MS for the analysis of such complex samples.

Quantitative structure-gas chromatographic retention index relationship of long-chain esters: The case of Scandix pecten-veneris L. essential-oil constituents

Motivated by a recent identification of two homologous series of branched butanoates and pentanoates in S. pecten-veneris essential oil, with an apparently regular change of their retention index (RI) values, we decided to examine the generality of such structure-chromatographic property relationship. Based on the experimentally obtained retention data (RI values of in total 20 compounds) of hexyl, decyl, tridecyl, tetradecyl, pentadecyl, hexadecyl, heptadecyl, octadecyl, heneicosyl and tricosyl isobutanoates and 3-methylbutanoates and selected 2-methylbutanoates, a model was built up that correlates the total number of carbon atoms, Wiener (WI), Balaban (BI) and molecular topological (MTI) indices of the mentioned esters and their RI data (RI = 240.5 + 91.2 x C + 2.94 x WI + 4.6 x 10-5 x BI - 0.381 x MTI). The obtained equation represents a new and simple tool for the prediction of gas chromatographic (retention indices) data for esters of straight long-chain fatty alcohols and branched short aliphatic acids.

Optimizing artificial neural network models for metabolomics and systems biology: an example using HPLC retention index data.

Artificial Neural Networks (ANN) are extensively used to model 'omics' data. Different modeling methodologies and combinations of adjustable parameters influence model performance and complicate model optimization. We evaluated optimization of four ANN modeling parameters (learning rate annealing, stopping criteria, data split method, network architecture) using retention index (RI) data for 390 compounds. Models were assessed by independent validation (I-Val) using newly measured RI values for 1492 compounds. The best model demonstrated an I-Val standard error of 55 RI units and was built using a Ward's clustering data split and a minimally nonlinear network architecture. Use of validation statistics for stopping and final model selection resulted in better independent validation performance than the use of test set statistics.

Retention Indices for Identification of Aroma Compounds by GC: Development and Application of a Retention Index Database

In this paper we focus on the development and application of a retention index database for aroma compounds with high structural diversity. Retention indices of over 300 aroma compounds were determined on three capillary columns of different polarity. The capability of the retention index database was evaluated by identification of the components of tobacco flavor. The results showed that the database can be used for qualitative identification. The database also contains a good set of retention index data for study of quantitative structure–retention relationships.

Volatile constituents of mini-watermelon fruits

Aroma is one of the essential factors for evaluating fruit quality, and the volatile constituents in mini-watermelon fruits were here determined by a headspace-solid-phase micro-extraction and gas chromatography–mass spectrometry method (HS-SPME-GC–MS). The plants, Citrullus lanatus [Thumb.] Matsum. and Nakai cv. Minirossa (Cucurbitaceae), were cultivated in Pachino, Sicily (Italy) under greenhouse conditions. The fruits were collected in two different production years. Sixty-eight volatile compounds have been identified and quantified; among these, the impact aroma compounds are also included. The volatile compounds have been identified by linear retention index, mass spectra, standard injection and reference data; the quantification has been carried out by the standard addition technique. The main compounds were (Z)-3-nonen-1-ol, (E)-2-nonenal and (Z,Z)-3,6-nonadien-1-ol, which are characteristic volatiles in the Cucurbitaceae family.