Artificial Neural Network prediction of specific VOCs and blended VOCs for various concentrations from the olfactory receptor firing rates of Drosophila melanogaster

In our previous work, we have investigated the classification of odorants based on their chemical classes only, e.g. Alcohol, Terpene or Ester, using Artificial Neural Networks (ANN) as the signal processing backend of an insect olfactory electronic nose, or e-nose. However, potential applications of e-noses in the food and beverage industry which include the assessment of a fruit's ripeness, quality of wines or identifying bacterial contamination in products, demand the ability to predict beyond chemical class and to identify exact chemicals, known as specific Volatile Organic Compounds (VOCs) and blends of chemical that present themselves as aromas, known as blended VOCs (BVOCs). In this work, we demonstrate for the first time how it is possible to predict such VOCs and also BVOCs at varying concentration levels. We achieve this goal by using ANNs in the form of hybrid Multi-Layer Perceptrons (MLPs), to analyze the firing rate responses of the model organism Drosophila melanogaster's odorant receptors (DmOrs), in order to predict the specific VOCs and BVOCs. We report for the raw and noise injected data how the highest MLP prediction for specific VOCs occurred at a 10-4mol.dm-3 concentration in which all the VOC validation vectors were identified and at a concentration of 10-2mol.dm-3 for BVOCs in which 8/9 or 88.9% were identified. The results demonstrate for the first time the power of using MLPs and insect odorant receptors (Ors) to predict specific VOCs and BVOCs.

[1]  Edmund J. Crampin,et al.  Predicting odorant chemical class from odorant descriptor values with an assembly of multi-layer perceptrons , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  John R Carlson,et al.  The Molecular Basis of Odor Coding in the Drosophila Antenna , 2004, Cell.

[3]  Y. Jiang,et al.  Comparison of two methods of adding jitter to artificial neural network training , 2004, CARS.

[4]  Guozhong An,et al.  The Effects of Adding Noise During Backpropagation Training on a Generalization Performance , 1996, Neural Computation.

[5]  Tai Hyun Park,et al.  Cell-based olfactory biosensor using microfabricated planar electrode. , 2009, Biosensors & bioelectronics.

[6]  C. P. Unsworth,et al.  Application of artificial neural networks on mosquito Olfactory Receptor Neurons for an olfactory biosensor , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[7]  J W Gardner and P N Bartlett,et al.  Electronic Noses: Principles and Applications , 1999 .

[8]  Edmund J. Crampin,et al.  Multilayer Perceptron Classification of Unknown Volatile Chemicals from the Firing Rates of Insect Olfactory Sensory Neurons and Its Application to Biosensor Design , 2013, Neural Computation.

[9]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[10]  Edmund J. Crampin,et al.  Using artificial neural networks to classify unknown volatile chemicals from the firings of insect olfactory sensory neurons , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  John R. Carlson,et al.  The Molecular Basis of Odor Coding in the Drosophila Larva , 2005, Neuron.

[12]  Mouna Marrakchi,et al.  A new concept of olfactory biosensor based on interdigitated microelectrodes and immobilized yeasts expressing the human receptor OR17-40 , 2007, European Biophysics Journal.

[13]  C. P. Unsworth,et al.  Excessive Noise Injection Training of Neural Networks for Markerless Tracking in Obscured and Segmented Environments , 2006, Neural Computation.

[14]  Jacques de Villiers,et al.  Backpropagation neural nets with one and two hidden layers , 1993, IEEE Trans. Neural Networks.

[15]  John R. Carlson,et al.  Coding of Odors by a Receptor Repertoire , 2006, Cell.

[16]  J. Sola,et al.  Importance of input data normalization for the application of neural networks to complex industrial problems , 1997 .

[17]  James P. Egan,et al.  Signal detection theory and ROC analysis , 1975 .

[18]  Nicholas A J Lieven,et al.  Measuring and Improving Neural Network Generalization for Model Updating , 2000 .

[19]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[20]  Taskin Kavzoglu Determining Optimum Structure for Artificial Neural Networks , 1999 .