Prediction of retention indexes. I. Structure-retention index relationship on apolar columns

AIRI: Predicting Retention Indices and Their Uncertainties Using Artificial Intelligence

The Kováts retention index (RI) is a quantity measured using gas chromatography and is commonly used in the identification of chemical structures. Creating libraries of observed RI values is a laborious task, so we explore the use of a deep neural network for predicting RI values from structure for standard semipolar columns. This network generated predictions with a mean absolute error of 15.1 and, in a quantification of the tail of the error distribution, a 95th percentile absolute error of 46.5. Because of the Artificial Intelligence Retention Indices (AIRI) network's accuracy, it was used to predict RI values for the NIST EI-MS spectral libraries. These RI values are used to improve chemical identification methods and the quality of the library. Estimating uncertainty is an important practical need when using prediction models. To quantify the uncertainty of our network for each individual prediction, we used the outputs of an ensemble of 8 networks to calculate a predicted standard deviation for each RI value prediction. This predicted standard deviation was corrected to follow the error between the observed and predicted RI values. The Z scores using these predicted standard deviations had a standard deviation of 1.52 and a 95th percentile absolute Z score corresponding to a mean RI value of 42.6.

Very-Long-Chain Wax Constituents from Primula veris and P. acaulis: Does the Paradigm of Non-Branched vs. Branched Chain Dominance Universally Hold in all Plant Taxa?

Herein n-, iso- and anteiso-series of very-long-chained (VLC) alkanes (C₂₁ -C₃₅ ), fatty acid benzyl esters (FABEs; C₂₀ -C₃₂ ), and 2-alkanones (C₂₃ -C₃₅ ) were identified in the wax of Primula veris L. and P. acaulis (L.) L. (Primulaceae). For the very first time in a sample of natural origin, the presence of iso- and anteiso-VLC FABEs and 2-alkanones was unequivocally confirmed by synthetic work, derivatization, and NMR. It should be noted that the studied species produced unusually high amounts of branched wax constituents (e. g., >50 % of 2-alkanones were branched isomers). The domination of iso-isomers, probably biosynthesized from leucine-derived starters, is a unique feature in the Plant Kingdom. The plant organ distribution of these VLC compounds in P. acaulis samples (different habitats and phenological phases) pointed to their possible ecological value. This was supported by a eutectic behavior of binary blends of FABEs and alkanes, as well as by high UV-C absorption by FABEs.

The chemistry and biological activities of Peperomia pellucida (Piperaceae): A critical review

ETHNOPHARMACOLOGICAL RELEVANCE

Peperomia pellucida (L.) Kunth is an annual weed with a preference to humid places with reduced solar radiation. This plant is mainly distributed in the Neotropics, Africa, Southeast Asia, and Australia. It is popularly employed in the treatment of a variety of health conditions such as abscesses, abdominal pain, skin sores, conjunctivitis, measles, and kidney troubles. Several studies have also described its antimicrobial, cytotoxic, antidiabetic and a variety of other bioactivities.

THE AIM OF THE REVIEW

The aim of this work is to evaluate, using a critical review, the present ethnomedicinal applications, phytochemistry and pharmacological studies of P. pellucida essential oils (EOs) and extracts from different locations around the world.

MATERIALS AND METHODS

This review was performed through an online survey of the ethnomedicinal practices, chemical compositions and pharmacological applications of P. pellucida EOs and extracts. The data were mainly obtained from online journals and books published in English, Portuguese and Spanish. The information was collected from websites such as Google, Google Scholar, PubMed, Science Direct, ResearchGate and other online databases that provided more information about this herb.

RESULTS

Peperomia pellucida bioactivities such as antimicrobial, cytotoxic, antioxidant, fracture healing, antidiabetic and anti-hypercholesterolemia have been described in several literature sources. Nonetheless, most reports only provide the phytochemical screening of extracts, which does not allow the identification of the active compounds. From these studies, some reported constituents are not included in the Dictionary of Natural Products (DNP), which raises questions toward their identification. In addition, some biological assays were even performed without standard controls for comparison which also makes these results questionable.

