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The present study investigated the volatile constituents of Cymbopogon citratus (lemongrass) grown in a greenhouse environment in Serbia, marking the first commercial cultivation of the plant for essential oil production in the region. The essential oils and hydrolates obtained through steam distillation were analyzed via gas chromatography–mass spectrometry (GC-MS), and the resulting chemical data were further processed using chemometric methods. This study applied quantitative structure retention relationship (QSRR) analysis, employing molecular descriptors (MDs) and artificial neural networks (ANNs) to predict the retention indices (RIs) of the compounds. A genetic algorithm (GA) was used to select the most relevant MDs for this predictive modeling. A total of 29 compounds were annotated in the essential oils, with geranial and neral being the dominant components, while 37 compounds were detected in the hydrolates. The ANN models effectively predicted the RIs of both essential oils and hydrolates, demonstrating high statistical accuracy and low prediction errors. This research offers valuable insights into the chemical profile of lemongrass cultivated in temperate conditions and advances QSRR modeling for essential oil analysis. [ABSTRACT FROM AUTHOR]

The present study investigated the volatile constituents of Cymbopogon citratus (lemongrass) grown in a greenhouse environment in Serbia, marking the first commercial cultivation of the plant for essential oil production in the region. The essential oils and hydrolates obtained through steam distillation were analyzed via gas chromatography–mass spectrometry (GC-MS), and the resulting chemical data were further processed using chemometric methods. This study applied quantitative structure retention relationship (QSRR) analysis, employing molecular descriptors (MDs) and artificial neural networks (ANNs) to predict the retention indices (RIs) of the compounds. A genetic algorithm (GA) was used to select the most relevant MDs for this predictive modeling. A total of 29 compounds were annotated in the essential oils, with geranial and neral being the dominant components, while 37 compounds were detected in the hydrolates. The ANN models effectively predicted the RIs of both essential oils and hydrolates, demonstrating high statistical accuracy and low prediction errors. This research offers valuable insights into the chemical profile of lemongrass cultivated in temperate conditions and advances QSRR modeling for essential oil analysis.

Defining the interaction of newly synthesized compounds with plasma proteins is an important step in the drug development process. Chromatographic techniques can be successfully used in predicting the biopharmaceutical and pharmacokinetic properties of newly synthesized compounds. The aim of this study is to investigate and isolate the most important molecular properties that affect the interaction of 20 newly synthesized chalcones and commercial compounds (lopinavir, ritonavir, darunavir and ivermectin) with human serum albumin (HSA). The retention behaviour of the selected compounds was tested on a CHIRALPAK®HSA column. A mixture of phosphate buffer (pH 7.0) and isopropanol (80:20 volume ratio) was used as the mobile phase, and the support vector method was used to form the quantitative structure retention relationship (QSRR) model. Based on the obtained values of retention parameters, it was observed that halogenated derivatives show the strongest, and methylated chalcone derivatives the weakest interaction with HSA. By correlating the retention and physicochemical properties of the tested compounds, it was shown that the structural (SDSCH) and electronic properties (MAXQ, EEM_F1) groups have the greatest influence on the retention behaviour and the interaction of the tested compounds with HSA. The obtained QSRR model can be applied in the prediction of the retention characteristics of new, structurally related chalcone derivatives on HSA stationary phase.

There is a rising interest in the modeling and predicting of chromatographic retention. The progress towards more complex and comprehensive models emphasized the need for broad reliable datasets. The present dataset comprises small pharmaceutical compounds selected to cover a wide range in terms of physicochemical properties that are known to impact the retention in reversed-phase liquid chromatography. Moreover, this dataset was analyzed at five pH with two gradient slopes. It provides a reliable dataset with a diversity of conditions and compounds to support the building of new models. To enhance the robustness of the dataset, the compounds were injected individually, and each sequence of injections included a quality control sample. This unambiguous detection of each compound as well as a systematic analysis of a quality control sample ensured the quality of the reported retention times. Moreover, three different liquid chromatographic systems were used to increase the robustness of the dataset.

