| Title: | Ratiometric Decoding of Pheromones for a Biomimetic Infochemical Communication System |
| Author(s): | Wei G; Thomas S; Cole M; Racz Z; Gardner JW; |
| Address: | "Microsensors and Bioelectronics Laboratory, School of Engineering, University of Warwick, Coventry CV4 7AL, UK. guangfen.wei@yahoo.com. School of Information & Electronic Engineering, Shandong Technology and Business University, Yantai 264005, China. guangfen.wei@yahoo.com. Microsensors and Bioelectronics Laboratory, School of Engineering, University of Warwick, Coventry CV4 7AL, UK. s.thomas.1@warwick.ac.uk. Microsensors and Bioelectronics Laboratory, School of Engineering, University of Warwick, Coventry CV4 7AL, UK. marina.cole@warwick.ac.uk. Microsensors and Bioelectronics Laboratory, School of Engineering, University of Warwick, Coventry CV4 7AL, UK. zoltan.racz@durham.ac.uk. School of Engineering and Computing Sciences, Durham University, Durham DH1 3LE, UK. zoltan.racz@durham.ac.uk. Microsensors and Bioelectronics Laboratory, School of Engineering, University of Warwick, Coventry CV4 7AL, UK. J.W.Gardner@warwick.ac.uk" |
| ISSN/ISBN: | 1424-8220 (Electronic) 1424-8220 (Linking) |
| Abstract: | "Biosynthetic infochemical communication is an emerging scientific field employing molecular compounds for information transmission, labelling, and biochemical interfacing; having potential application in diverse areas ranging from pest management to group coordination of swarming robots. Our communication system comprises a chemoemitter module that encodes information by producing volatile pheromone components and a chemoreceiver module that decodes the transmitted ratiometric information via polymer-coated piezoelectric Surface Acoustic Wave Resonator (SAWR) sensors. The inspiration for such a system is based on the pheromone-based communication between insects. Ten features are extracted from the SAWR sensor response and analysed using multi-variate classification techniques, i.e., Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), and Multilayer Perception Neural Network (MLPNN) methods, and an optimal feature subset is identified. A combination of steady state and transient features of the sensor signals showed superior performances with LDA and MLPNN. Although MLPNN gave excellent results reaching 100% recognition rate at 400 s, over all time stations PNN gave the best performance based on an expanded data-set with adjacent neighbours. In this case, 100% of the pheromone mixtures were successfully identified just 200 s after they were first injected into the wind tunnel. We believe that this approach can be used for future chemical communication employing simple mixtures of airborne molecules" |
| Keywords: | Animals *Biomimetics Insecta Pheromones Polymers SAW sensor array VOC detection biomimetic infochemical communication pheromone ratiometric decoding; |
| Notes: | "MedlineWei, Guangfen Thomas, Sanju Cole, Marina Racz, Zoltan Gardner, Julian W eng Switzerland 2017/10/31 Sensors (Basel). 2017 Oct 30; 17(11):2489. doi: 10.3390/s17112489" |
