Mr Luqman Ramadhana Bachtiar
Research | Current
Title: Automatic odorant detection with artificial neural networks
Olfaction is a method for detecting and recognising various odours and flavours. It is a critical sense for many organisms and harnessing it for use in biosensor technology would have applications in food quality assessment, medical diagnostics, and intercepting pests, diseases and contraband at borders.
This study concerns molecular olfaction, which involves identifying the mechanisms that influence smell in insects, to assist in new product development. It is achieved by the isolation and characterisation of elements in odour production and chemosensory reception systems.
Through the use of artificial neural network analysis, it is possible to study the underlying patterns of insect olfactory receptor responses to various volatile chemical odours. By training the artificial neural network system on a known set of receptor odour responses, unknown chemical odorants can identified based on their known insect receptor responses.
This work is performed in collaboration with the Cybernose Group of Plant & Food Research and the algorithm developed supports their research into insect olfactory receptor odour responses as a sensory system for an olfactory biosensor device.
- Biomedical signal processing
- Artificial neural networks in biosignal processing and classification
- Biomedical simulation involving signal processing
- Pattern recognition methods for data mining in biosignals
Bachtiar, L. R., Unsworth, C. P., Newcomb, R. D. (2013). Application of Artificial Neural Networks on Mosquito Olfactory Receptor Neurons for an Olfactory Biosensor. Paper presented at the 35th Annual International IEEE EMBS Conference, Osaka, 5390-5393.
L. R. Bachtiar, C. P. Unsworth, R. D. Newcomb, and E. J. Crampin. Multilayer Perceptron Classification of Unknown Volatile Chemicals from the Firing Rates of Insect Olfactory Sensory Neurons and Its Application to Biosensor Design. Neural Computation, vol. 25, no. 1, pp. 259-287, 2013.
Bachtiar, L. R., Unsworth, C. P., Newcomb, R. D., & Crampin, E. J. (2011). Predicting Odorant Chemical Class From Odorant Descriptor Values With An Assembly Of Multi-Layer Perceptrons. Paper presented at the 33rd Annual International IEEE EMBS Conference, Boston, 2756-2759.
Bachtiar, L. R., Unsworth, C. P., Newcomb, R. D., & Crampin, E. J. (2011). Using Artificial Neural Networks to Classify Unknown Volatile Chemicals from the Firings of Insect Olfactory Sensory Neurons. Paper presented at the 33rd Annual International IEEE EMBS Conference, Boston, 2752-2755.
Selected publications and creative works (Research Outputs)
- Bachtiar, L. R., Newcomb, R. D., Kralicek, A. V., & Unsworth, C. P. (2016). Improving odorant chemical class prediction with multi-layer perceptrons using temporal odorant spike responses from drosophila melanogaster olfactory receptor neurons. 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), 6393-6396. Orlando, Florida, US: IEEE - Institute of Electrical and Electronics Engineers. 10.1109/EMBC.2016.7592191
Other University of Auckland co-authors: Richard Newcomb, Charles Unsworth
- Bachtiar, L. R., Unsworth, C. P., Newcomb, R. D., & Crampin, E. J. (2011). Using Artificial Neural Networks to Classify Unknown Volatile Chemicals from the Firing Rates of Insect Olfactory Sensory Neurons. Paper presented at 33rd Annual International IEEE EMBS Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA. 30 August - 3 September 2011. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC. (pp. 4). 10.1109/IEMBS.2011.6090754
Other University of Auckland co-authors: Edmund Crampin, Richard Newcomb