CONCLUSION

This review evaluates data regarding the phytopharmaceutical potential of P. pellucida. In general, several important aspects were questionable or missing in these manuscripts, which points out the need of more investigation on the pharmacological properties and phytochemical compositions of this herb.

Headspace solid-phase microextraction-gas chromatographic-time-of-flight mass spectrometric methodology for geographical origin verification of coffee

Increasing consumer awareness of food safety issues requires the development of highly sophisticated techniques for the authentication of food commodities. The food products targeted for falsification are either products of high commercial value or those produced in large quantities. For this reason, the present investigation is directed towards the characterization of coffee samples according to the geographical origin. The conducted research involves the development of a rapid headspace solid-phase microextraction (HS-SPME)-gas chromatography-time-of-flight mass spectrometry (GC-TOFMS) method that is utilized for the verification of geographical origin traceability of coffee samples. As opposed to the utilization of traditional univariate optimization methods, the current study employs the application of multivariate experimental designs to the optimization of extraction-influencing parameters. Hence, the two-level full factorial first-order design aided in the identification of two influential variables: extraction time and sample temperature. The optimum set of conditions for the two variables was 12 min and 55 degrees C, respectively, as directed by utilization of Doehlert matrix and response surface methodology. The high-throughput automated SPME procedure was completed by implementing a single divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) 50/30 microm metal fiber with excellent durability properties ensuring the completion of overall sequence of coffee samples. The utilization of high-speed TOFMS instrument ensured the completion of one GC-MS run of a complex coffee sample in 7.9 min and the complete list of benefits provided by ChromaTOF software including fully automated background subtraction, baseline correction, peak find and mass spectral deconvolution algorithms was exploited during the data evaluation procedure. The combination of the retention index (RI) system using C(8)-C(40) alkanes and the mass spectral library search was utilized for the confirmation of analyte identity in the reference authentic Brazilian coffee sample. The semi-quantitative results were then submitted to statistical evaluation, namely principal component analysis (PCA) for the establishment of geographical origin discriminations.

Alkylphenol retention indices

A novel type of retention indices for alkylphenols and related compounds are proposed. The alkylphenol retention indices (APRI) use para-substituted n-alkylphenols as reference series. APRI for para-n-alkylphenols are per definition equal to the number of carbon atoms in the alkyl substituent; the value for phenol is zero. Application of the APRI system with different types of derivatisation of the phenolic hydroxy group showed that the derivatisation has limited influence on these indices. Especially para-substituted alkylphenols gave APRI values that could be transferred with high accuracy from one type of derivative to another. By comparing results obtained with different gradients in temperature-programmed GC, it was also shown that APRI is less affected by chromatographic conditions than retention indices based on n-alkanes.

Quantitative study of the structure-retention index relationship in the imine family

The Kováts retention index is one of the most popular descriptors of the performance of organic compounds in gas chromatography (GC). The mathematical modeling of this index is an interesting and open problem in analytical chemistry. In this paper, two models for the prediction of the Kováts retention index are presented. Topologic, topographic and quantum-chemical descriptors were used as structural descriptors. Multiple linear regression (MLR) analysis provides the first model using the forward stepwise procedure for the variable selection. For the second one, an ensemble of artificial neural network (ANN) was constructed using the pruning algorithm. Both methods were validated by an external set of compounds, by the Golbraikh and Tropsha method and by the leave-one-out (LOO) and the leave many out (LMO) procedures. The R2, RMScv and Q2, values for the training sets were 0.884, 0.589 and 0.830 for NN and 0.974, 0.417 and 0.970 for MLR models, respectively. The robustness of both models was demonstrated. Both portrait the chromatographic performance of the sample but in this case, the results of MLR equation are better than the NN ones. The MLR model is recommended because of its simplicity.