In this work, biopartitioning micellar liquid chromatography (BMLC) was used to the assessment of affinity binding of 13 pain‐relief drugs to human serum albumin (HSA) molecules. The values of BMLC retention factors were determined by aqueous CTAB solution as mobile phase. Then, these values were correlated to some molecular structural descriptors by using a quantitative structure retention relationship methodology. The statistical quality of the obtained models was evaluated by different validation tests. Also, selected descriptors were correlated with protein–drug binding values and resulted in correlation coefficients higher than 0.8. Results indicated that selected descriptors can address the most important structural features influencing the binding affinity of studied drugs to HSA. [ABSTRACT FROM AUTHOR]

Polychlorinated biphenyls belong to a class of hazardous and environmental pollutants. Gas chromatography separation and experimental relative retention time evaluation of these compounds on a poly (94% methyl/5% phenyl) silicone‐based capillary non‐bonded and cross‐linked column are time consuming and expensive. In this study, relative retention times were estimated using two‐dimensional images of molecules based on a newly implemented rapid and simple quantitative structure retention relationship methodology. The resulting descriptors were subjected to partial least square and principal component‐radial basis function neural networks as linear and nonlinear models, respectively, to attain a statistical explanation of the retention behavior of the molecules. The high numerical values of correlation coefficients and low root mean square errors in the case of the partial least square model, confirm the supremacy of this model as well as the linear dependency of images of molecules to their relative retention times. Evaluation of the best correlation model performed using internal and external tests and its good applicability domain was checked using a distance to the model in the X‐Space plot. This study provides a practical and effective method for analytical chemists working with chromatographic platforms to improve predictive confidence of studies that seek to identify unknown molecules or impurities. [ABSTRACT FROM AUTHOR]

High-resolution mass spectrometry (HRMS) provides a powerful tool for the rapid analysis and identification of compounds in herbs. However, the diversity and large differences in the content of the chemical constituents in herbal medicines, especially isomerisms, are a great challenge for mass spectrometry-based structural identification. In the current study, a new strategy for the structural characterization of potential new phthalide compounds was proposed by isomer structure predictions combined with a quantitative structure-retention relationship (QSRR) analysis using phthalide compounds in Chuanxiong as an example. This strategy consists of three steps. First, the structures of phthalide compounds were reasonably predicted on the basis of the structure features and MS/MS fragmentation patterns: (1) the collected raw HRMS data were preliminarily screened by an in-house database; (2) the MS/MS fragmentation patterns of the analogous compounds were summarized; (3) the reported phthalide compounds were identified, and the structures of the isomers were reasonably predicted. Second, the QSRR model was established and verified using representative phthalide compound standards. Finally, the retention times of the predicted isomers were calculated by the QSRR model, and the structures of these peaks were rationally characterized by matching retention times of the detected chromatographic peaks and the predicted isomers. A multiple linear regression QSRR model in which 6 physicochemical variables were screened was built using 23 phthalide standards. The retention times of the phthalide isomers in Chuanxiong were well predicted by the QSRR model combined with reasonable structure predictions (R 2 = 0.955). A total of 81 peaks were detected from Chuanxiong and assigned to reasonable structures, and 26 potential new phthalide compounds were structurally characterized. This strategy can improve the identification efficiency and reliability of homologues in complex materials. [ABSTRACT FROM AUTHOR]

The review is mainly focused on application of thin layer chromatography (TLC) as simple, rapid and inexpensive method for lipophilicity assessment. Among separation techniques, TLC is still one of the most popular for lipophilicity measurement. The principles and methodology of Quantitative Structure Retention Relationship (QSRR) employed to lipophilicity prediction from retention data are presented. Moreover, applications of TLC retention constants in Quantitative Structure Activity Relationship (QSAR) studies were critically overviewed. The paper concerns also bioautography as a TLC method complementary to QSAR studies. In the article, the advantages and limitations of well established and less common planar chromatography modes applied for drug discovery process were discussed. [ABSTRACT FROM AUTHOR]

