Modeling Retention Indices of a Series Components Food and Pollutants of the Environment: Methods; OLS, LAD

Modeling Retention Indices of a Series Components Food and Pollutants of the Environment: Methods; OLS, LAD

Fatiha Mebarki

Contact

Khadidja Amirat

Contact

Salima Ali Mokhnache

Contact

Djelloul Messadi

Contact

α University Badji Mokhtar Annaba

Modeling Retention Indices of a Series Components Food and Pollutants of the Environment: Methods; OLS, LAD

Article Fingerprint

ReserarchID

3LO1H

Modeling Retention Indices of a Series Components Food and Pollutants of the Environment: Methods; OLS, LAD Banner

AI TAKEAWAY

Connecting with the Eternal Ground

Abstract

The gas chromatographic retention indices for (89 pyrazines of test and 25 of validation) on O V-101 and Carbowax -20M are successfuty modeled with the ald of a computer and the Software system. Structural descriptors are calculated and multiple linear regression analysis are used to generate model equations relating structural features to observed retention characteristics then was treated with two methods. The detection of influential observations for the standard least squares regression model is a problem which has been extensively studied. LAD regression diagnostics offers alternative dicapproaches whose main feature is the robustness. Here a nonparametric method for detecting influential observations is presented and compared with other classical diagnostics methods. Comparisons are between models generated for the two stationary was carried out with two methods, and descriptors that may encode differences in solute interactions with stationary phases of differing polarity are discussed and validated results in the state approached by the tests statistics: Test of Anderson-Darling, shapiro-wilk, Agostino, Jarque-Bera and the confidence interval thanks to the concept of robustness to check if the distribution of the errors is really approximate.

References

28 Cites in Article

Reference Format

D Stanton,P Jurs (1989). Computer-assisted prediction of gas chromatographic retention indexes of pyrazines.
G Berlin (1982). The Pyrazine.
Imen Touhami,Karima Mokrani,Djelloul Messadi (2012). Modèle QSRR Hybrides Algorithme Génétique Régression Linéaire Multiple des indices de rétentions de pyrazines en chromatographie gazeuse.
Thomas Parliment,Martin Epstein (1973). Organoleptic properties of some alkyl-substituted alkoxy- and alkylthiopyrazines.
R Kaliszan (1986). Quantitative relationships between molecular structure and Chromatographic retention.
R Kaliszan (1987). Quantitative structure-chromatographic retention relationships.
Seppo Pynnönen,Timosalmi (1994). Least Absolute Deviation Regression.
Tiffany Machabert (2014). Modèles en très grande dimension avec des outliers.
Y Dodge,Valentin Rousson (2004). Analyses de régression appliquée.
Kani Chen,Zhiliang Ying,Hong Zhang,L Zhao (2006). Analysis of least absolute deviation.
Gabriela Ciuperca (2009). Estimation robuste dans un modèle paramétrique avec rupture.
Gilbert Saporta,Gilles Stoltz (2012). Gilbert Saporta : un parcours éclectique Analyse, fouille, science des données. Entretien.
Ndèyeniang-Gilbert Saporta (2014). V. Introduction à la régression non paramétrique.
Nadia Dr,Asmaa Al -Noor (2013). Mohammad. Model of Robust Regression with Parametric and Nonparametric Methods.
Rekaia Soumaya Indicateurs de la sensibilité de l'estimateur Least Absolute Déviation Assas Paris.
Y Dodge (2004). Statistique: Dictionnaire encyclopédique.
Y Dodge,J Jureckova (2000). Adaptive Regression.
R Kaliszan (1987). Quantitative structure-chromatographic retention relationships.
Seung Lee,Ki,Yulia Polyakova,Kyung Row,Ho (2004). Evaluation of predictive retention factors for phenolic compounds with QSPR equations.
I Levine (2000). Quantum chemistry.
V Magnuson,D Harriss,S Basak (1983). Topological indices based on neighbor. 26. Symmetry: chemical and biological applications.
Hideki Masuda,Misaku Yoshida,Takayuki Shibamoto (1981). Synthesis of new pyrazines for flavor use.
Hideki Masuda,Satoru Mihara (1986). Use of modified molecular connectivity indices to predict retention indices of monosubstituted alkyl, alkoxy, alkylthio, phenoxy and (phenylthio)pyrazines.
S Mihara,N Enomoto (1985). Calculation of retention indices of pyrazines on the basis of molecular structure.
S Mihara,H Masuda (1987). Correlation between molecular structures and retention indices of pyrazines.
G Buchbauer (2000). Threshold-based structureactivity relationships of pyrazines with bellpepper Flavor.
Nornadiah Mohd Razali,Bee Yab,Yah (2011). Power Comparaisons of shapiro-wilk, Kolmogorovsmornov, lillieffors and Anderson-Darling tests.
N Damodar,Dawn Gujarati,Porter (2009). Basic Econometrics Fifth Edition.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Fatiha Mebarki. 2016. \u201cModeling Retention Indices of a Series Components Food and Pollutants of the Environment: Methods; OLS, LAD\u201d. Global Journal of Human-Social Science - B: Geography, Environmental Science & Disaster Management GJHSS-B Volume 16 (GJHSS Volume 16 Issue B1): .