Mosaic increments for predicting the gas chromatographic retention data of the chlorobenzenes

The chlorinated organic compounds are very important from the point of view of the chemical industry and environmental protection, and therefore the gas chromatographic analysis of these compounds is very interesting for analytical chemists. In this paper we studied the relationship between the molecular structure and gas chromatographic retention on several stationary phases having different polarity and at several temperatures of benzene and 12 chlorobenzene compounds as model compounds. A coding system involving primary (mosaic increments) and secondary (bond increments)calculation methods was developed. The retention indices of benzene and the chlorobenzenes calculated on HP-5 at 120 degrees C shows a better performance of the mosaic increments (average absolute deviation delta of 1.7 retention index units) compared with the bond increments (delta = 11.7 retention index units). Retention factors, k, calculated with mosaic increments for chlorobenzenes on SPB-1 and WAX-10, at 140 degrees C, yield average relative errors of epsilon = 0.9 and 3.5%, respectively. Therefore, the presented paper provides a new possibility for precalculation of the retention data.

Gas chromatographic retention indices for N-substituted amino s-triazines on capillary columns - Part IV: Influence of column polarity on retention index

The retention index increment for the addition of a methylene group to the alkyl group of an analyte molecule is shown to be lower than 100 i.u. for N-substituted amino s-triazines. In temperature programmed gas chromatography, a linearly interpolated retention index I, determined from the linear regression equation, I = AZ + (GRF)z, with the number of atoms (Z) in the molecule as variable, was used to describe the retention of 25 N-substituted amino s-triazines, on DB-1, DB-5 and DB-WAX capillary columns divided into five series according to the similarity of the alkyl groups in the particular series. In the above equation, A is the linear regression coefficient or the retention index increment per atom addition, Z the number of C,N and Cl atoms in the molecule, and (GRF)z the group retention factor or functionality constant for functional groups in the molecule, based on the number Z. It is possible to estimate the retention indices of an unknown member of the series from the Z, A and (GRF) values.

Quantitative structure-retention relationships for gas chromatographic retention indices of alkylbenzenes with molecular graph descriptors

Quantitative structure-retention relationships (QSRR) represent statistical models that quantify the connection between the molecular structure and the chromatographic retention indices of organic compounds, allowing the prediction of retention indices of novel, not yet synthesized compounds, solely from their structural descriptors. Using multiple linear regression, QSRR models for the gas chromatographic Kováts retention indices of 129 alkylbenzenes are generated using molecular graph descriptors. The correlational ability of structural descriptors computed from 10 molecular matrices is investigated, showing that the novel reciprocal matrices give numerical indices with improved correlational ability. A QSRR equation with 5 graph descriptors gives the best calibration and prediction results, demonstrating the usefulness of the molecular graph descriptors in modeling chromatographic retention parameters. The sequential orthogonalization of descriptors suggests simpler QSRR models by eliminating redundant structural information.

Prediction of retention indices. V. Influence of electronic effects and column polarity on retention index

The retention index increment for addition of a methylene group to an analyte molecule is shown for 1-halo-n-alkanes to be different from 100 i.u., a value that is customarily assigned according to the current convention in retention index prediction. In temperature-programmed gas chromatography using linearly interpolated retention index I, a linear regression equation, I=AZ+(GRF), with the number of atoms (Z) in the molecule as variable can describe the retention of 16 homologous series of organic compounds on non-polar and polar columns with characteristic A (linear regression coefficient) and (GRF) (group retention factor) values. A molecular model of retention on the basis of electron density and electron density distribution relative to that of n-alkane is proposed. This model brings out the inter- and intramolecular electronic effects in the analyte molecule and its dipole-dipole interaction with the stationary liquid phases, as variations in the A value. The (GRF) value varies with the connectivity ability of a functional group for extended conjugation, substitution, etc., but is most influenced by hydrogen bonding (H-bonding) with the stationary liquid phase. One can estimate the sequence of elution of a mixture of organic compounds from any two of the three parameters on the right-hand side of the above equation or retrieve the retention indexes of an entire homologous series from its A and (GRF) values. The fact that each analyte molecule has its own A value on different columns makes column difference (deltaI) compound-specific rather than column-specific, a departure from previous assumptions.

