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 AbstractModeling potential occupational inhalation exposures and associated risks of toxic organics from chemical storage tanks used in hydraulic fracturing using AERMOD    Next AbstractA citric acid-assisted deposition strategy to synthesize mesoporous SiO(2)-confined highly dispersed LaMnO(3) perovskite nanoparticles for n-butylamine catalytic oxidation »

J Sep Sci


Title:Non-targeted volatile profiles for the classification of the botanical origin of Chinese honey by solid-phase microextraction and gas chromatography-mass spectrometry combined with chemometrics
Author(s):Chen H; Jin L; Fan C; Wang W;
Address:"Agro-Product Safety Research Center, Chinese Academy of Inspection and Quarantine, Beijing, China. Agilent Technologies (China) Company, Ltd., Beijing, China"
Journal Title:J Sep Sci
Year:2017
Volume:20171010
Issue:22
Page Number:4377 - 4384
DOI: 10.1002/jssc.201700733
ISSN/ISBN:1615-9314 (Electronic) 1615-9306 (Linking)
Abstract:"A potential method for the discrimination and prediction of honey samples of various botanical origins was developed based on the non-targeted volatile profiles obtained by solid-phase microextraction with gas chromatography and mass spectrometry combined with chemometrics. The blind analysis of non-targeted volatile profiles was carried out using solid-phase microextraction with gas chromatography and mass spectrometry for 87 authentic honey samples from four botanical origins (acacia, linden, vitex, and rape). The number of variables was reduced from 2734 to 70 by using a series of filters. Based on the optimized 70 variables, 79.12% of the variance was explained by the first four principal components. Partial least squares discriminant analysis, naive Bayes analysis, and back-propagation artificial neural network were used to develop the classification and prediction models. The 100% accuracy revealed a perfect classification of the botanical origins. In addition, the reliability and practicability of the models were validated by an independent set of additional 20 authentic honey samples. All 20 samples were accurately classified. The confidence measures indicated that the performance of the naive Bayes model was better than the other two models. Finally, the characteristic volatile compounds of linden honey were tentatively identified. The proposed method is reliable and accurate for the classification of honey of various botanical origins"
Keywords:Bayes Theorem *Gas Chromatography-Mass Spectrometry Honey/*analysis Reproducibility of Results *Solid Phase Microextraction Volatile Organic Compounds/*analysis botanical origin chemometrics honey non-targeted volatiles solid-phase microextraction;
Notes:"MedlineChen, Hui Jin, Linghe Fan, Chunlin Wang, Wenwen eng Germany 2017/09/20 J Sep Sci. 2017 Nov; 40(22):4377-4384. doi: 10.1002/jssc.201700733. Epub 2017 Oct 10"

 
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