Sensor array optimization techniques for exhaled breath analysis to discriminate diabetics using an electronic nose

Jeon, Jong Up; Choi, Jang Sik; Yu, Jae‐Young; Lee, Haeryong; Jang, Byoung Kuk; Byun, Hyung‐Gi

doi:10.4218/etrij.2017-0018

Cited by 17 publications

(8 citation statements)

References 20 publications

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“…In order to overcome the noise contamination of the E-nose signals when monitoring beef quality, the discrete wavelet transform and long shortterm memory signal processing technique was proposed by Wijaya et al [16] and obtained a performance of 94.83% accuracy. A multivariate analysis of variance test like Wilks' lambda (Λ)-statistics can also be performed to reduce the unwanted noise from the E-nose signals [17]. Other signal processing methods, such as Mahalanobis distance and genetic algorithm, as well as LDA, PCA, and Wilks' lambda statistics, were described by Sun et al [18] to reduce the dimension of an Enose system's output.…”

Section: Introductionmentioning

confidence: 99%

A deep neural network with electronic nose for water stress prediction in Khasi Mandarin Orange plants

Sharma,

Barkataki,

Sarma

2023

Meas. Sci. Technol.

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Water stress is a significant environmental factor that hampers plant productivity and leads to various physiological and biological changes in plants. These include modifications in stomatal conductance and distribution, alteration of leaf water potential & turgor loss, altered chlorophyll content, and reduced cell expansion and growth. Additionally, water stress induces changes in the emission of volatile organic compounds across different parts of the plants. This study presents the development of an electronic nose (E-nose) system integrated with a deep neural network (DNN) to detect the presence and levels of water stress induced in Khasi Mandarin Orange plants. The proposed approach offers an alternative to conventional analytical methods that demand expensive and complex laboratory facilities. The investigation employs the leaf relative water content (RWC) estimation, a conventional technique, to evaluate water stress induction in the leaves of 20 plants collected over a span of 9 days after stopping irrigation. Supervised pattern recognition algorithms are trained using the results of RWC measurement, categorising leaves into non-stressed or one of four stress levels based on their water content. The dataset used for training and optimising the DNN model consists of 27 940 samples. The performance of the DNN model is compared to traditional machine learning methods, including linear and radial basis function support vector machines, k-nearest neighbours, decision tree, and random forest. From the results, it is seen that the optimised DNN model achieves the highest accuracy of 97.59% in comparison to other methods. Furthermore, the model is validated on an unseen dataset, exhibiting an accuracy of 97.32%. The proposed model holds the potential to enhance agricultural practices by enabling the detection and classification of water stress in crops, thereby aiding in water management improvements and increased productivity.

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Section: Introductionmentioning

confidence: 99%

A deep neural network with electronic nose for water stress prediction in Khasi Mandarin Orange plants

Sharma,

Barkataki,

Sarma

2023

Meas. Sci. Technol.

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“…This can be a promising way for non-invasive diabetes diagnosis and glucose monitor-ing. A few attempts to perform diabetes diagnosis with gas sensors were made based on a limited number of medical samples [17], [39]- [41]. Some works used synthetic samples instead of direct data collection from patients [42], [43].…”

Section: Introductionmentioning

confidence: 99%

Precise Detection and Quantitative Prediction of Blood Glucose Level With an Electronic Nose System

Wang

Hua

et al. 2022

IEEE Sensors J.

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Blood glucose level is an important health indicator. Non-invasive, easy-to-use glucose detection and monitoring methods and tools are desperately needed, especially for patients with diabetes. In this work, we developed a new method to quantitively identify and analyze the blood glucose level by measuring the biomarkers in breath with an electronic nose (E-Nose) system based on a metal oxide (MOX) gas sensor array. Advanced machine-learning models have been studied and developed to precisely predict the blood glucose level based on the measurement of 41 participants for 10 days. The testing result shows that the E-Nose system and proposed analysis models identify blood glucose levels at an accuracy of 90.4% and a small average error of 0.69 mmol/L in blood glucose concentration. This study indicates that the E-Nose system enabled with machine learning is an efficient and precise method to achieve low-cost and non-invasive disease diagnosis.

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“…Moreover, we investigated the application of various basic machine-learning methods to the sensor array for improving the discriminant ability. Previous research investigated sensor arrays using multiple gas sensors to be analyzed with various statistical methods, i.e., machine learning [18][19][20][21][22][23][24][25][26][27][28]. We also explored discriminating between several VOCs in a contaminant gas using a semiconductor sensor array with principal-component analysis (PCA) [17,29].…”

Section: Introductionmentioning

confidence: 99%

Selective Detection of Target Volatile Organic Compounds in Contaminated Air Using Sensor Array with Machine Learning: Aging Notes and Mold Smells in Simulated Automobile Interior Contaminant Gases

Itoh

Koyama

Shin

et al. 2020

Sensors

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We investigated the selective detection of target volatile organic compounds (VOCs) which are age-related body odors (namely, 2-nonenal, pelargonic acid, and diacetyl) and a fungal odor (namely, acetic acid) in the presence of interference VOCs from car interiors (namely, n-decane, and butyl acetate). We used eight semiconductive gas sensors as a sensor array; analyzing their signals using machine learning; principal-component analysis (PCA), and linear-discriminant analysis (LDA) as dimensionality-reduction methods; k-nearest-neighbor (kNN) classification to evaluate the accuracy of target-gas determination; and random forest and ReliefF feature selections to choose appropriate sensors from our sensor array. PCA and LDA scores from the sensor responses to each target gas with contaminant gases were generally within the area of each target gas; hence; discrimination between each target gas was nearly achieved. Random forest and ReliefF efficiently reduced the required number of sensors, and kNN verified the quality of target-gas discrimination by each sensor set.

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Sensor array optimization techniques for exhaled breath analysis to discriminate diabetics using an electronic nose

Cited by 17 publications

References 20 publications

A deep neural network with electronic nose for water stress prediction in Khasi Mandarin Orange plants

A deep neural network with electronic nose for water stress prediction in Khasi Mandarin Orange plants

Precise Detection and Quantitative Prediction of Blood Glucose Level With an Electronic Nose System

Selective Detection of Target Volatile Organic Compounds in Contaminated Air Using Sensor Array with Machine Learning: Aging Notes and Mold Smells in Simulated Automobile Interior Contaminant Gases

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