The Electronic Nose Coupled with Chemometric Tools for Discriminating the Quality of Black Tea Samples In Situ

Hidayat, Shidiq Nur; Triyana, Kuwat; Fauzan, Inggrit; Julian, Trisna; Lelono, Danang; Yusuf, Yusril; Ngadiman, Nor Hasrul Akhmal; Veloso, Ana C. A.; Peres, António M.

doi:10.3390/chemosensors7030029

Cited by 37 publications

(15 citation statements)

References 45 publications

(69 reference statements)

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“…We use the Metal-Oxide-Semiconductor (MOS)-based gas sensors. All sensors used in the eNose-TB device are commercial sensors available in the market with a high level of replication [17]. There are 16 sensors used, with the characteristics of global selectivity.…”

Section: Plos Onementioning

confidence: 99%

eNose-TB: A trial study protocol of electronic nose for tuberculosis screening in Indonesia

et al. 2021

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Background Even though conceptually, Tuberculosis (TB) is almost always curable, it is currently the world’s leading infectious killer. Patients with pulmonary TB are the source of transmission. Approximately 23% of the world’s population is believed to be latently infected with TB bacteria, and 5–15% of them will progress at any point in time to develop the disease. There was a global diagnostic gap of 2.9 million between notifications of new cases and the estimated number of incident cases, and Indonesia carries the third-highest of this gap. Therefore, screening TB among the community is of great importance to prevent further transmission and infection. The electronic nose for screening TB (eNose-TB) project is initiated in Yogyakarta, Indonesia, to screen TB by breath test with an electronic-nose that is easy-to-use, point-of-care, does not expose patients to radiation, and can be produced at low cost. Methods/Design The objectives of the two-phase planned project are to: 1) investigate the potential of an eNose-TB as a screening tool in Indonesia, in comparison with screening with clinical symptoms and chest radiology, which are currently used as a standard, and 2) analyze the time and cost of a screening algorithm with eNose-TB to obtain additional case detection. A cross-sectional study will be conducted in the first phase to validate the eNose-TB. The validation phase will involve 395 presumptive TB patients in the Surakarta General Hospital, Central Java. In the second phase, a cross-sectional research will be conducted, involving 1,383 adults and children in the municipality of Yogyakarta and Kulon Progo district of Yogyakarta Province. Discussion The findings will provide data concerning the sensitivity and specificity of the eNose-TB as a screening tool for tuberculosis, and the time and cost analysis of a screening algorithm with the eNose. Trial registration NCT04567498; https://clinicaltrials.gov/.

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Section: Plos Onementioning

confidence: 99%

eNose-TB: A trial study protocol of electronic nose for tuberculosis screening in Indonesia

et al. 2021

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“…Subsequently, 3 mL of inoculated TSB was transferred into a sterile falcon tube and covered with a sterile vial plastic bottle. The samples then incubated at 37 • C and e-nose measurement was performed sequentially at 2,8,16,24,32,40, and 48 h. For reidentification, the L. monocytogenes was Gram stained, followed by being cultured in blood agar, listeria selective agar (LSA), and Christie Atkins Munch Peterson (CAMP). On the other hand, B. cereus was Gram and spore stained, followed by being cultured in blood agar, Bacillus cereus agar (BCA) and mannitol egg yolk polymixin agar (MYP).…”

Section: Sample Preparationmentioning

confidence: 99%

“…Table 1. Sensor types used in the lab-made e-nose and the targeted volatile compounds [24]. Figure 2a shows the electrical schematic of the recording signal from the sensor in the e-nose.…”

Section: Electronic Nose Specificationmentioning

confidence: 99%

Lab-Made Electronic Nose for Fast Detection of Listeria monocytogenes and Bacillus cereus

