Such methods employ regression models, harnessing machine learning algorithms mapping experimentally derived t\({}_{R}\) analytes with various structural and physicochemical descriptors, known as quantitative structure retention relationships (QSRR) models. In this study, 12 Machine Learning (ML) Regression algorithms, namely Multiple Linear, Lasso, Ridge, Bayesian Ridge, XGradientBoost, AdaBoost, Random Forest, Partial Least Squares, Support Vector Machine (linear/non-linear), Passive Aggressive, and ElasticNet, were tested for their ability to perform t\({}_{R}\) predictions. Each algorithm was evaluated on publicly available RP LC-MS retention data sets [1,2] using various experimental setups and on experimental chromatographic data (?900 metabolites), including Merck/MSMLS library, analysed in different chromatographic columns. For each analyte data set, a total of 200 Molecular Descriptors (MDs, features) were calculated using the RDKit library (open-source ChemInformatics software). Finally, through a filtering process (e.g., removing features showing low variance among molecules or a correlation greater than a certain limit), the final set of features for each data set was determined. A number of feature subsets were defined and evaluated, based on both the importance of the features in the ML algorithm used, and on the application of a variety of Genetic Algorithm techniques. Additional QSRR models were developed by using dimensionality reduction techniques (such as Principal Component Analysis and Partial Least Squares Regression), on the original set of features. All analyses were performed using Python programming language. Our study shows that each algorithm performance strongly depends on the dataset used, making the necessity for an initial performance inspection of the algorithms when dealing with new sets of data clear, in order to attain the best prediction results possible. This research is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme \"Human Resources Development, Education and Lifelong Learning 2014-2020\" in the context of the project \"Metabolomics analysis. New methods for improved identification and qualification of biomarkers\" (MIS 5047879). ## References  
* Bouwmeester et al. 2019 Bouwmeester, R.; Martens, L.; Degroeve, S. Comprehensive and Empirical Evaluation of Machine Learning Algorithms for Small Molecule LC Retention Time Prediction. _Anal. Chem._**2019**, _91_, 3694 -3703. * Folberth et al. 2020 Folberth, J.; Begemann, K.; Johren, O.; Schwaninger, M.; Othman, A. MS2 and LC libraries for untargeted metabolomics: Enhancing method development and identification confidence. _J. Chromatogr. B_**2020**, _1145_, 122105. * Fabrication of a Novel Polydopamine-Modified Luffa Sponge for the Sorption-Based Extraction in Sample Pretreatment
* Anagnostou and Stalikas 1995**P. Anagnostou, C. Stalikas
* and T. Chatzimitakos Laboratory of Analytical Chemistry, Department of Chemistry, University of Ioannina, 45110 Ioannina, Greece
* Correspondence: cstalika@uoi.gr  
Luffa sponge is a natural and biodegradable material, commonly referred to as vegetable sponge (Figure 28  
**Reference**  
1. Chatzimitakos, T.G. ; Stalikas, C.D. Sponges and sponge-like materials in sample preparation: A journey from past to present and into the future. _Molecules_**2020**, _25_, 3673. _16.17. Development of Chromatographic Conditions for Separation of Fifteen Phenolic Acids with the Use of Silica-Based Monolithic Columns_  
**M. Staniak \({}^{\star}\), M. Wojciak, S. Dresler and J. Sawicki**  
Department of Analytical Chemistry, Medical University of Lublin, Chodzki 4a, 20093 Lublin, Poland  
\({}^{\star}\) Correspondence: michalstaniak@umlub.pl  
High performance liquid chromatography (HPLC) is one of the most common analytical techniques used for the analysis of plant secondary metabolites. The huge diversity of packing materials for chromatographic columns enables the selection of the appropriate column for a specific analytical purpose.