Combination of artificial neural network technique and linear free energy relationship parameters in the prediction of gradient retention times in liquid chromatography

Intelligent Recommendation Systems Powered by Consensus Neural Networks: The Ultimate Solution for Finding Suitable Chiral Chromatographic Systems?

The selection of suitable combinations of chiral stationary phases (CSPs) and mobile phases (MPs) for the enantioresolution of chiral compounds is a complex issue that often requires considerable experimental effort and can lead to significant waste. Linking the structure of a chiral compound to a CSP/MP system suitable for its enantioseparation can be an effective solution to this problem. In this study, we evaluate algorithmic tools for this purpose. Our proposed consensus model, which uses multiple optimized artificial neural networks (ANNs), shows potential as an intelligent recommendation system (IRS) for ranking chromatographic systems suitable for the enantioresolution of chiral compounds with different molecular structures. To evaluate the IRS potential in a proof-of-concept stage, 56 structural descriptors for 56 structurally unrelated chiral compounds across 14 different families are considered. Chromatographic systems under study comprise 7 cellulose and amylose derivative CSPs and acetonitrile or methanol aqueous MPs (14 chromatographic systems in all). The ANNs are optimized using a fit-for-purpose version of the chaotic neural network algorithm with competitive learning (CCLNNA), a novel approach not previously applied in the chemical domain. CCLNNA is adapted to define the inner ANN complexity and perform feature selection of the structural descriptors. A customized target function evaluates the correctness of recommending the appropriate CSP/MP system. The ANN-consensus model exhibits no advisory failures and requires only an experimental attempt to verify the IRS recommendation for complete enantioresolution. This outstanding performance highlights its potential to effectively resolve this problem.

Advances in AI-Driven Retention Prediction for Different Chromatographic Techniques: Unraveling the Complexity

Retention prediction through Artificial intelligence (AI)-based techniques has gained exponential growth due to their abilities to process complex sets of data and ease the crucial task of identification and separation of compounds in most employed chromatographic techniques. Numerous approaches were reported for retention prediction in different chromatographic techniques, and consistent results demonstrated that the accuracy and effectiveness of deep learning models outclassed the linear machine learning models, mainly in liquid and gas chromatography, as ML algorithms use fewer complex data to train and predict information. Support Vector machine-based neural networks were found to be most utilized for the prediction of retention factors of different compounds in thin-layer chromatography. Cheminformatics, chemometrics, and hybrid approaches were also employed for the modeling and were more reliable in retention prediction over conventional models. Quantitative Structure Retention Relationship (QSRR) was also a potential method for predicting retention in different chromatographic techniques and determining the separation method for analytes. These techniques demonstrated the aids of incorporating QSRR with AI-driven techniques acquiring more precise retention predictions. This review aims at recent exploration of different AI-driven approaches employed for retention prediction in different chromatographic techniques, and due to the lack of summarized literature, it also aims at providing a comprehensive literature that will be highly useful for the society of scientists exploring the field of AI in analytical chemistry.

Current trends in chromatographic prediction using artificial intelligence and machine learning

Artificial intelligence (AI) and machine learning (ML) gained tremendous growth and are rapidly becoming popular in various fields of prediction due to their potential abilities, accuracy, and speed. Machine learning algorithms employ historical data to analyze or predict information using patterns or trends. AI and ML were most employed in chromatographic predictions and particularly attractive options for liquid chromatography method development, as they can help achieve desired results faster, more accurately, and more efficiently. This review aims at exploring various AI and ML models employed in the determination of chromatographic characteristics. This review also aims to provide deep insight into reported artificial neural network (ANN) associated techniques which maintained better accuracy and significant possibilities for chromatographic characteristics prediction in liquid chromatography over classical linear models and also emphasizes the integration of a fuzzy system with an ANN, as this integrated study provides more efficient and accurate methods in chromatographic prediction than other linear models. This study also focuses on the retention prediction of a target molecule employing QSRR methodology combined with an ANN, highlighting a more effective technique than the QSRR alone. This approach showed the benefits of combining AI or ML algorithms with the QSRR to obtain more accurate retention predictions, emphasizing the potential of artificial intelligence and machine learning for overcoming adversities in analytical chemistry.