Retrieval of structure information from retention index

The chromatographic identity of a compound can be determined by four parameters, namely, I, A, Z and (GRF). These are interrelated in a linear regression equation, given in the paper as Eq. 8. The retrieval of structural information from retention data requires the introduction of a new meaning to the Kováts retention index, the use of column difference (delta I) to characterize functional groups, the redefinition of the role of electronegative oxygen and nitrogen atoms, and the division of retention index (I) into contributions from atoms and from functional groups. The separation of retention index (I) into molecular and interaction contributions is a necessary condition for retention index prediction from structure and also for structure information retrieval from retention data. According to Eq. 8 the retention index is uniquely determined by three parameters, namely A, Z and (GRF). For prediction of retention index, the A value is assigned a value of 100 index units (i.u.), the Z value is obtained directly from the compound, and the (GRF) value is pre-calibrated. In Eq. 10, the m and n values represent the pre-calibrated terms for a quantitative structure-retention index relationship. These terms account for the positive and negative retention contributions from polar and polarizable atom groups. All atom groups that are different from methylene and methyl groups will interact with the stationary phase and contribute to retention. The m and n values for various functional, polar and polarizable atom groups and their column differences (delta I values) are the results of interactions between the solute and the stationary phase and are structure dependent. The interaction increases with increasing polarities of the solute and the stationary phase. The column difference not only reflects the strength of the interaction, but is also characteristic of the functional and polarizable groups. The retrieval of structural information from retention data is equivalent to obtaining Z and (GRF) values from known I and delta I values, which is straightforward for monofunctional compounds. For multi-functional compounds, additional data will be needed for retrieval of structural information. These can be obtained from derivatization of the unknown compound, from its chemical reactions with other reagents, from GC-MS analysis and from structure match using internal or external standards. The additional data required will depend upon the complexity of the unknown structure. This approach demonstrates that a system can be devised to utilize GC retention characteristics uniquely for structure elucidation.

Isopentenoid synthesis in embryonic Drosophila cells: prenylated protein profile and prenyl group usage

It has been established that vertebrates and yeasts modified a unique subset of polypeptides with farnesyl and geranylgeranyl residues. This observation has been extended to Drosophila Kc cells. [3H]Mevalonate was incorporated into 54 Kc cell peptides (18-92 kDa). As reported for mammalian cells, most of the labeled peptides had molecular weights between 21 and 27 kDa. C18 radio-HPLC tryptic digest profiles for delipidized, [3H]mevalonate-labeled (a) insect (Drosophila and Spodoptera frugiperda) and mammalian (Chinese hamster ovary met 18-2b) cells, (b) Kc cell nuclear lamin, and (c) a 23.5-kDa purified Kc cell GTP-binding protein were compared and analyzed. [35S]Cysteine-labeled Kc cells yielded a tryptic digest radio-HPLC profile which was congruent with that for [3H]mevalonate-labeled cells. A significant fraction (30-33%) of the doubly labeled tryptic peptides were eluted with greater than or equal to 93% acetonitrile. Kc cell nuclear lamin tryptic digests yielded a single 3H-labeled product which migrated as S-farnesylcysteine. The Kc cell 23.5-kDa GTP-binding protein's 3H-labeled oligopeptide(s)/amino acid(s) was geranylgeranylated and its tryptic digest profile was representative of prenylated proteins whose oligopeptides eluted with greater than or equal to 93% acetonitrile. Moreover, the 3H-labeled oligopeptide/amino acid profiles plus prenyl group patterns for [3H]mevalonate-labeled Kc and mammalian cell total extracts were similar. Collectively, these observations supported a prenylated protein spectrum and prenyl group usage as highly conserved eukaryotic cellular characteristics.