Astantri

Prakoso

Triyana

et al. 2020

Veterinary Sciences

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The aim of this study is to determine the performance of a lab-made electronic nose (e-nose) composed of an array of metal oxide semiconductor (MOS) gas sensors in the detection and differentiation of Listeria monocytogenes (L. monocytogenes) and Bacillus cereus (B. cereus) incubated in trypticsoy broth (TSB) media. Conventionally, the detection of L. monocytogenes and B. cereus is often performed by enzyme link immunosorbent assay (ELISA) and polymerase chain reaction (PCR). These techniques require trained operators and expert, expensive reagents and specific containment. In this study, three types of samples, namely, TSB media, L. monocytogenes (serotype 4b American Type Culture Collection (ATCC) 13792), and B. cereus (ATCC) 10876, were used for this experiment. Prior to measurement using the e-nose, each bacterium was inoculated in TSB at 1 × 10 3 -10 4 CFU/mL, followed by incubation for 48 h. To evaluate the performance of the e-nose, the measured data were then analyzed with chemometric models, namely linear and quadratic discriminant analysis (LDA and QDA), and support vector machine (SVM). As a result, the e-nose coupled with SVM showeda high accuracy of 98% in discriminating between TSB media and L. monocytogenes, and between TSB media and B. cereus. It could be concluded that the lab-made e-nose is able to detect rapidly the presence of bacteria L. monocytogenes and B. cereus on TSB media. For the future, it could be used to identify the presence of L. monocytogenes or B. cereus contamination in the routine and fast assessment of food products in animal quarantine.Listeria monocytogenes causes the highest case of hospitalization (up to 91%) among other foodborne illnesses [4]. Listeriosis is infectious to humans and mammals, including the ruminant and monogastric animals. The clinical signs of listeriosis in humans include gastroenteritis, diarrhea, meningitis, bacteremia, and it causes encephalitis, septicemia, abortion, mastitis, and gastroenteritis in cows [5,6]. Member of genus Listeria is a non-spore bacterium, being anaerobic facultative, a small size, Gram-positive, and rod-shaped (0.5-4.0 µm diameter and 0.5-2.0 µm long). Listeria monocytogenes can contaminate a wide range of foods, including yogurt, cheese, meat, ham, smoked salmon, poultry, seafood and vegetable products [2,7].Bacillus cereus is a facultative aerobic to anaerobic, Gram-positive, rod-shaped, and spore-forming bacteria. Spore endurance to unfavorable conditions has assisted the widespread of Bacillus [8,9]. Although the culture method is the gold standard for bacteria identification, it is inefficient, time-consuming (more than 1 week), requires laboratory operator expertise, and identification depends on specific microbiological and biochemical testing [7,9,10]. Besides these methods, there are also other detection methods such as polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA). However, the PCR-based technique requires sophisticated equipment, complicated techniques, and lengthy processes such as ...

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“…A lab-made E-nose with eight MOS gas sensors (TGS series) along with relative humidity and temperature sensor, was used to analyze cocoa bean samples. The eight TGS series of gas sensor (TGS 822, TGS 2612, TGS 2620, TGS 832, TGS 826, TGS 2603, TGS 2600, TGS 813) were equipped in the device as well as the most targeted gases as listed in our previous work [27]. Here, the Sensirion SHT-31 sensor was used as relative humidity and temperature sensor.…”

Section: Electronic Nose Apparatus and Analysismentioning

confidence: 99%

“…Indeed, E-nose is usually low-cost devices, user-friendly interfaces and do not require complex sample pre-treatments nor experienced operators. The E-nose is a device that aims to mimic the working concept of the human nose through a set of gas sensor arrays that are able to detect and distinguish different VOCs [24][25][26][27]. So, some studies reported the use of E-noses for cocoa bean analysis.…”

Section: Introductionmentioning

confidence: 99%

Electronic Nose Coupled with Linear and Nonlinear Supervised Learning Methods for Rapid Discriminating Quality Grades of Superior Java Cocoa Beans

Hidayat¹,

Rusman²,

Julian³

et al. 2019

IJIES

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An electronic nose (E-nose), comprising eight metal oxide semiconductor (MOS) gas sensors and a moisture-temperature sensor, was used for classifying three quality grades of superior java cocoa beans, namely fine cocoa dark bean < 20%, fine cocoa dark bean > 60%, and bulk cocoa bean that is a harder task compared to the discrimination of high versus low-quality cocoa beans. The E-nose signals were pre-processed using the maximum value method. The capability for discriminating the quality grade of the cocoa beans was checked by applying multivariate statistical tools, namely, linear discriminant analysis (LDA), support vector machine (SVM) and artificial neural networks (ANN). For this, the experimental dataset was split into two subsets, one for training (i.e., establishing the classification models) and the other for external-validation purposes. Furthermore, hyperparameter optimization and K-fold cross-validation variant were implemented during the model training procedure to select the best classification models and to avoid over-fitting issues. The best predictive classification performance was obtained with the E-nose-MLP-ANN procedure, which allowed 99% of correct classifications (overall accuracy) for the training dataset and 95% of correct classifications (overall accuracy) for the external-validation dataset. The satisfactory results clearly demonstrated that the E-nose could be applied as a quality control tool in the cocoa industry, requiring minimum and simple sample preparation.

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The Electronic Nose Coupled with Chemometric Tools for Discriminating the Quality of Black Tea Samples In Situ

Cited by 37 publications

References 45 publications

eNose-TB: A trial study protocol of electronic nose for tuberculosis screening in Indonesia

eNose-TB: A trial study protocol of electronic nose for tuberculosis screening in Indonesia

Lab-Made Electronic Nose for Fast Detection of Listeria monocytogenes and Bacillus cereus

Electronic Nose Coupled with Linear and Nonlinear Supervised Learning Methods for Rapid Discriminating Quality Grades of Superior Java Cocoa Beans

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