Design of Electronic Nose System Using Gas Chromatography Principle and Surface Acoustic Wave Sensor

Faricha, Anifatul; Suwito, Suwito; Rivai, Muhammad; Nanda, Muhammad Achirul; Purwanto, Djoko; Anhar, R P Rizki

doi:10.12928/telkomnika.v16i4.7127

Cited by 19 publications

(10 citation statements)

References 26 publications

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“…QCM sensor contains a piezoelectric material which is quartz and sandwiched between two electrodes. When a voltage which is electrical signal that consist of electron is applied to the electrode, the piezoelectric effect will convert the electrical signal into mechanical vibration in terms of acoustic wave [22,23]. Then, the mechanical vibration will be converted back to the electrical signal.…”

Section: Design Conceptmentioning

confidence: 99%

Investigation on the mass sensitivity of quartz crystal microbalance gas sensor using finite element simulation

Ralib¹,

Zamri²,

Hazadi³

et al. 2019

Bulletin EEI

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The increasing global trends in healthcare priorities towards improving the effectiveness of diagnostic procedure by utilizing a non-invasive method which is breath analysis. This will benefit in increasing treatment efficiency and also reducing healthcare costs. Breath is a simple technique where the sample are easily obtained and can be provided immediately. The most popular method that had been used in hospital are urine and blood. Contradict with breath, urine and blood take too much time to analyse the disease and a painful process. The detection technique of breath analysis is done by using electroacoustic wave sensor from piezoelectric substrate. This acoustic wave sensor has been used to detect the changes in the frequency where it will be used to detect the disease. Breath analysis is a technique where it uses an electronic nose (E-nose) as a device. E-nose consist of Quartz Crystal Microbalance (QCM) sensor in order to differentiate odor in human breath. QCM is a sensitive weighing device which have a high efficiency. AT-cut quartz was chosen as the piezoelectric material and aluminum as the electrode. The objective of this paper is to design and simulate a QCM sensor for breath analysis application. Other than that, it also to determine the important key parameters that influence the performance of breath analysis which is sensitivity and resonant frequency. QCM sensor is being simulate by using COMSOL Multiphysics software. This is to evaluate the behavior of QCM sensor in terms of Eigen frequency analysis. The simulated QCM sensor with quartz radius of 166 um resonates at 8.871 MHz. The sensitivity of the sensor is 0.167 MHz/ng after exposed to acetone gas which act as the breath marker for detection of diseases in exhaled breath. Hence, the proposed design can be used as a non-invasive approach for early detection of disease through breath analysis.

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Section: Design Conceptmentioning

confidence: 99%

Investigation on the mass sensitivity of quartz crystal microbalance gas sensor using finite element simulation

Ralib¹,

Zamri²,

Hazadi³

et al. 2019

Bulletin EEI

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“…(3,4) Gas chromatograph is one of the best techniques used for the analysis of organic volatile compounds employing various detectors. In order to make the fast analysis researcher optimized the various parameters for development of fast gas chromatography (5)(6)(7)(8)(9) . The sensitivity and resolution are the key parameter of any gas sensor system.…”

Section: Introductionmentioning

confidence: 99%

“…The Flame ionization (FID), Thermal conductivity (TCD), photo-ionization (PID) etc., are non-selective detectors whereas nitrogen phosphorous (NPD), electron capture (ECD), or mass (MS) are selective detectors. The property of few detectors shown in the Table 1 (3)(4)(5)(6)10,11) . SAW detector used as Non-selective GC detector shows the linear response towards the mass change over the surface of the detector (6) .…”

Section: Introductionmentioning

confidence: 99%

“…The property of few detectors shown in the Table 1 (3)(4)(5)(6)10,11) . SAW detector used as Non-selective GC detector shows the linear response towards the mass change over the surface of the detector (6) . To the best of our knowledge, in literature no comparative study of resolution considering two different detectors (SAW/FID) done earlier for fast analysis.…”

Section: Introductionmentioning

confidence: 99%

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GC-SAW and GC-FID performance comparison for fast analysis of volatile organic compounds

Kumar¹

2020

IJST

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Objectives: To develop gas chromatograph (GC) based chemical agent detector and to compare the response of Surface Acoustic Wave (SAW) detector with Flame Ionization Detector (FID) for the development of a reliable and fast gas/vapour analyzer. Also, to describe the limitations of the FID over SAW detector. Methods: An uncoated 433.92 MHz SAW device was used as Gas Chromatography (GC) detector and its response was recorded and compared with conventional FID detector using short capillary column. The response of both the detectors were analyzed by using standard mixture of seven Volatile Organic Compounds (VOCs). Besides this comparison various key parameter of GC i.e. Flow, temperature and length of GC column were also optimized for fast GC analysis. Findings: After analyzing the fast GC data of both the detectors with same sample, it was observed that the resolution of chromatograms with SAW detector showed more resolved peaks as compare to the FID detector for the same GC parameters. Improvements/Applications: It is concluded that the SAW detector is more suitable for fast and reliable analysis of chemical vapors in rapid analysis.

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“…TA adalah kelas A yang diklasifikasikan dengan benar, TB adalah kelas B yang diklasifikasikan dengan benar, TC adalah kelas C yang diklasifikasikan dengan benar. FA1 adalah kelas B yang diklasifikasikan ke dalam kelas A, FA2 adalah kelas C yang diklasifikasikan ke dalam kelas A. FB1 adalah kelas A yang diklasifikasikan ke dalam kelas B, FB2 adalah kelas C yang diklasifikasikan ke dalam kelas B. FC1 adalah kelas A yang diklasifikasikan ke dalam kelas C, FC2 adalah kelas B yang diklasifikasikan ke dalam kelas C[15]. dengan confusion matrix pada Tabel 2, maka TN untuk kelas A yaitu TB+FB2+FC2+TC, FP untuk kelas A yaitu FA1+FA2.…”

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Klasifikasi Jenis Pantun Dengan Metode Support Vector Machines (SVM)

Irmanda¹,

Astriratma²

2020

RESTI

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This study aims to create a model for categorizing pantun types and analyze the accuracy of support vector machines (SVM). The first stage is collecting pantun that have been labeled with pantun category. The pantun categories consist of pantun for children, pantun for young people, and pantun for elder. After collecting data, the next stage is pre-processing. This pre-processing stage makes data ready to be processed on the extraction stage. The pre-processing stage consists of text segmentation, case folding, tokenization, stop word removal, and stemming. The feature extraction stage is intended to analyze potential information and represent terms as a vector. Separating training data and testing data is necessary to be conducted before the classification process. Then the classification process is done by using multiclass SVM. The results of the classification are evaluated to obtain accuracy and will be analyzed whether the classification model is proper to be used. The results showed that SVM classified the types of pantun with accuracy of 81,91%.

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Design of Electronic Nose System Using Gas Chromatography Principle and Surface Acoustic Wave Sensor

Cited by 19 publications

References 26 publications

Investigation on the mass sensitivity of quartz crystal microbalance gas sensor using finite element simulation

Investigation on the mass sensitivity of quartz crystal microbalance gas sensor using finite element simulation

GC-SAW and GC-FID performance comparison for fast analysis of volatile organic compounds

Klasifikasi Jenis Pantun Dengan Metode Support Vector Machines (SVM)

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