Structure-retention index relationships for derivatized monosaccharides on non-polar gas chromatography columns

A gas chromatographic method for predicting the retention index of a derivatized monosaccharide is presented. The procedures are especially useful to detect and predict minute quantities of sugars in biological or chemical samples. Monosaccharides are first converted to the alditols and then derivatized by acetylation, permethylation or silylation. The derivatized monosaccharide structure-retention index relationship that has been developed is useful in the identification of unknown monosaccharides that can be readily confirmed by gas chromatography-mass spectrometry.

Prediction of retention indexes. IV. Chain branching in alkylbenzene isomers with C10-13 alkyl chains identified in a scintillator solvent

Twenty solvent components in a commercial scintillator were identified by chromatography on polar and non-polar columns and by gas chromatography-mass spectrometry (GC-MS) as isomeric 1-(alkyl)m(alkyl)nbenzenes with formulae C16H26, C17H28, C18H30 and C19H32. These isomers occur in four clusters of chromatographic peaks representing ca. 6, 44, 34 and 16% of the total solvent mass. The retention indexes of the isomers are influenced by the lengths of the alkyl chains in the molecule, and their polarity and polarizability can affect the column difference, which is the difference between retention indexes on polar and non-polar columns. 1-Methylalkylbenzenes have higher retention indexes and larger column differences than the evenly distributed isomers, such as 1-butylhexyl-1-pentylhexyl, 1-pentylheptyl- and 1-pentyloctylbenzene. The results demonstrate the effect of structural symmetry on the retention indexes of the isomers. This study shows that the ability to relate GC data and column differences to structures can facilitate the interpretation of GC-MS data in the structure identification of isomers.

Prediction of retention indexes. III. Silylated derivatives of polar compounds

Polar compounds containing hydroxyl, amino and carboxyl groups, singly or in combination, can be chromatographed after the polar functional groups are silylated. The silylated derivatives of acids, alcohols, amines, diols, amino alcohols, amino acids are shown to behave chromatographically as hydrocarbons, and their retention indexes can be readily predicted from their base values. The column difference, namely, the difference between the retention indexes of the analyte on polar and non-polar columns is minimal for the silylated derivatives in comparison to that observed for the underivatized analytes. This minimal column difference is attributed to the hydrocarbon-like chromatographic characteristics of the silylated derivatives. The retention indexes of the silyl derivatives appear to correlate with the atom number Z of the analyte.

Prediction of retention indexes. II. Structure-retention index relationship on polar columns

A method is described for the prediction of the retention index (I) from chemical structure, using the number of atoms in the molecule (Z), the I increment for atom addition (A) and the group retention factors (GRFs) of the functional groups and substituents. This method can predict the retention indexes of a wide range of compounds, such as acids, alcohols, amines, acid esters, aldehydes, ketones, ethers, aromatic hydrocarbons, alicyclics, heterocyclics, etc. on polar as well as non-polar columns to within 3% error. Accurate A and GRF values are essential to the prediction. These values can be obtained from homologous series, but a system of arbitrarily assigned A value and adjusted GRFs are also used. The GRFs of the substituents and functional groups depend on the polarity and polarizability of the analyte and the stationary phase and also on the molecular connectivity of the atoms, namely, primary, secondary and tertiary carbon atoms or hydrogen atoms, to which these groups are attached. Highly polar and polarizable groups can alter the A value. When the functionality of a group is masked by substitution, the analyte molecule will tend to behave chromatographically like hydrocarbons. The difficulty in predicting the I values of compounds of multi-functionality by the rule of additivity is the unknown intramolecular interaction that can alter both A and GRF values.