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

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.

Lacticaseibacillus Strains Isolated from Raw Milk: Screening Strategy for Their Qualification as Adjunct Culture in Cheesemaking

The microbial ecology fundamentals of raw milk and long-ripened cheeses consist of a complex interaction between starter lactic acid bacteria (SLAB) and non-starter LAB (NSLAB). Although NSLAB aromatic properties are paramount, other phenotypic traits need to be considered for their use as adjunct cultures, such as the capability to endure technological parameters encountered during cheesemaking. The present study focused on the isolation and characterization of NSLAB from spontaneously fermented raw cow's milk coming from 20 dairies that produce Grana Padano PDO cheese. From 122 isolates, the screening process selected the 10 most diverse strains belonging to Lacticaseibacillus spp. to be phenotypically characterized. The strains were tested for their growth performance in milk in combination with the application of technological stresses, for their ability to produce volatile compounds after their growth in milk, and for their ability to use different nutrient sources and resist chemicals. The complex characterization qualified the strains 5959_Lbparacasei and 5296_Lbparacasei as the best candidates to be used as adjunct strains in the production of raw milk and long-ripened cheeses, provided that antibiotic resistance is measured before their employment. Other strains with interesting aromatic capabilities but lower heat resistance were 5293_Lbparacasei, 5649_Lbparacasei and 5780_Lbparacasei, which could be candidates as adjunct strains for uncooked cheese production.

Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data

Non-target analysis combined with liquid chromatography high resolution mass spectrometry is considered one of the most comprehensive strategies for the detection and identification of known and unknown chemicals in complex samples. However, many compounds remain unidentified due to data complexity and limited number structures in chemical databases. In this work, we have developed and validated a novel machine learning algorithm to predict the retention index (r[Formula: see text]) values for structurally (un)known chemicals based on their measured fragmentation pattern. The developed model, for the first time, enabled the predication of r[Formula: see text] values without the need for the exact structure of the chemicals, with an [Formula: see text] of 0.91 and 0.77 and root mean squared error (RMSE) of 47 and 67 r[Formula: see text] units for the NORMAN ([Formula: see text]) and amide ([Formula: see text]) test sets, respectively. This fragment based model showed comparable accuracy in r[Formula: see text] prediction compared to conventional descriptor-based models that rely on known chemical structure, which obtained an [Formula: see text] of 0.85 with an RMSE of 67.

Long-chain syn-1-phenylalkane-1,3-diyl diacetates, related phenylalkane derivatives, and sec-alcohols, all possessing dominantly iso-branched chain termini, and 2/3-methyl-branched fatty acids from Primula veris L. (Primulaceae) wax

Herein, the results of the first study of non-flavonoid constituents of aboveground surface-wax washings of Primula veris L. (Primulaceae) are presented. Chromatography of the washings yielded a minor fraction composed of n-, iso-, and anteiso-series of long-chained syn-1-phenylalkane-1,3-diyl diacetates, 3-oxo-1-phenylalkan-1-yl acetates, 1-phenylalkane-1,3-diones, 1-hydroxy-1-phenylalkan-3-ones, sec-alcohols (2- to 10-alkanols), and n-, iso-, anteiso-, 2-methylalkanoic and 3-methylalkanoic acids; 118 of these constituents represent up to now unreported natural compounds. The structural/stereochemical elucidation was accomplished by the synthesis of authentic standards, derivatization reactions, the use of gas chromatographic retention data and detailed 1D and 2D-NMR analyses of the obtained complex chromatographic fraction. Primula veris produces unusually high amounts of branched long-chained metabolites (>60%) except for the fatty acids where the percentage of branched isomers is comparable to the ones with n-chains. Noteworthy is the fact that long-chained α- and β-methyl substituted fatty acids were detected herein for the first time in the kingdom Plantae.

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.

Migration- and Proliferation-Promoting Activities in Human Keratinocytes of Zea mays Flower Absolute and Its Chemical Composition

Zea mays L. (ZM) has cytotoxic and anti-inflammatory activities, but its biological activities such as skin regeneration and wound healing in human skin have not been reported. In the present study, we tested the effects of ZM flower (ZMF) absolute on proliferation and migration of human keratinocytes (HaCaTs) and identified its components by using gas chromatography/mass spectrometry (GC/MS) analysis. GC/MS analysis revealed that the ZMF absolute contained 13 constituents, and it increased HaCaT proliferation and migration. The ZMF absolute enhanced the phosphorylation levels of serine/threonine-specific protein kinase (Akt), p38 mitogen-activated protein kinase (MAPK), and extracellular signal-regulated kinase1/2 in HaCaTs. In addition, the absolute induced an increase in sprout outgrowth of HaCaTs. The present study reports for the first time that ZMF absolute may promote skin wound healing and/or skin regeneration by stimulating proliferative and migratory activities in dermal keratinocytes through the Akt/MAPK pathway. Therefore, ZMF absolute may be a promising natural material for the use in skin regeneration and/or wound healing applications.

