Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractPotential for Biological Control of Pythium schmitthenneri Root Rot Disease of Olive Trees (Olea europaea L.) by Antagonistic Bacteria    Next AbstractTherapeutic drug monitoring: measurements of antiepileptic and barbiturate drug levels in blood by gas chromatography with nitrogen - selective detector »

Anal Chim Acta


Title:Systematic ratio normalization of gas chromatography signals for biological sample discrimination and biomarker discovery
Author(s):Lehallier B; Ratel J; Hanafi M; Engel E;
Address:"INRA, UR 370 QuaPA, MASS laboratory, 63122 Saint-Genes-Champanelle, France"
Journal Title:Anal Chim Acta
Year:2012
Volume:20120425
Issue:
Page Number:16 - 22
DOI: 10.1016/j.aca.2012.04.019
ISSN/ISBN:1873-4324 (Electronic) 0003-2670 (Linking)
Abstract:"The present paper introduces a new gas chromatography data processing procedure dubbed systematic ratio normalization (SRN) enabling to improve both sample set discrimination and biomarker identification. SRN consists in (1) calculating, for each sample, all the log-ratios between abundances of chromatography-analyzed compounds, then (2) selecting the log-ratio(s) that best maximize the discrimination between sample-sets. The relevance of SRN was evaluated on two data sets acquired through gas chromatography-mass spectrometry as part of separate studies designed (i) to discriminate source-origins between vegetable oils analyzed via an analytical system exposed to instrument drift (data set 1) and (ii) to discriminate animal feed between meat samples aged for different durations (data set 2). Applying SRN to raw data made it possible to obtain robust discrimination models for the two data sets by enhancing the contribution to the data variance of the factor-of-interest while stabilizing the contribution of the disturbance factor. The most discriminant log-ratios were shown to employ the most relevant biomarkers presenting relative independence of the factor-of-interest as well as co-behavior of the disturbance effects potentially biasing the discrimination, such as instrument drift or sample biochemical changes. SRN can be run a posteriori on any data set, and might be generalizable to most of separating methods"
Keywords:Algorithms Animals Discriminant Analysis Gas Chromatography-Mass Spectrometry/*methods Meat/*analysis Multivariate Analysis Plant Oils/*chemistry Principal Component Analysis Sheep Vegetables/*chemistry Volatile Organic Compounds/*analysis;
Notes:"MedlineLehallier, Benoist Ratel, Jeremy Hanafi, Mohamed Engel, Erwan eng Evaluation Study Research Support, Non-U.S. Gov't Netherlands 2012/06/19 Anal Chim Acta. 2012 Jul 6; 733:16-22. doi: 10.1016/j.aca.2012.04.019. Epub 2012 Apr 25"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 22-11-2024