Determination of Ethanol and Methyl Tert-Butyl Ether (MTBE) in Gasoline by NIR–AOTF-based Spectroscopy and Multiple Linear Regression with Variables Selected by Genetic Algorithm

This paper describes a near infrared spectroscopic method developed for determination of ethanol and methyl tert-butyl ether (MTBE) as additives in gasoline. The methodology employs data collected from a near infrared spectrophotometer whose monochromator is an Acousto-Optic Tunable Filter (AOTF) operating in the 1500–2400 nm range. Genetic Algorithm variable selection was used in the multiple linear regression (MLR) modelling. Seven wavelengths were selected by the algorithm and the results obtained by MLR revealed that the method produces improved results, when compared with the PLS regression method, as confirmed by the lower RMSEP obtained for ethanol and MTBE determination. Besides the improvement achieved in the analytical results, the variable selection allows a reduction in the time necessary for data acquisition. This fact has special importance when AOTFs are being used as the monochromator element. The AOTF's capability of random access to the selected wavelengths can be employed to access the necessary information very rapidly, enabling the methodology to be used for in-line monitoring of fuel additives.

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Determination of methyl-tert-butyl ether in gasoline by infrared spectrometry

Analytical Chemistry ◽

10.1021/ac00253a061 ◽

1983 ◽

Vol 55 (2) ◽

pp. 407-408 ◽

Cited By ~ 11

Author(s):

Slaton E. Fry ◽

Mike P. Fuller ◽

F. Tom. White ◽

David R. Battiste

Keyword(s):

Methyl Tert Butyl Ether ◽

Infrared Spectrometry ◽

Butyl Ether ◽

Tert Butyl

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Quantitative Structure–Activity Relationship and Molecular Docking Studies of Imidazolopyrimidine Amides as Potent Dipeptidyl Peptidase-4 (DPP4) Inhibitors

Journal of Pharmaceutical Research International ◽

10.9734/jpri/2019/v27i630186 ◽

2019 ◽

pp. 1-15 ◽

Cited By ~ 2

Author(s):

Leila Emami ◽

Razieh Sabet ◽

Amirhossein Sakhteman ◽

Mehdi Khoshnevis Zade

Keyword(s):

Genetic Algorithm ◽

Linear Regression ◽

Variable Selection ◽

Multiple Linear Regression ◽

Quantitative Structure Activity Relationship ◽

Dipeptidyl Peptidase ◽

Docking Studies ◽

Quantitative Structure ◽

Dipeptidyl Peptidase 4 ◽

Structure Activity

Type 2 diabetes (T2DM) is a metabolic disorder disease and DPP-4 inhibitors are a class of oral hypoglycemic that blocks the dipeptidyl peptidase-4 (DPP-4) enzyme. DPP-4 inhibitors reduce glucagon and blood glucose levels and don’t have side effects such as hypoglycemia or weight gain. In this paper, a series of imidazolopyrimidine amides analogues as DPP4 inhibitors were selected for quantitative structure-activity relationship (QSAR) analysis and docking studies. A collection of chemometric methods such as multiple linear regression (MLR), factor analysis-based multiple linear regression (FA-MLR), principal component regression (PCR), genetic algorithm for variable selection-MLR (GA-MLR) and partial least squared combined with genetic algorithm for variable selection (GA-PLS), were conducted to make relations between structural features and DPP4 inhibitory of a variety of imidazolopyrimidine amides derivatives. GA-PLS represented superior results with high statistical quality (R2 = 0.94 and Q2 = 0.80) for predicting the activity of the compounds. Docking studies of these compounds reveals and confirms that compounds 15, 18, 25, 26, and 28 are introduced as good candidates for DPP-4 inhibitors were introduced as a good candidate for DPP-4 inhibitory compounds.

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Principles of QSAR Modeling

International Journal of Quantitative Structure-Property Relationships ◽

10.4018/ijqspr.20200701.oa1 ◽

2020 ◽

Vol 5 (3) ◽

pp. 61-97 ◽

Cited By ~ 7

Author(s):

Paola Gramatica

Keyword(s):

Genetic Algorithm ◽

Linear Regression ◽

Variable Selection ◽

Multiple Linear Regression ◽

Molecular Descriptors ◽

Academic Career ◽

Environmental Pollutants ◽

Ordinary Least Squares ◽

Qsar Modeling ◽

Qsar Models

At the end of her academic career, the author summarizes the main aspects of QSAR modeling, giving comments and suggestions according to her 23 years' experience in QSAR research on environmental topics. The focus is mainly on Multiple Linear Regression, particularly Ordinary Least Squares, using a Genetic Algorithm for variable selection from various theoretical molecular descriptors, but the comments can be useful also for other QSAR methods. The need for rigorous validation, also external, and for applicability domain check to guarantee predictivity and reliability of QSAR models is particularly highlighted. The commented approach is the “predictive” one, based on chemometrics, and is usefully applied to the prioritization of environmental pollutants. All the discussed points and the author's ideas are implemented in the software QSARINS, as a legacy to the QSAR community.

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