More Citation Formats

Select Citation Style:

Download Citation

Download Article

GJHSS Volume 16 Issue B1
Pg. 17- 26

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/GJHSS

Print ISSN 0975-587X

e-ISSN 2249-460X

Keywords

Not Found

Classification

GJHSS-B Classification: FOR Code: 300899p

Submission ReceivedDecember 16, 2015
Peer Review Double Blind
Handling Editor
Accepted January 1, 2016
Published January 15, 2016

Version of record

v1.2

Issue date

March 16, 2016

Language

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Score: 104

Country: Algeria

Subject: Global Journal of Human-Social Science - B: Geography, Environmental Science & Disaster Management

Authors: Fatiha Mebarki, Khadidja Amirat, Salima Ali Mokhnache, Djelloul Messadi (PhD/Dr. count: 0)

View Count (all-time): 162

Total Views (Real + Logic): 4016

Total Downloads (simulated): 1918

Publish Date: 2016 03, Wed

Monthly Totals (Real + Logic):

Month 1: 30 views
Month 2: 54 views
Month 3: 50 views
Month 4: 63 views
Month 5: 54 views
Month 6: 49 views
Month 7: 38 views
Month 8: 25 views
Month 9: 50 views
Month 10: 42 views
Month 11: 56 views
Month 12: 32 views
Month 13: 36 views
Month 14: 37 views
Month 15: 23 views
Month 16: 37 views
Month 17: 30 views
Month 18: 22 views
Month 19: 26 views
Month 20: 42 views
Month 21: 21 views
Month 22: 23 views
Month 23: 19 views
Month 24: 13 views
Month 25: 28 views
Month 26: 36 views
Month 27: 24 views
Month 28: 42 views
Month 29: 40 views
Month 30: 35 views
Month 31: 32 views
Month 32: 35 views
Month 33: 21 views
Month 34: 18 views
Month 35: 26 views
Month 36: 24 views
Month 37: 19 views
Month 38: 21 views
Month 39: 37 views
Month 40: 16 views
Month 41: 23 views
Month 42: 22 views
Month 43: 40 views
Month 44: 25 views
Month 45: 21 views
Month 46: 30 views
Month 47: 28 views
Month 48: 40 views
Month 49: 27 views
Month 50: 40 views
Month 51: 41 views
Month 52: 40 views
Month 53: 20 views
Month 54: 26 views
Month 55: 26 views
Month 56: 38 views
Month 57: 30 views
Month 58: 31 views
Month 59: 38 views
Month 60: 34 views
Month 61: 13 views
Month 62: 32 views
Month 63: 22 views
Month 64: 38 views
Month 65: 27 views
Month 66: 16 views
Month 67: 25 views
Month 68: 27 views
Month 69: 37 views
Month 70: 39 views
Month 71: 41 views
Month 72: 12 views
Month 73: 37 views
Month 74: 36 views
Month 75: 27 views
Month 76: 24 views
Month 77: 39 views
Month 78: 33 views
Month 79: 35 views
Month 80: 15 views
Month 81: 27 views
Month 82: 32 views
Month 83: 17 views
Month 84: 48 views
Month 85: 21 views
Month 86: 13 views
Month 87: 22 views
Month 88: 24 views
Month 89: 30 views
Month 90: 40 views
Month 91: 28 views
Month 92: 25 views
Month 93: 44 views
Month 94: 37 views
Month 95: 24 views
Month 96: 22 views
Month 97: 36 views
Month 98: 21 views
Month 99: 37 views
Month 100: 34 views
Month 101: 23 views
Month 102: 32 views
Month 103: 26 views
Month 104: 20 views
Month 105: 36 views
Month 106: 39 views
Month 107: 43 views
Month 108: 30 views
Month 109: 27 views
Month 110: 22 views
Month 111: 23 views
Month 112: 19 views
Month 113: 29 views
Month 114: 45 views
Month 115: 32 views
Month 116: 67 views
Month 117: 58 views
Month 118: 57 views
Month 119: 54 views
Month 120: 30 views
Month 121: 46 views
Month 122: 31 views

Total Views: 4016

Total Downloads: 1918

2026 Trends

Published Article

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]