Transfer of gas chromatographic retention data among poly(siloxane) columns by quantitative structure-retention relationships based on molecular descriptors of both solutes and stationary phases

In the present study, computational molecular descriptors of 90 saturated esters and seven poly(siloxane) stationary phases with different polarity (SE-30, OV-7, DC-710, OV-25, XE-60, OV-225 and Silar-5CP) were combined into quantitative structure-retention relationship (QSRR) models aimed at predicting the Kováts retention indices (RIs) of the solutes. The molecular descriptors (174) of the stationary phases included in the models were computed using Dragon software from poly(siloxane) oligomers made of 20 siloxane units reflecting the nominal composition of the stationary phase, whereas 439 molecular descriptors were adopted to represent the esters. Different QSRR models were generated by means of Partial Least Squares (PLS) regression to assess the accuracy of this approach in predicting the RIs of unexplored solutes both in known and external stationary phases. After calibration of each PLS model, the descriptors were selected/discarded according to their relevance, evaluated by Covariance Selection (CovSel), and the PLS models were re-built, which resulted in a noticeable improvement of their predictive ability. Firstly, all the available data were equally divided into a training and a test set; the model built on the calibration set was used to predict the RIs of the validation observations. Successively, seven diverse PLS models were created following a "leave-one-column-out" fashion procedure, each one finalized to the estimation of the RIs of the 90 esters associated with a single stationary phase, whereas the calibration model was calculated on the remaining data. All the estimated models provided successful results on the external stationary phase, and predictive performance further increased after variable selection based on CovSel analysis. The final models provided a Root Mean Square Error in Cross Validation (RMSECV) in the range 12-20, a Root Mean Square Error in Prediction (RMSEP) in the range 11-26, and Mean Absolute Percentage Errors in Prediction (MAMEPs) in the range 0.7-1.5, revealing accurate cross-column prediction. Eventually, to test the robustness of the proposed approach, the 90 solutes were equally partitioned into a calibration and a test set and two further QSSR strategies were applied. The first PLS model was calibrated on all the seven stationary phases and the RIs of the 45 external solutes in the same seven columns were simultaneously predicted. The last QSRR approach followed a "leave-one-column-out" scheme and RI of 45 test solutes on an external stationary phase was predicted by a PLS model calibrated with the data of the 45 remaining solutes and the six left stationary phases. After selection of the significant molecular descriptors, PLS regression provided RMSECV values in the range 6-19, RMSEPs in the range 10-14, and MAPEPs in the range 0.9-2.4, revealing the suitability of the approach to deduce the RI of unknown solutes in uncharted stationary phases.

Modelling of multilinear gradient retention time of bio-sweetener rebaudioside A in HPLC analysis

Artificial neural network (ANN), as one of the artificial intelligence methods, has been widely using in HPLC studies for modelling purposes. Stevia rebaudiana is an important industrial plant due to its bio-sweetener molecule, rebaudioside-a, in its leaves. Although rebaudioside-a is up to 300-fold sweeter than sucrose, its calorie is almost zero. In this study, HPLC optimization of rebaudioside-a was studied and the optimization data based on multilinear gradient retention times were modelled by ANN. The input parameters were selected as concentrations, column temperatures, initial acetonitrile percentage for the first step of gradient elution, initial acetonitrile percentage for the second step of gradient elution, slope of acetonitrile, wavelengths, flow rates. The retention time was the output. Also, dried S. rebaudiana leaves were extracted and the concentrations were evaluated by HPLC. According to the ANN results, the most effective parameters on the prediction of non-linear gradient retention time for rebaudioside-a were found as flow rate and initial acetonitrile percentage for the second step of gradient. The best back propagation was selected as Levenberg-Marquardt algorithm. The highest rebaudioside-a level was found as 96.53 ± 6.36 μg mL^-1. ANN modelling methods can be used in preparative HPLC applications to estimate the retention time of steviol glycosides.

Prediction of liquid chromatographic retention time using quantitative structure-retention relationships to assist non-targeted identification of unknown metabolites of phthalates in human urine with high-resolution mass spectrometry

The non-targeted analysis and identification of contaminant metabolites such as metabolites of phthalates and their alternatives in human biofluid samples constitutes a growing research field in human biomonitoring because of their importance as biomarkers of human exposure to the parent compounds. High-resolution mass spectrometry (HRMS) combined with high-performance liquid chromatography (HPLC) can provide fast separation and sensitive analysis using this application. However, the diversity of potential metabolites, especially isomers, in human samples, makes mass spectrometry-based structural identification very challenging, even with high-resolution and accurate mass. In this study, we present a retention time (t_R) prediction model based on quantitative structure-retention relationship (QSRR). This model can predict the retention time of a given structure of phthalates including isomers. Twenty-three molecular descriptors were used in the development of the multivariate linear regression QSRR model. The regression coefficient (R²) between predicted and experimental retention times of 26 training set compounds was 0.9912. The combination of the retention time prediction model with identification via accurate mass search and target MS/MS spectrum interpretation can enhance the identification confidence in the lack of reference standards. Two previously unreported phthalate metabolites were identified in human urine, using this model. The results of this study showed that the developed QSRR model could be a useful tool to predict the retention times of unknown metabolites of phthalates and their alternatives in future non-targeted screening analysis. The concentration of these two unknown compounds was also estimated using a quantitative structure-ion intensity relationship (QSIIR) model.

