Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction

Zou, Yunling; Lv, Jianhui

doi:10.3390/electronics9122205

Cited by 19 publications

(10 citation statements)

References 53 publications

(44 reference statements)

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“…Zou and Lv [50] optimized an electronic nose system in terms of data preprocessing and pattern recognition. They used the recurrent neural network (RNN) to identify the signature pattern and to ensure accuracy and stability.…”

Section: Discussionmentioning

confidence: 99%

Performance Analysis of MAU-9 Electronic-Nose MOS Sensor Array Components and ANN Classification Methods for Discrimination of Herb and Fruit Essential Oils

et al. 2021

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The recent development of MAU-9 electronic sensory methods, based on artificial olfaction detection of volatile emissions using an experimental metal oxide semiconductor (MOS)-type electronic-nose (e-nose) device, have provided novel means for the effective discovery of adulterated and counterfeit essential oil-based plant products sold in worldwide commercial markets. These new methods have the potential of facilitating enforcement of regulatory quality assurance (QA) for authentication of plant product genuineness and quality through rapid evaluation by volatile (aroma) emissions. The MAU-9 e-nose system was further evaluated using performance-analysis methods to determine ways for improving on overall system operation and effectiveness in discriminating and classifying volatile essential oils derived from fruit and herbal edible plants. Individual MOS-sensor components in the e-nose sensor array were performance tested for their effectiveness in contributing to discriminations of volatile organic compounds (VOCs) analyzed in headspace from purified essential oils using artificial neural network (ANN) classification. Two additional statistical data-analysis methods, including principal regression (PR) and partial least squares (PLS), were also compared. All statistical methods tested effectively classified essential oils with high accuracy. Aroma classification with PLS method using 2 optimal MOS sensors yielded much higher accuracy than using all nine sensors. The accuracy of 2-group and 6-group classifications of essentials oils by ANN was 100% and 98.9%, respectively.

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Section: Discussionmentioning

confidence: 99%

Performance Analysis of MAU-9 Electronic-Nose MOS Sensor Array Components and ANN Classification Methods for Discrimination of Herb and Fruit Essential Oils

et al. 2021

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“…Due to their high sensitivity, quick response times, and simple structures, oxide semiconductor gas sensors have been widely used to detect harmful and toxic gases; however, the sensors' poor gas selectivity frequently limited their ability to detect these gases [18]. As a result, simple techniques such as PCA were used to analyze the sensing patterns obtained from the sensors [19,20], and the performance of sensor arrays was subsequently improved using machine learning with various algorithms including artificial neural networks, convolution neural network (CNN), and recurrent neural networks (RNN) [21][22][23][24].…”

Section: B Electronic Nose Systemmentioning

confidence: 99%

A Study of Drift Effect in a Popular Metal Oxide Sensor and Gas Recognition Using Public Gas Datasets

et al. 2023

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Metal oxide sensor is widely used in many research fields, including E-nose for gas detection due to their tunable sensitivity, space efficiency and low cost. One of the most popular open data sets in electronic nose research contains data on various gases sampled using a MOx sensor in a wind tunnel over 16 months. A recent study published in 2022 by Nik Dennler has reported the discovery of the drift effect of a public dataset due to incorrect experimental design. they reported that the order of gas collection was not randomized and further discovered that a select set of gases were collected over a particular period. This paper expands the previous paper, by analyzing the drift effect with low signal, zero-offset subtracted signal's mean, and standard deviation value by location and time, and examining it with TSNE, a dimensional reduction method. In addition, the accuracy by time and location was analyzed by applying it to various Deep Learning methods. According to the results, we confirmed that gas information is already classified before the gas leaks in terms of temporal and spatial domain. Therefore, the classification accuracy overestimates the actual accuracy that can be obtained due to the drift effect. Based on the results of this study, it is necessary to thoroughly verify the temporal and spatial validity of the gas dataset when using the publicly available gas dataset to develop gas detection algorithms.

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“…Deep learning-based drift compensation strategies have been proposed for E-Nose. Recurrent neural network (RNN), which is most commonly used in the domain of natural language processing, was utilized by Zou et al [167] on a real-life sensor drift dataset and performed several experiments. In another study by Yutong Tian et al, a deep belief network (DBN), a generative graphical model, was utilized for drift compensation [168].…”

Section: Use Of Deep Learning In Exhaled Breath Analysismentioning

confidence: 99%

Breath VOC analysis and machine learning approaches for disease screening: a review

Haripriya¹,

Rangarajan²,

Sitaramgupta³

2023

J. Breath Res.

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Early disease detection is often correlated with a reduction in mortality rate and improved prognosis. Currently, techniques like biopsy and imaging that are used to screen chronic diseases are invasive, costly or inaccessible to a large population. Thus, a non-invasive disease screening technology is the need of the hour. Existing non-invasive methods like gas chromatography-mass spectrometry, selected-ion flow-tube mass spectrometry, and proton transfer reaction-mass-spectrometry are expensive. These techniques necessitate experienced operators, making them unsuitable for a large population. Various non-invasive sources are available for disease detection, of which exhaled breath is preferred as it contains different volatile organic compounds (VOCs) that reflect the biochemical reactions in the human body. Disease screening by exhaled breath VOC analysis can revolutionize the healthcare industry. This review focuses on exhaled breath VOC biomarkers for screening various diseases with a particular emphasis on liver diseases and head and neck cancer as examples of diseases related to metabolic disorders and diseases unrelated to metabolic disorders, respectively. Single sensor and sensor array-based (Electronic Nose) approaches for exhaled breath VOC detection are briefly described, along with the machine learning techniques used for pattern recognition.

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Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction

Cited by 19 publications

References 53 publications

Performance Analysis of MAU-9 Electronic-Nose MOS Sensor Array Components and ANN Classification Methods for Discrimination of Herb and Fruit Essential Oils

Performance Analysis of MAU-9 Electronic-Nose MOS Sensor Array Components and ANN Classification Methods for Discrimination of Herb and Fruit Essential Oils

A Study of Drift Effect in a Popular Metal Oxide Sensor and Gas Recognition Using Public Gas Datasets

Breath VOC analysis and machine learning approaches for disease screening: a review

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