Author
Yuqi Qin
Bio: Yuqi Qin is an academic researcher from Zhejiang University. The author has contributed to research in topics: Electronic nose & Artificial neural network. The author has an hindex of 1, co-authored 1 publications receiving 60 citations.
Papers
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TL;DR: 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, has a good performance in classification of these VOCs of different concentrations.
Abstract: 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.
73 citations
Cited by
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TL;DR: The present review evaluates the key modules of the electronic nose, a biomimetic system, with specific examples of applications to industrial emissions monitoring and measurement, and describes the pros and cons of artificial olfaction technique for the industrial applications.
156 citations
01 Jan 2014
TL;DR: This chapter provides an overview of both cuckoo search and firefly algorithm as well as their latest developments and applications and analyzes these algorithms to gain insight into their search mechanisms and find out why they are efficient.
Abstract: 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.
131 citations
TL;DR: The artificial neural network was used to build the classification model based on the relevant features of beer and allowed the discrimination between qualities of beer samples with up to 100% of correct classifications.
105 citations
TL;DR: 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.
Abstract: 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.
81 citations
TL;DR: Wang et al. as mentioned in this paper applied a portable electronic nose to objectively assess twenty most favorable and commercially available Chinese spirits, and the responsive value obtained from electronic nose measurement provided strong evidence that sensor S1 (aromatic compounds, toluene), S3 (aromorphic compounds, ammonia), S5 (alkenes, aromatic compounds), S6 (broad-methane), S7 (sulphur-organic) and S9 (aromatics compounds, sulphur- organic) could be applied to assess spirits.
72 citations