[Display omitted] • QSRR modeling of retention in the MEKC-CTAB system. • Compression of GA-PLS and GA-SVM to support the prediction of retention. • Insights into the molecular mechanism of retention in the MEKC-CTAB system. Capillary electrophoresis (CE) is an analytical technique widely applied in clinical, industrial, and scientific laboratories. Discussion of scientists' and specialists' concerns regarding the superiority of CE and more frequently used high-performance liquid chromatography (HPLC) is well known. With several advantages like a short analysis time, high efficiency, and low reagents consumption (mostly organic solvent), CE is considered a "green" alternative to HPLC. The relationship between retention and molecule structure has paid attention practically from the very beginning of separation methods. It can be established using a quantitative structure retention relationship (QSRR) method. The main goal of our investigation is to fill the gap related to QSRR analyses for the cetrimonium bromide (CTAB) micellar electrokinetic chromatography (MEKC) system using a heterogeneous set of 89 model molecules. The genetic algorithm (GA) supported the selection of theoretical descriptors that quantitatively describe target solutes. Comparison of linear and non-linear algorithms has been performed. Finally, QSRR models using partial least squares regression (PLS) and support vector regression (SVR) have been developed, and their performances were evaluated. The obtained results clearly indicate that machine learning (ML) models can be used as supportive tools in predicting retention in the CTAB-MEKC system. Among investigated models, the best performance showed GA-PLS. This finding has been proved by leave-one-out cross-validation and external validation procedures. Notably, the established models also give a view into the molecular mechanism of interaction between molecules and CTAB-formed micelles. Our investigations confirmed that descriptors related to the lipophilicity of molecules are the most significant factors in these types of interactions. [ABSTRACT FROM AUTHOR]

Small peptides such as dipeptides and tripeptides show various biological activities in organisms. However, methods for identifying dipeptides/tripeptides from complex biological samples are lacking. Here, an annotation strategy involving the derivatization of dipeptides and tripeptides via dansylation was suggested based on liquid chromatography-mass spectrometry (LC-MS) and iterative quantitative structure retention relationship (QSRR) to choose dipeptides/tripeptides by using a small number of standards. First, the LC-autoMS/MS method and initial QSRR model were built based on 25 selected grid-dipeptides and 18 test-dipeptides. To achieve high-coverage detection, dipeptide/tripeptide pools containing abundant dipeptides/tripeptides were then obtained from four dansylated biological samples including serum, tissue, feces, and soybean paste by using the parameter-optimized LC-autoMS/MS method. The QSRR model was further optimized through an iterative train-by-pick strategy. Based on the specific fragments and t R tolerances, 198 dipeptides and 149 tripeptides were annotated. The dipeptides at lower annotation levels were verified by using authentic standards and grid-correlation analysis. Finally, variation in serum dipeptides/tripeptides of three different liver diseases including hepatitis B infection, liver cirrhosis, and hepatocellular carcinoma was characterized. Dipeptides with N-prolinyl, C-proline, N-glutamyl, and N-valinyl generally increased with disease severity. In conclusion, this study provides an efficient strategy for annotating dipeptides/tripeptides from complex samples.

High resolution gas chromatographic relative retention time (HRGC-RRT) models were developed to predict relative retention times of the 209 individual polychlorinated biphenyls (PCBs) congeners. To estimate and predict the HRGC-RRT values of all PCBs on 18 different stationary phases, a multiple linear regression equation of the form RRT = a + a (no. o-Cl) + a (no. m-Cl) + a (no. p-Cl) + a ( V or S) was used. Molecular descriptors in the models included the number of ortho-, meta-, and para-chlorine substituents (no. o-Cl, m-Cl and p-Cl, respectively), the semi-empirically calculated molecular volume ( V), and the molecular surface area ( S). By means of the final variable selection method, four optimal semi-empirical descriptors were selected to develop a QSRR model for the prediction of RRT in PCBs with a correlation coefficient between 0.9272 and 0.9928 and a leave-one-out cross-validation correlation coefficient between 0.9230 and 0.9924 on each stationary phase. The root mean squares errors over different 18 stationary phases are within the range of 0.0108–0.0335. The accuracy of all the developed models were investigated using cross-validation leave-one-out (LOO), Y-randomization, external validation through an odd–even number and division of the entire data set into training and test sets. [ABSTRACT FROM AUTHOR]