Current status and prospects of computational resources for natural product dereplication: a review

Research in natural products has always enhanced drug discovery by providing new and unique chemical compounds. However, recently, drug discovery from natural products is slowed down by the increasing chance of re-isolating known compounds. Rapid identification of previously isolated compounds in an automated manner, called dereplication, steers researchers toward novel findings, thereby reducing the time and effort for identifying new drug leads. Dereplication identifies compounds by comparing processed experimental data with those of known compounds, and so, diverse computational resources such as databases and tools to process and compare compound data are necessary. Automating the dereplication process through the integration of computational resources has always been an aspired goal of natural product researchers. To increase the utilization of current computational resources for natural products, we first provide an overview of the dereplication process, and then list useful resources, categorizing into databases, methods and software tools and further explaining them from a dereplication perspective. Finally, we discuss the current challenges to automating dereplication and proposed solutions.

Method to predict gas chromatographic response factors for the trace-level analysis of volatile organic compounds based on the effective carbon number concept

A procedure has been developed to estimate GC-MS response factors based on the theory of effective carbon number defined as the sum of the carbon number and carbon number equivalent for each selected molecular descriptor (multiplied by its number of occurrences) in each compound's molecular structure. As a means to validate the effective carbon number procedure for GC-MS analysis, a test suite of 19 volatile organic compounds was analyzed by the sorbent-tube thermal desorption method. In the effective carbon number procedure, the carbon number equivalent for each descriptor was determined to yield the optimal linear plots between response factor versus the effective carbon number with the maximum R(2) (>0.975) and the minimum mean absolute error (<5%). Effective carbon number analysis is validated as a potent approach to estimate response factor values for most compounds amenable to the sorbent-tube thermal desorption GC-MS method. Overall, it is concluded that the application of response factor versus effective carbon number relationship can produce fairly reliable prediction with reduced errors relative to other comparable procedures such as the response factor versus the carbon number approach.

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.

Approaches to identifying and quantifying polycyclic aromatic hydrocarbons of molecular weight 302 in diesel particulates

Among the PAH class of compounds, high molecular weight PAH are now considered as relevant cancer inducers, but not all of them have the same biological activity. However, their analysis is difficult, mainly due to the presence of numerous isomers and due to their low volatility. Retention indices (Ri) for 13 dibenzopyrenes and homologues were determined by high-resolution capillary gas chromatography (GC) with four different stationary phases: a 5% phenyl-substituted methylpolysiloxane column (DB-5 ms), a 35% phenyl-substituted methylpolysiloxane column (BPX-35), a 50% phenyl-substituted methylpolysiloxane column (BPX-50), and a 35% trifluoropropylmethyl polysiloxane stationary phase (Rtx-200). Correlations for retention on each phase were investigated by using 8 independent molecular descriptors. Ri has been shown to be linearly correlated to PAH volume, polarisability alpha, Hückel-pi energy on the four examined columns. Ionisation potential Ip is a fourth variable which improves the regression model for DB-5ms, BPX-35, and BPX-50 column. Correlation coefficients ranging from r2 = 0.935 to r2 = 0.952 are then observed. Application of these indices to the identification and quantification of PAH with MW 302 in certified diesel particulate matter SRM 1650a is presented and discussed.

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.

Analysis of alkyl organoiodide mixtures by high-performance liquid chromatography using electrochemical detection with a post-column photochemical reactor

A method for the analysis of six alkyl organoiodides (iodomethane, iodoethane, 1-iodopropane, 1-iodobutane, 1-iodopentane, 1-iodohexane) commonly found in acetic acid process was developed. In this method the target analytes were determined by high-performance liquid chromatography (HPLC) using a post-column photochemical reactor with electrochemical detection (ED) in less than 30 min. HPLC was performed in ODS C18 reversed-phase column (5 microm, 250 x 4.6 mm I.D.) under isocratic conditions with methanol-0.067 M acetate buffer (70:30, v/v), pH 6.2 as mobile phase at flow-rate 1.1 ml/min. Alkyl organoiodides, which are electrochemically inactive, were made oxidizable at potential of 120 mV after post-column irradiation with low-pressure mercury lamp in a knitted PTFE tube. The photoreactor was placedin an aluminum housing full of nitrogen in order to prevent from the interference of oxygen. The detection limit for most analytes was of the order of 1-2 microg/l. The HPLC-ED method with a post-column photochemical reactor has good precision and linearity and can be readily applied to the routine determination of alkyl organoiodides in real acetic acid samples.