QSPR analysis for the retention index of flavors and fragrances on a OV-101 column

Rojas, Cristián Guerra; Duchowicz, Pablo R.; Tripaldi, Piercósimo; Diez, Reinaldo Pis

doi:10.1016/j.chemolab.2014.09.020

Cited by 38 publications

(35 citation statements)

References 43 publications

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“…In addition, the model presented here could also be used for the quality control of pepper in such a way as to identify contaminants with a defined molecular scaffold (Rojas et al., ). Finally, we have demonstrated elsewhere that the retention index property of volatile organic compounds measured in stationary phases of different polarity (for instance OV‐101, Carbowax 20M, DB‐225MS, HP5‐MS, HP‐1, and DVB‐CAR‐PDMS) is well predicted by the use of a conformation‐independent molecular representation only (Rojas et al., , , , ).…”

Section: Resultsmentioning

confidence: 94%

“…In addition, it has been stated that the split of a dataset should be done in order to guarantee similar structure-property relationships (Marrero-Ponce et al, 2018;Martin et al, 2012;Rojas et al, 2015a), in such a way that the space defined by the VOCs of the training set should be representative of the validation and test set molecules for cross-validation and prediction purposes, respectively. Thus, we used the Balanced Subsets Method (BSM) (Rojas et al, 2015a) based on the k-means cluster analysis (k-MCA) for the partition of the dataset. This procedure was applied elsewhere for studying the retention index property of several VOCs in stationary phases of different polarities (Rojas et al, 2015a(Rojas et al, , 2015b(Rojas et al, , 2017(Rojas et al, , 2018.…”

Section: Partition Of the Datasetmentioning

confidence: 99%

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Foodinformatics: Quantitative Structure‐Property Relationship Modeling of Volatile Organic Compounds in Peppers

Rojas

Duchowicz

Castro

2019

Journal of Food Science

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The aim of this work was the foodinformatic (chemoinformatic) modeling of volatile organic compounds (VOCs) of different samples of peppers based on a quantitative structure‐property relationship (QSPR) for the retention indices of 273 identified compounds. The experimental retention indices were measured by means of comprehensive two‐dimensional gas chromatography combined with quadrupole‐mass spectrometry (GC × GC/qMS) using the BPX5 and BP20 column coupled system. All the VOCs were represented by means of both conformation‐independent molecular descriptors and molecular fingerprints calculated in the Dragon and PaDEL‐Descriptor software. The dataset was divided into training, validation and test sets of molecules according to the Balanced Subsets Method (BSM). Subsequently, the V‐WSP unsupervised variable reduction method was used to reduce the presence of multicollinearity, redundancy, and noise in the initial pool of 4,336 molecular descriptors and fingerprints. Using this method, a reduced pool of 1,664 was submitted to the supervised selection by means of the replacement method (RM) variable subset selection in order to define a four‐descriptor model. The quality of the model was measured by means of the coefficient of determination and the root‐mean‐square deviation in fitting (Rtrain2=0.879 and italicRMSDitalictrain=72.1), validation (Rval2=0.832 and italicRMSDitalicval=91.7), and prediction (Rtest2=0.915 and italicRMSDitalictest=55.4). The negligible differences among the parameters in the three sets indicate a stable and predictive QSPR model. This quantitative structure‐activity relationship was developed keeping in mind the five principles defined by the Organization for Economic Co‐operation and Development (OECD) to make it applicable. Practical Application This predictive mathematical model developed from the retention indices of 273 volatile organic compounds (VOCs) detected in pepper samples could be useful for chromatographers working on the identification of other common VOCs in peppers or other foods by means of comprehensive two‐dimensional gas chromatography combined with quadrupole‐mass spectrometry (GC × GC/qMS) using a bi‐dimensional stationary phase coupled system (BPX5 and BP20).

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Section: Resultsmentioning

confidence: 94%

Section: Partition Of the Datasetmentioning

confidence: 99%

Foodinformatics: Quantitative Structure‐Property Relationship Modeling of Volatile Organic Compounds in Peppers

Rojas

Duchowicz

Castro

2019

Journal of Food Science

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“…Randomly splitting a dataset may not lead to rational solutions unless the generated sets have similar structure‐activity relationships. To this end the split of the dataset is carried out by the Balanced Subsets Method (BSM), a technique based on k‐Means Cluster Analysis (k‐MCA). The procedure of BSM ensures that the training set is representative of the validation and test sets.…”

Section: Methodsmentioning

confidence: 99%

Conformation‐Independent QSAR Study on Human Epidermal Growth Factor Receptor‐2 (HER2) Inhibitors

et al. 2017

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Inhibition of HER2 (human epidermal growth factor receptor 2) expression and function is required in several cancer treatments. Numerous compounds with very different molecular structures have been suggested as HER2 inhibitors. Here we perform quantitative structure‐activity relationship (QSAR) analysis on 444 of such compounds to investigate the molecular properties that may influence its efficiency. Models based on 1D and 2D flexible molecular descriptors are proposed to develop simple models based solely on constitutional and topological molecular features. A large number of non‐conformational descriptors (17974) was used to thoroughly explore the structural characteristics that influence the HER2 inhibitory activity. Three different approaches were explored using: 1) Molecular Descriptors, 2) Flexible Molecular Descriptors, and 3) Hybrid Descriptors. A QSAR model for HER2 inhibitors was successfully developed. Some properties such as electronegativity, aromatic character, and the presence of amino groups appear as molecular characteristics that may have influence in the HER2 inhibitory activity.

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“…The data-set was divided into a training set of 63 and a test set of 30 chalcones by applying a k-means cluster analysis [13], in order to have representative molecules of the complete dataset in both training and test sets. The basis of the k-means cluster analysis is to create k clusters or groups of molecules, in such a way that compounds in the same cluster are very similar in terms of a distance metrics and compounds in other clusters are very different; details of the procedure have been presented elsewhere [14].…”

Section: Data Setsmentioning

confidence: 99%

Chalcone derivative cytotoxicity activity against MCF-7 human breast cancer cell QSAR study

Dimić

Mercader

Castro

2015

Chemometrics and Intelligent Laboratory Systems

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QSPR analysis for the retention index of flavors and fragrances on a OV-101 column

Cited by 38 publications

References 43 publications

Foodinformatics: Quantitative Structure‐Property Relationship Modeling of Volatile Organic Compounds in Peppers

Foodinformatics: Quantitative Structure‐Property Relationship Modeling of Volatile Organic Compounds in Peppers

Conformation‐Independent QSAR Study on Human Epidermal Growth Factor Receptor‐2 (HER2) Inhibitors

Chalcone derivative cytotoxicity activity against MCF-7 human breast cancer cell QSAR study

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