In this work, the liquid chromatographic retention times of some organic pollutants were modeled and predicted by the quantitative structure retention relationship (QSRR) approach. The data set consists of the retention times of 36 organic pollutants. The genetic algorithm-partial least square (GA-PLS) was used as a featured selection technique; the artificial neural network (ANN) and the support vector machine (SVM) were used for generation of the QSRR models. Descriptors which were selected by GA-PLS are mean atomic Van der Waals volume, molecular weight, number of double bonds, number of acceptor atom for H-bond, and topographic electronic descriptor. These descriptors were used as inputs for developed ANN and SVM models. After generation and optimization of ANN and SVM models, the models were used to calculate the retention time for internal test set. The root mean square errors of the GA-ANN model were 0.89 and 1.22 and the root mean squares of the GA-SVR model were 3.08 and 1.67 for training and test sets, respectively. Also, for the further evaluation of the credibility of the models, the leave-seven-out cross validation test was done. The statistical parameters of these tests were Q2 = 0.905 and SPRESS = 7.5 for the GA-ANN model and Q2 = 0.690 and SPRESS = 6.55 for the GA-SVR model. These results reveal the suitability of ANN in the prediction of liquid chromatographic retention times of organic pollutants. [ABSTRACT FROM AUTHOR]

A new quantitative structure–property relationship (QSRR) eight parameter correlation ( R = 0.998) of gas chromatographic retention times for a diverse set of 35 petroleum condensate components was developed by application of multiple linear regression analysis (MLR). The descriptors are all calculated directly from the molecular structure using Dragon software. A boiling point-based model ( R = 0.999) of a subset of 19 components was used as assistance. The predictive ability of both models was tested for some unknown components. The obtained model was used for interpretation of the retention behaviour of the investigated petroleum components. [ABSTRACT FROM AUTHOR]

Abstract: A quantitative structure retention relationship technique using an artificial neural network (ANN) has been used for the prediction of the Lee retention indices for PAHs on SE-52 and DB-5 stationary phases. The selected descriptors that appear in the ANN model are the boiling point, molecular weight, connectivity index and the Schabron molecular size descriptor. The network was trained and optimized using a training and validation data sets. For the evaluation of the predictive power of the ANN, the optimized network was used to predict the temperature-programmed Lee retention indices of two unseen testing data sets. The results obtained showed that the mean of relative errors and the correlation coefficients between the calculated ANN and the experimental values of Lee retention indices for the validation and two testing sets are 1.42% and 0.9460 on SE-52; 1.32% and 0.9381; 1.43% and 0.8939 on DB-5 stationary phases, respectively. These values are in good agreement with the relative error obtained by experiment. [Copyright &y& Elsevier]

This paper describes the results of the evaluation of a new Log( P) model for reversed- phase ion-pair chromatography (RP-IPC) by linear gradient elution. In the model (Eq. (7)), the retention time ( t ) is related to the molecular descriptors as: t = The first four terms describe the contribution to retention from neutral components of solutes, and the fifth term represents the contribution to retention from solute’s ionization. The last term describes the retention increase due to ion-pair effect. Retention times obtained for 60 solutes (neutral, acidic and basic) in acetonitrile/aqueous mobile phases with different ion-pair reagents (phosphoric acid, trifluoroacetic acid, heptafluorobutyric acid, perchloric acid, and hexafluorophosphoric acid) on C18 column are used to evaluate the capability of the function. It is concluded that the model describes the retention of neutral and ionizable/ionized compounds very well with and without ion-pair condition. Accordingly, the function can be used to predict gradient retention for both neutral and ionizable compounds in RP-IPC to assist in chromatographic optimization, including selectivity optimization and internal standard selection. [ABSTRACT FROM AUTHOR]

