A novel background interferences elimination method in electronic nose using pattern recognition

Zhang, Lei; Tian, Fengchun; Dang, Lijun; Li, Guorui; Peng, Xiongwei; Yin, Xin; Liu, Shouqiong

doi:10.1016/j.sna.2013.07.032

Cited by 37 publications

(12 citation statements)

References 34 publications

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“…Chemical detection systems are widely used to recognize the presence of a particular substance or identify low concentration of volatile compounds and hazardous gases by mimicking an animal’s sense of smell ( Glatz and Bailey-Hill 2011 ; Oh et al 2011 ; Lee et al 2012 ). Although recent advances have improved the precision and efficacy of these detection technologies, these are still imperfect ( Dacres et al 2011 ; Zhang et al 2013 ), and animals continue to appear more sensitive than man-made systems ( Shelby et al 2006 ; Macias et al 2010 ; Weber et al 2011 ; Bomers et al 2012 ; Horvath et al 2013 ), in addition to having the advantage of being a more dynamic system allowing quick detection over a large search area ( Calbk et al 2008 ). However, regardless of the nature of the detection system both false positive (where the system detects the target as present when it is absent) and false negative (where the target is present but the system fails to detect it) errors occur in these and in every detection system.…”

Section: Introductionmentioning

confidence: 99%

Using Sniffing Behavior to Differentiate True Negative from False Negative Responses in Trained Scent-Detection Dogs

et al. 2014

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False negatives are recorded in every chemical detection system, but when animals are used as a scent detector, some false negatives can arise as a result of a failure in the link between detection and the trained alert response, or a failure of the handler to identify the positive alert. A false negative response can be critical in certain scenarios, such as searching for a live person or detecting explosives. In this study, we investigated whether the nature of sniffing behavior in trained detection dogs during a controlled scent-detection task differs in response to true positives, true negatives, false positives, and false negatives. A total of 200 videos of 10 working detection dogs were pseudorandomly selected and analyzed frame by frame to quantify sniffing duration and the number of sniffing episodes recorded in a Go/No-Go single scent-detection task using an eight-choice test apparatus. We found that the sniffing duration of true negatives is significantly shorter than false negatives, true positives, and false positives. Furthermore, dogs only ever performed one sniffing episode towards true negatives, but two sniffing episodes commonly occurred in the other situations. These results demonstrate how the nature of sniffing can be used to more effectively assess odor detection by dogs used as biological detection devices.

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Section: Introductionmentioning

confidence: 99%

Using Sniffing Behavior to Differentiate True Negative from False Negative Responses in Trained Scent-Detection Dogs

et al. 2014

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“…The structure of this idea is shown in Figure 5 . That is, for the dynamic interference, the suppression methods consisted of two steps: interference discrimination and interference correction [ 40 , 41 ]. First, determine whether the current response was from interference or not.…”

Section: Methods For Suppressing the Interference Caused By Changementioning

confidence: 99%

Study on Interference Suppression Algorithms for Electronic Noses: A Review

Liang

Tian

Yang

et al. 2018

Sensors

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Electronic noses (e-nose) are composed of an appropriate pattern recognition system and a gas sensor array with a certain degree of specificity and broad spectrum characteristics. The gas sensors have their own shortcomings of being highly sensitive to interferences which has an impact on the detection of target gases. When there are interferences, the performance of the e-nose will deteriorate. Therefore, it is urgent to study interference suppression techniques for e-noses. This paper summarizes the sources of interferences and reviews the advances made in recent years in interference suppression for e-noses. According to the factors which cause interference, interferences can be classified into two types: interference caused by changes of operating conditions and interference caused by hardware failures. The existing suppression methods were summarized and analyzed from these two aspects. Since the interferences of e-noses are uncertain and unstable, it can be found that some nonlinear methods have good effects for interference suppression, such as methods based on transfer learning, adaptive methods, etc.

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“…One study [ 26 ] used a chaotic BPNN algorithm to identify distilled liquors; the recognition rate reached 100%, and the convergence speed was 75.5 times faster that of the BPNN algorithm. Zhang used the LPC pattern recognition algorithm based on KPCA to enhance the elimination of background interference and improved the prediction accuracy of mixed gases [ 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Research on a Mixed Gas Recognition and Concentration Detection Algorithm Based on a Metal Oxide Semiconductor Olfactory System Sensor Array

Zhao

Chen

et al. 2018

Sensors

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As a typical machine olfactory system index, the accuracy of hybrid gas identification and concentration detection is low. This paper proposes a novel hybrid gas identification and concentration detection method. In this method, Kernel Principal Component Analysis (KPCA) is employed to extract the nonlinear mixed gas characteristics of different components, and then K-nearest neighbour algorithm (KNN) classification modelling is utilized to realize the recognition of the target gas. In addition, this method adopts a multivariable relevance vector machine (MVRVM) to regress the multi-input nonlinear signal to realize the detection of the concentration of the hybrid gas. The proposed method is validated by using CO and CH4 as the experimental system samples. The experimental results illustrate that the accuracy of the proposed method reaches 98.33%, which is 5.83% and 14.16% higher than that of principal component analysis (PCA) and independent component analysis (ICA), respectively. For the hybrid gas concentration detection method, the CO and CH4 concentration detection average relative errors are reduced to 5.58% and 5.38%, respectively.

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A novel background interferences elimination method in electronic nose using pattern recognition

Cited by 37 publications

References 34 publications

Using Sniffing Behavior to Differentiate True Negative from False Negative Responses in Trained Scent-Detection Dogs

Using Sniffing Behavior to Differentiate True Negative from False Negative Responses in Trained Scent-Detection Dogs

Study on Interference Suppression Algorithms for Electronic Noses: A Review

Research on a Mixed Gas Recognition and Concentration Detection Algorithm Based on a Metal Oxide Semiconductor Olfactory System Sensor Array

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