Predicting retention times of naturally occurring phenolic compounds in reversed-phase liquid chromatography: a Quantitative Structure-Retention Relationship (QSRR) approach

Prediction of retention data of phenolic compounds by quantitative structure retention relationship models under reverse-phase liquid chromatography

Quantitative Structure-Retention Relationship models were developed to identify phenolic compounds using a typical LC- system, with both UV and MS detection. A new chromatographic method was developed for the separation of fifty-two standard phenolic compounds. Over 5000 descriptors for each standard were calculated using AlvaDesc software and then selected through Genetic Algorithm. The selected descriptors were used as variables for models construction and to obtain a better understanding of the retention behaviour of phenols during reverse-phase separation. Three distinct molecule sets, including fifty-two phenolic compounds (Set 1), 32 flavonoids (Set 2) and 15 mono-substituted flavonoids were divided into training and validation sets to build Partial Least Square, Multiple Linear Regression and Partial Least Square-Artificial Neural Network models. To assess the predictivity of the models, these were tested on a bergamot juice sample. Partial Least Square and Partial Least Square-Artificial Neural Network exhibit the lowest prediction error, and the latter showed the best predictive power in real sample recognition. The building and implementation of such predictive models showed to be a powerful tool to identify phenolic compounds based on retention data and avoiding the use of expensive and sophisticated detectors such as tandem MS.

Assessment of environmental forensic indicator for anthropogenic groundwater contamination via target/suspect/nontarget analysis using HRMS techniques

This study compared target/suspect/nontarget analysis via liquid chromatography-high-resolution mass spectrometry (LC-HRMS) with traditional environmental forensic methods, specifically nitrate and its stable N isotope, in assessing groundwater pollution from livestock manure and agriculture. Using an in-house database of 1471 target and suspects, 35 contaminants (pesticides, veterinary drugs, surfactants) were identified, some uniquely linked to specific pollution sources, such as sulfamethazine and 4-formylaminoantipyrine in manure-affected areas. Pesticides were widespread, typically showing higher intensity in agricultural zones. On the other hand, the results of stable N isotope analysis (δ15N-NO3: 4.8 to 16.4‰) indicated the influence of human activities such as fertilizers, sewage, and manure in all sampling sites, including the control site far from the pollution sources and cannot differentiate the specific sources. The study underscores LC-HRMS's efficacy in different pollution sources, surpassing the limitations of stable N isotope analysis, and provides valuable insights for polluted groundwater source tracking strategies.

Construction and application of a QSRR approach for identifying flavonoids

A quantitative structure retention relationship (QSRR) method was developed to identify flavonoid isomers auxiliary using an ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method based on the linear relationships between the Ln(k') values of flavonoids and their hydrogen bonding energy (XAH) and dissolution energy (ES). Chromatographic separation was achieved with a Hypersil GOLD C18 (100 mm × 2.1 mm, 1.9 µm) column and Agilent SB-C18 (2.1 ×50 mm, 1.8 µm) column on a Dionex Ultimate 3000 RSLC chromatograph. Compounds were eluted isocratically using a mobile phase containing 0.1% formic acid/water solution and methanol at a ratio of 55:45 (v/v). Mass spectrometry was performed in the negative and positive ionization modes on a Thermo Fisher Q Exactive Orbitrap mass spectrometer equipped with an electrospray ionization interface. The established QSRR model was Ln(k') = 5.6163 + 0.0469ES - 0.0984XAH, with a determination coefficient (R2) of 0.9981, adjusted determination coefficient (adjR2) of 0.9976, and corrected root mean square error of 0.0682. The determination coefficient of the leave-one-out (LOO) cross-validation (Q2LOO) was 0.9976, and the cross-verification root mean square error was 0.0754. Simulated samples containing 7 flavonoids were used to validate the feasibility of the method. The classical method (UHPLC-MS/MS combined the CD software and the mzCloud, mzVault and Chemspider databases) was used to identify the seven flavonoids in the simulated samples. This classic identification strategy cannot provide accurate identification results, which provided multiple identification results for each compound in the simulated samples. On the basis of the results, the 7 flavonoids were accurately identified by the established QSRR model, and the reference standards were used to validate it. The relative error of retention time(RE(tR)) between the model calculation and experimental results was less than 10%. This method effectively complements and improves the classical methods, that UHPLC-MS/MS combined the CD software and the mass spectra databases were used to identify flavonoids identification.