Various quantitative structure retention relationships have been published in an effort to understand and predict chromatographic retention times. This work presents a chlorine substitution pattern contribution (Cl-SPC) model for relative retention times (RRT) of polychlorinated biphenyls (PCBs), using 27 sets of previously published gas chromatography RRT data. The Cl-SPC model calculates the contribution factors (βk) for each of 19 chlorine substitution "patterns" (such as 2-, 2,4-, 2,3,6-, 2,3,4,5,6-, etc.) using multiple linear regression (MLR). The 27 separate MLRs had R(2) values ranging from 0.961 to 1.000; the average absolute errors were 0.55% for the training sets and 0.95% for the test sets. Cross-validation of the model was carried out by splitting each data set into training and test sets for groupings based on nine PCB congener mixes commercialized by AccuStandard. No weakening of the model performance was observed when the size of data set used to develop the model was decreased from 209 to 39 congeners. In addition to the separate models, a single mixed model was fit combining all 27 data sets. The estimated random effects, which reflect the impact of GC configuration and operational conditions on RRTs, are minor compared with the fixed effects estimated for the βk values. The major advantages of the Cl-SPC model are its unmatched simplicity and equally excellent robustness when compared with other quantitative structure retention relationship models., (Copyright © 2015 Elsevier B.V. All rights reserved.)

The lack of authentic standards represents a major bottleneck in the quantitative analysis of complex samples. Here we propose a quantitative structure and ionization intensity relationship (QSIIR) approach to predict the absolute levels of compounds in complex matrixes. An absolute quantitative method for simultaneous quantification of 25 organic acids was firstly developed and validated. Napierian logarithm (LN) of the relative slope rate derived from the calibration curves was applied as an indicator of the relative ionization intensity factor (RIIF) and serves as the dependent variable for building a QSIIR model via a multiple linear regression (MLR) approach. Five independent variables representing for hydrogen bond acidity, HOMO energy, the number of hydrogen bond donating group, the ratio of organic phase, and the polar solvent accessible surface area were found as the dominant contributors to the RIIF of organic acids. This QSIIR model was validated to be accurate and robust, with the correlation coefficients (R(2)), R(2) adjusted, and R(2) prediction at 0.945, 0.925, and 0.89, respectively. The deviation of accuracy between the predicted and experimental value in analyzing a real complex sample was less than 20% in most cases (15/18). Furthermore, the high adaptability of this model was validated one year later in another LC/MS system. The QSIIR approach is expected to provide better understanding of quantitative structure and ionization efficiency relationship of analogous compounds, and also to be useful in predicting the absolute levels of analogous analytes in complex mixtures., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Identification of nontarget compounds in complex mixtures is of significant importance in various scientific fields. On the basis of the universal property that the compounds in complex mixtures can be classified to various analogous families, this study presents a general strategy for the rapid identification of nontarget compounds from complex matrixes using herbal medicine as an example. The proposed strategy consists of three sequential steps. First, a blank control sample is prepared for the purpose of removing interferences in the complex matrixes via automatic chromatographic and mass spectrometric data comparisons. Second, the diagnostic ions guided bridging network strategy is developed for the rapid classification of analogous compounds and structural characterizations. Finally, a quantitative structure retention relationship (QSRR) is built to validate the identifications and to differentiate isomers. Using this strategy, we have successfully identified a total of 45 organic acids from Mai-Luo-Ning and Flos Lonicerae injection, and 46 ginsenosides from Shen-Mai injection samples. The QSRR approach enabled a successful differentiation of most isomers. The proposed strategy will be expected to be applicable to the identification of nontarget compounds in complex mixtures., (Copyright © 2013 Elsevier B.V. All rights reserved.)