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

Zisi, Ch.; Sampsonidis, Ioannis; Fasoula, S.; Papachristos, Konstantinos; Witting, Michael; Gika, Helen; Nikitas, P.; Pappa-Louisi, A.

doi:10.3390/metabo7010007

Cited by 22 publications

(9 citation statements)

References 24 publications

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“…Zisi et al. [43] performed QSRR for 94 metabolite standards and their results indicated that the inclusion of RTs from a different chromatographic column as an additional descriptor improves prediction accuracy.…”

Section: Retention Time Prediction In Lc–ms‐based Metabolomicsmentioning

confidence: 99%

Current status of retention time prediction in metabolite identification

Witting

Böcker

2020

J of Separation Science

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Metabolite identification is a crucial step in nontargeted metabolomics, but also represents one of its current bottlenecks. Accurate identifications are required for correct biological interpretation. To date, annotation and identification are usually based on the use of accurate mass search or tandem mass spectrometry analysis, but neglect orthogonal information such as retention times obtained by chromatographic separation. While several tools are available for the analysis and prediction of tandem mass spectrometry data, prediction of retention times for metabolite identification are not widespread. Here, we review the current state of retention time prediction in liquid chromatography-mass spectrometry-based metabolomics, with a focus on publications published after 2010. K E Y W O R D Sliquid chromatography, mass spectrometry, metabolite identification, metabolomics, retention time prediction 1746www.jss-journal.com

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Section: Retention Time Prediction In Lc–ms‐based Metabolomicsmentioning

confidence: 99%

Current status of retention time prediction in metabolite identification

Witting

Böcker

2020

J of Separation Science

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“…[ 68 ]. The connection between the molecular descriptors and the retention time can be established by artificial neural network, machine learning algorithms [ 69 , 70 , 71 , 72 , 73 ] or by boosted trees regression (BRT) [ 74 ].…”

Section: Discussionmentioning

confidence: 99%

Chemical Characterization of Marrubium vulgare Volatiles from Serbia

Aćimović

Ivanović

Simić

et al. 2021

Plants

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Marrubium vulgare is a cosmopolitan medicinal plant from the Lamiaceae family, which produces structurally highly diverse groups of secondary metabolites. A total of 160 compounds were determined in the volatiles from Serbia during two investigated years (2019 and 2020). The main components were E-caryophyllene, followed by germacrene D, α-humulene and α-copaene. All these compounds are from sesquiterpene hydrocarbons class which was dominant in both investigated years. This variation in volatiles composition could be a consequence of weather conditions, as in the case of other aromatic plants. According to the unrooted cluster tree with 37 samples of Marrubium sp. volatiles from literature and average values from this study, it could be said that there are several chemotypes: E-caryophyllene, β-bisabolene, α-pinene, β-farnesene, E-caryophyllene + caryophyllene oxide chemotype, and diverse (unclassified) chemotypes. However, occurring polymorphism could be consequence of adaptation to grow in different environment, especially ecological conditions such as humidity, temperature and altitude, as well as hybridization strongly affected the chemotypes. In addition, this paper aimed to obtain validated models for prediction of retention indices (RIs) of compounds isolated from M. vulgare volatiles. A total of 160 experimentally obtained RIs of volatile compounds was used to build the prediction models. The coefficients of determination were 0.956 and 0.964, demonstrating that these models could be used for predicting RIs, due to low prediction error and high r2.

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“…In a study by Tropsha and Golbraikh 18 , the numerical model that represents the relation between the molecular descriptors and the retention time can be established by numerous machine learning algorithms, or by using the artificial neural network (ANN), which is used in this study as it has already been proven to be an excellent tool in published literature. 11,19 The aim of this paper was to establish a new QSRR model for predicting the retention times of chemical compounds in A. clypeolata essential oil obtained by hydrodistillation and analyzed by GC-MS using the genetic algorithm (GA) variable selection method and the artificial neural network (ANN) model.…”

Section: O N L I N E F I R S Tmentioning

confidence: 99%

Achillea clypeolata Sibth. & Sm. essential oil composition and QSRR model for predicting retention indices

Aćimović

Pezo

Cvetković

et al. 2021

JSCS

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The aim of this study was the prediction model of retention indices of compounds from the aboveground parts of Achillea clypeolata Sibth. & Sm. essential oil, obtained by hydrodistillation and analysed by GC-MS. The quantitative structure-retention relationship analysis was applied in order to anticipate the retention time of the obtained compounds. The selection of the seven molecular descriptors was done by a genetic algorithm. The chosen descriptors were uncorrelated and were used to construct an artificial neural network. A total of 40 experimentally obtained retention indices was used to build this prediction model. The coefficient of determination for the training, testing and validation cycles were: 0.950, 0.825 and 1.000, respectively, indicating that this model could be used for prediction of retention indices for A. clypeolata, essential oil compounds.

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

Cited by 22 publications

References 24 publications

Current status of retention time prediction in metabolite identification

Current status of retention time prediction in metabolite identification

Chemical Characterization of Marrubium vulgare Volatiles from Serbia

Achillea clypeolata Sibth. & Sm. essential oil composition and QSRR model for predicting retention indices

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