Mechanistic Chromatographic Column Characterization for the Analysis of Flavonoids Using Quantitative Structure-Retention Relationships Based on Density Functional Theory

This work aimed to unravel the retention mechanisms of 30 structurally different flavonoids separated on three chromatographic columns: conventional Kinetex C18 (K-C18), Kinetex F5 (K-F5), and IAM.PC.DD2. Interactions between analytes and chromatographic phases governing the retention were analyzed and mechanistically interpreted via quantum chemical descriptors as compared to the typical 'black box' approach. Statistically significant consensus genetic algorithm-partial least squares (GA-PLS) quantitative structure retention relationship (QSRR) models were built and comprehensively validated. Results showed that for the K-C18 column, hydrophobicity and solvent effects were dominating, whereas electrostatic interactions were less pronounced. Similarly, for the K-F5 column, hydrophobicity, dispersion effects, and electrostatic interactions were found to be governing the retention of flavonoids. Conversely, besides hydrophobic forces and dispersion effects, electrostatic interactions were found to be dominating the IAM.PC.DD2 retention mechanism. As such, the developed approach has a great potential for gaining insights into biological activity upon analysis of interactions between analytes and stationary phases imitating molecular targets, giving rise to an exceptional alternative to existing methods lacking exhaustive interpretations.

Impact of metal ionic characteristics on adsorption potential of Ficus carica leaves using QSPR modeling

The present study describes Quantitative Structure Property Relationship (QSPR) modeling to relate metal ions characteristics with adsorption potential of Ficus carica leaves for 13 selected metal ions (Ca⁺², Cr⁺³, Co⁺², Cu⁺², Cd⁺², K⁺¹, Mg⁺², Mn⁺², Na⁺¹, Ni⁺², Pb⁺², Zn⁺², and Fe⁺²) to generate QSPR model. A set of 21 characteristic descriptors were selected and relationship of these metal characteristics with adsorptive behavior of metal ions was investigated. Stepwise Multiple Linear Regression (SMLR) analysis and Artificial Neural Network (ANN) were applied for descriptors selection and model generation. Langmuir and Freundlich isotherms were also applied on adsorption data to generate proper correlation for experimental findings. Model generated indicated covalent index as the most significant descriptor, which is responsible for more than 90% predictive adsorption (α = 0.05). Internal validation of model was performed by measuring [Formula: see text] (0.98). The results indicate that present model is a useful tool for prediction of adsorptive behavior of different metal ions based on their ionic characteristics.

QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression

Modified quantitative structure retention relationships (QSRRs) are proposed and applied to describe two retention data sets: A set of 94 metabolites studied by a hydrophilic interaction chromatography system under organic content gradient conditions and a set of tryptophan and its major metabolites analyzed by a reversed-phase chromatographic system under isocratic as well as pH and/or simultaneous pH and organic content gradient conditions. According to the proposed modification, an additional descriptor is added to a conventional QSRR expression, which is the analyte retention time, tR(R), measured under the same elution conditions, but in a second chromatographic column considered as a reference one. The 94 metabolites were studied on an Amide column using a Bare Silica column as a reference. For the second dataset, a Kinetex EVO C18 and a Gemini-NX column were used, where each of them was served as a reference column of the other. We found in all cases a significant improvement of the performance of the QSRR models when the descriptor tR(R) was considered.

Retention time prediction for dereplication of natural products (CxHyOz) in LC-MS metabolite profiling

The detection and early identification of natural products (NPs) for dereplication purposes require efficient, high-resolution methods for the profiling of crude natural extracts. This task is difficult because of the high number of NPs in these complex biological matrices and because of their very high chemical diversity. Metabolite profiling using ultra-high pressure liquid chromatography coupled to high-resolution mass spectrometry (UHPLC–HR-MS) is very efficient for the separation of complex mixtures and provides molecular formula information as a first step in dereplication. This structural information alone or even combined with chemotaxonomic information is often not sufficient for unambiguous metabolite identification. In this study, a representative set of 260 NPs containing C, H, and O atoms only was analysed in generic UHPLC–HR-MS profiling conditions. Two easy to use quantitative structure retention relationship (QSRR) models were built based on the measured retention time and on eight simple physicochemical parameters calculated from the structures. First, an original approach using several partial least square (PLS) regressions according to the phytochemical classes provided satisfactory results with an easy calculation. Secondly, a unique artificial neural network (ANN) model provided similar results on the whole set of NPs but required dedicated software. The retention prediction methods described in this study were found to improve the level of confidence of the identification of given analytes among putative isomeric structures. Its applicability was verified for the dereplication of NPs in model plant extracts.

A single step reversed-phase high performance liquid chromatography separation of polar and non-polar lipids

This paper reports a simple chromatographic system to separate lipids classes as well as their molecular species. By the use of phenyl coated silica as stationary phase in combination with a simple mobile phase consisting of methanol and water, all tested lipid classes elute within 30 min. Furthermore, a method to accurately predict retention times of specific lipid components for this type of chromatography is presented. Common detection systems were used, namely evaporative light scattering detection (ELSD), charged aerosol detection (CAD), electrospray mass spectrometry (ESI-MS), and UV detection.