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 AbstractTree Response to Herbivory Is Affected by Endogenous Rhythmic Growth and Attenuated by Cotreatment With a Mycorrhizal Fungus    Next Abstract'Super e-noses': Multi-layer perceptron classification of volatile odorants from the firing rates of cross-species olfactory receptor arrays »

Annu Int Conf IEEE Eng Med Biol Soc


Title:Using artificial neural networks to classify unknown volatile chemicals from the firings of insect olfactory sensory neurons
Author(s):Bachtiar LR; Unsworth CP; Newcomb RD; Crampin EJ;
Address:"Department of Engineering Science, The University of Auckland, Auckland 1010, New Zealand. lbac004@aucklanduni.ac.nz"
Journal Title:Annu Int Conf IEEE Eng Med Biol Soc
Year:2011
Volume:2011
Issue:
Page Number:2752 - 2755
DOI: 10.1109/IEMBS.2011.6090754
ISSN/ISBN:2694-0604 (Electronic) 2375-7477 (Linking)
Abstract:"The olfactory system detects volatile chemical compounds, known as odour molecules or odorants. Such odorants have a diverse chemical structure which in turn interact with the receptors of the olfactory system. The insect olfactory system provides a unique opportunity to directly measure the firing rates that are generated by the individual olfactory sensory neurons (OSNs) which have been stimulated by odorants in order to use this data to inform their classification. In this work, we demonstrate that it is possible to use the firing rates from an array of OSNs of the vinegar fly, Drosophila melanogaster, to train an Artificial Neural Network (ANN), as a series of a Multi-Layer Perceptrons (MLPs), to differentiate between eight distinct chemical classes. We demonstrate that the MLPs when trained on 108 odorants, for both clean and 10% noise injected data, can reliably identify 87% of an unseen validation set of chemicals using noise injection. In addition, the noise injected MLPs provide a more accurate level of identification. This demonstrates that a 10% noise injected series of MLPs provides a robust method for classifying chemicals from the firing rates of OSNs and paves the way to a future realisation of an artificial olfactory biosensor"
Keywords:"Animals Insecta *Neural Networks, Computer *Odorants Olfactory Receptor Neurons/*physiology Volatile Organic Compounds/*classification;"
Notes:"MedlineBachtiar, Luqman R Unsworth, Charles P Newcomb, Richard D Crampin, Edmund J eng Research Support, Non-U.S. Gov't 2012/01/19 Annu Int Conf IEEE Eng Med Biol Soc. 2011; 2011:2752-5. doi: 10.1109/IEMBS.2011.6090754"

 
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 26-12-2024