Retention Modelling of Phenoxy Acid Herbicides in Reversed-Phase HPLC under Gradient Elution

Phenoxy acid herbicides are used worldwide and are potential contaminants of drinking water. Reversed phase high-performance liquid chromatography (RP-HPLC) is commonly used to monitor phenoxy acid herbicides in water samples. RP-HPLC retention of phenoxy acids is affected by both mobile phase composition and pH, but the synergic effect of these two factors, which is also dependent on the structure and pKa of solutes, cannot be easily predicted. In this paper, to support the setup of RP-HPLC analysis of phenoxy acids under application of linear mobile phase gradients we modelled the simultaneous effect of the molecular structure and the elution conditions (pH, initial acetonitrile content in the eluent and gradient slope) on the retention of the solutes. In particular, the chromatographic conditions and the molecular descriptors collected on the analyzed compounds were used to estimate the retention factor k by Partial Least Squares (PLS) regression. Eventually, a variable selection approach, Genetic Algorithms, was used to reduce the model complexity and allow an easier interpretation. The PLS model calibrated on the retention data of 15 solutes and successively tested on three external analytes provided satisfying and reliable results.

Characterisation of Gas-Chromatographic Poly(Siloxane) Stationary Phases by Theoretical Molecular Descriptors and Prediction of McReynolds Constants

Retention in gas–liquid chromatography is mainly governed by the extent of intermolecular interactions between the solute and the stationary phase. While molecular descriptors of computational origin are commonly used to encode the effect of the solute structure in quantitative structure–retention relationship (QSRR) approaches, characterisation of stationary phases is historically based on empirical scales, the McReynolds system of phase constants being one of the most popular. In this work, poly(siloxane) stationary phases, which occupy a dominant position in modern gas–liquid chromatography, were characterised by theoretical molecular descriptors. With this aim, the first five McReynolds constants of 29 columns were modelled by multilinear regression (MLR) coupled with genetic algorithm (GA) variable selection applied to the molecular descriptors provided by software Dragon. The generalisation ability of the established GA-MLR models, evaluated by both external prediction and repeated calibration/evaluation splitting, was better than that reported in analogous studies regarding nonpolymeric (molecular) stationary phases. Principal component analysis on the significant molecular descriptors allowed to classify the poly(siloxanes) according to their chemical composition and partitioning properties. Development of QSRR-based models combining molecular descriptors of both solutes and stationary phases, which will be applied to transfer retention data among different columns, is in progress.

Artificial Neural Network Prediction of Retention of Amino Acids in Reversed-Phase HPLC under Application of Linear Organic Modifier Gradients and/or pH Gradients

A multi-layer artificial neural network (ANN) was used to model the retention behavior of 16 o-phthalaldehyde derivatives of amino acids in reversed-phase liquid chromatography under application of various gradient elution modes. The retention data, taken from literature, were collected in acetonitrile⁻water eluents under application of linear organic modifier gradients ( gradients), pH gradients, or double pH/ gradients. At first, retention data collected in  gradients and pH gradients were modeled separately, while these were successively combined in one dataset and fitted simultaneously. Specific ANN-based models were generated by combining the descriptors of the gradient profiles with 16 inputs representing the amino acids and providing the retention time of these solutes as the response. Categorical "bit-string" descriptors were adopted to identify the solutes, which allowed simultaneously modeling the retention times of all 16 target amino acids. The ANN-based models tested on external gradients provided mean errors for the predicted retention times of 1.1% ( gradients), 1.4% (pH gradients), 2.5% (combined  and pH gradients), and 2.5% (double pH/ gradients). The accuracy of ANN prediction was better than that previously obtained by fitting of the same data with retention models based on the solution of the fundamental equation of gradient elution.

Chemometric-assisted method development in hydrophilic interaction liquid chromatography: A review

With an enormous growth in the application of hydrophilic interaction liquid chromatography (HILIC), there has also been significant progress in HILIC method development. HILIC is a chromatographic method that utilises hydro-organic mobile phases with a high organic content, and a hydrophilic stationary phase. It has been applied predominantly in the determination of small polar compounds. Theoretical studies in computer-aided modelling tools, most importantly the predictive, quantitative structure retention relationship (QSRR) modelling methods, have attracted the attention of researchers and these approaches greatly assist the method development process. This review focuses on the application of computer-aided modelling tools in understanding the retention mechanism, the classification of HILIC stationary phases, prediction of retention times in HILIC systems, optimisation of chromatographic conditions, and description of the interaction effects of the chromatographic factors in HILIC separations. Additionally, what has been achieved in the potential application of QSRR methodology in combination with experimental design philosophy in the optimisation of chromatographic separation conditions in the HILIC method development process is communicated. Developing robust predictive QSRR models will undoubtedly facilitate more application of this chromatographic mode in a broader variety of research areas, significantly minimising cost and time of the experimental work.