Application of electronic nose for industrial odors and gaseous emissions measurement and monitoring--An overview.

Firefly algorithm (FA) was developed by Xin-She Yang in 2008, while cuckoo search (CS) was developed by Xin-She Yang and Suash Deb in 2009. Both algorithms have been found to be very efficient in solving global optimization problems. This chapter provides an overview of both cuckoo search and firefly algorithm as well as their latest developments and applications. We analyze these algorithms and gain insight into their search mechanisms and find out why they are efficient. We also discuss the essence of algorithms and its link to self-organizing systems. In addition, we also discuss important issues such as parameter tuning and parameter control, and provide some topics for further research.

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Cuckoo Search and Firefly Algorithm

Application of artificial neural network in food classification.

This work examines gas sensor array technology combined with multivariate data processing methods and demonstrates a promising potential for rapid, non-destructive analysis of food. Main attention is focused on detailed description of sensor used in e-nose instruments, construction, and principle of operation of these systems. Moreover, this paper briefly reviews the progress in the field of artificial olfaction and future trends in electronic nose technology, namely, e-nose based on mass spectrometry. Further discussion concerns a comparison of artificial nose with gas chromatography-olfactometry and the application of e-nose instruments in different areas of food industry.

Invited review article: an odor-sensing system--powerful technique for foodstuff studies.

Application of electronic nose in Chinese spirits quality control and flavour assessment

Artificial neural networks (ANNs) are generally considered as the most promising pattern recognition method to process the signals from a chemical sensor array of electronic noses, which makes the system more bionics. This paper presents a chaotic neural network entitled KIII, which modeled olfactory systems, applied to an electronic nose to discriminate six typical volatile organic compounds (VOCs) in Chinese rice wines. Thirty-two-dimensional feature vectors of a sensor array consisting of eight sensors, in which four features were extracted from the transient response of each TGS sensor, were input into the KIII network to investigate its generalization capability for concentration influence elimination and sensor drift counteraction. In comparison with the conventional back propagation trained neural network (BP-NN), experimental results show that the KIII network has a good performance in classification of these VOCs of different concentrations and even for the data obtained 1 month later than the training set. Its robust generalization capability is suitable for electronic nose applications to reduce the influence of concentration and sensor drift.

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Yuqi Qin

Papers

A pattern recognition method for electronic noses based on an olfactory neural network