Detection of diabetes from gas analysis of human breath using e-Nose

Hariyanto, Hariyanto; Sarno, Riyanarto; Wijaya, Dedy Rahman

doi:10.1109/icts.2017.8265677

Cited by 35 publications

(28 citation statements)

References 11 publications

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“…Moreover, studies have been carried out on breath samples where it has been detected VOCs for the screening of people who may have diabetes, obtaining sensitivities and specificities of 91.5% and 90.7% respectively; with this, is proposing this type of monitoring systems of blood glucose as a complement to the standard criteria (K. Yan, Zhang, Wu, Wei, & Lu, 2014). Other study was able to detect patterns of breath VOCs through an eNOSE (electrochemical gas sensors), being able to distinguish patients with T2D and clinically healthy people, with yields of 95% accuracy, 91.3% precision of diabetes, 94.12% precision of healthy and 0.898 kappa statistic's value, using k‐NN classifier (Hariyanto & Wijaya, 2017). In addition, a study conducted the detection of VOC patterns for discrimination of urine samples from people with diabetes and control subjects through two types of electronic nose, founding that with the electronic nose FAIMS obtained sensitivities above 90% and specificities greater than 80%, and with the FOX 4000 they obtained sensitivities and specificities above 90% (Esfahani, Wicaksono, Mozdiak, Arasaradnam, & Covington, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…The electronic nose (eNOSE) has emerged as an analytical technique for the identification of volatile organic metabolites and has recently been employed in the non‐invasive monitoring of various molecular components. In this regard, chemical compounds found in urine and exhaled breath have been linked to metabolic reactions and different health conditions like renal diseases (Lin et al, 2001; Saidi et al, 2018), diabetes (Hariyanto & Wijaya, 2017; Ping, Yi, Haibao, & Farong, 1997; Siyang, Kerdcharoen, & Wongchoosuk, 2012; K. Yan & Zhang, 2014), breast cancer (Phillips et al, 2003), etc. Moreover, eNOSE systems are portable devices that provide real‐time data (Dymerski et al, 2013; Finamore et al, 2018; Santini et al, 2016); also, the eNose based on ultrafast GC (FGC eNose) is a promising technology to separate and identify different VOCs through chemometric analysis.…”

Section: Introductionmentioning

confidence: 99%

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Identification of metabolic markers in patients with type 2 Diabetes by Ultrafast gas chromatography coupled to electronic nose. A pilot study

Méndez-Rodríguez

Figueroa-Vega

Ilizaliturri-Hernández

et al. 2020

Biomedical Chromatography

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Metabolomics is a potential tool for the discovery of new biomarkers in the early diagnosis of diseases. An ultra‐fast gas chromatography system equipped to an electronic nose detector (FGC eNose) was used to identify the metabolomic profile of Volatile Organic Compounds (VOCs) in type 2 diabetes (T2D) urine from Mexican population. A cross‐sectional, comparative, and clinical study with translational approach was performed. We recruited twenty T2D patients and twenty‐one healthy subjects. Urine samples were taken and analyzed by FGC eNose. Eighty‐eight compounds were identified through Kovats's indexes. A natural variation of 30% between the metabolites, expressed by study groups, was observed in Principal Component 1 and 2 with a significant difference (p < 0.001). The model, performed through a Canonical Analysis of Principal coordinated (CAP), allowed a correct classification of 84.6% between healthy and T2D patients, with a 15.4% error. The metabolites 2‐propenal, 2‐propanol, butane‐ 2,3‐dione and 2‐methylpropanal, were increased in patients with T2D, and they were strongly correlated with discrimination between clinically healthy people and T2D patients. This study identified metabolites in urine through FGC eNose that can be used as biomarkers in the identification of T2D patients. However, more studies are needed for its implementation in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of metabolic markers in patients with type 2 Diabetes by Ultrafast gas chromatography coupled to electronic nose. A pilot study

Méndez-Rodríguez

Figueroa-Vega

Ilizaliturri-Hernández

et al. 2020

Biomedical Chromatography

View full text Add to dashboard Cite

show abstract

“…Audio Signature Type from the extraction process is then applied in the sliding algorithm and k-NN using Bhattacharyya distance. In this experiment, k-NN was used because it has been successfully reported for favorable performance in non-stationary signal processing [17], [22], [23]. The details of this process are depicted in Figure 1.…”

Section: Song Recognition Methodsmentioning

confidence: 99%

Music fingerprinting based on bhattacharya distance for song and cover song recognition

Sarno¹,

Wijaya²,

Mahardika³

2019

IJECE

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People often have trouble recognizing a song especially, if the song is sung by a not original artist which is called cover song. Hence, an identification system might be used to help recognize a song or to detect copyright violation. In this study, we try to recognize a song and a cover song by using the fingerprint of the song represented by features extracted from MPEG-7. The fingerprint of the song is represented by Audio Signature Type. Moreover, the fingerprint of the cover song is represented by Audio Spectrum Flatness and Audio Spectrum Projection. Furthermore, we propose a sliding algorithm and k-Nearest Neighbor (k-NN) with Bhattacharyya distance for song recognition and cover song recognition. The results of this experiment show that the proposed fingerprint technique has an accuracy of 100% for song recognition and an accuracy of 85.3% for cover song recognition.

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“…The different sample has different volatiles that acts as biomarkers. For example, e-noses have been used to various applications such as blood glucose level detection [2], halal authentication [3], [4], meat quality detection [5]- [10], classifying vegetable oils and animal fats [11], tea classification [12], [13], monitoring tempeh fermentation [14], etc. In e-nose application, not optimal sensor array susceptible to overlapping selectivity which means…”

Section: Introductionmentioning

confidence: 99%

Stability Assessment of Feature Selection Algorithms on Homogeneous Datasets: A Study for Sensor Array Optimization Problem

Wijaya

Afianti

2020

IEEE Access

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A feature selection algorithm (FSA) is used to eliminate redundant and irrelevant features. Obviously, it can reduce dimensionality as well as the complexity of the original problem. Furthermore, the stability of FSA output becomes a major issue in real-world applications. Stability refers to the consistency of its feature preference related to the perturbation of data samples. In sensor array optimization, an FSA is used to find the best sensor combination in a sensor array. Typically, the main objectives of sensor array optimization are reducing data dimensions, electrical consumption, production cost, computational and traffic overhead, etc. Furthermore, the stable outputs of FSA in several observations are necessary to make a firm conclusion of selected sensors. The contribution of this research is to investigate the stability of FSAs in twelve homogeneous datasets in relation to the sensor array optimization problem. In this study, the stability of seventeen filter-based FSAs is compared across twelve homogeneous datasets. These datasets are generated from the electronic nose (e-nose) used to monitor twelve types of beef cuts. In this case, gas sensor array must have good generalization to differentiate all beef types. The experimental results show that a single FSA cannot guarantee stable sensors recommendation in sensor array optimization. Thus, it becomes a caution to researchers and practitioners to use a proper approach when performing sensor array optimization.

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Detection of diabetes from gas analysis of human breath using e-Nose

Cited by 35 publications

References 11 publications

Identification of metabolic markers in patients with type 2 Diabetes by Ultrafast gas chromatography coupled to electronic nose. A pilot study

Identification of metabolic markers in patients with type 2 Diabetes by Ultrafast gas chromatography coupled to electronic nose. A pilot study

Music fingerprinting based on bhattacharya distance for song and cover song recognition

Stability Assessment of Feature Selection Algorithms on Homogeneous Datasets: A Study for Sensor Array Optimization Problem

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