Ge-NOSE: Electronic Nose for Sniffing Food-Borne Bacteria

Pudji Astutı; Prima Febri Astantrı; Wredha Prakoso; Claude Mona Aırın; Tri Untarı; Kuwat Trıyana

Research Article

Year 2019, Volume: 8 , 56 - 59, 10.12.2019

Pudji Astutı Prima Febri Astantrı Wredha Prakoso Claude Mona Aırın Tri Untarı Kuwat Trıyana

Abstract

References

Arnold J.W & Senter, S.D., 2012. Use of digital aroma technology and SPME GC–MS to compare volatile compounds produced by bacteria isolated from processed poultry, Journal of the Science of Food and Agriculture 78 (3) (1998) 343–348. Astantri, P.F. 2017. Pemanfaatan Electronic Nose (E-nose) Untuk Deteksi Listeria monocytogenes dan Bacillus cereus Berbasis Sensor Gas. Thesis. Universitas Gadjah Mada, Yogyakarta, Indonesia Capone, S., Epifani, M., Quaranta F., Siciliano, P., Taurino A., & Vasanelli, L., 2001. Monitoring of rancidity of milk by means of an electronic nose and a dynamic PCA analysis. Sensors and Actuators B: Chemical 78, 174-9. Dwidjoseputro, D., 1998. Dasar-dasar Mikrobiologi. Djambatan: Jakarta. Hal:59-62 Evans P., Persaud, K.C., Mcneish A.S., Sneath, R.W., Hobson, N., Magan, N., 2000. Evaluation of a radial basis function neural network for the determination of wheat quality from electronic nose data. Sensors and Actuators B: Chemical 69, 348-58. Gates, K,W., 2011. Rapid Detection and Characterization of Foodborne Pathogens by Molecular Techniques. Journal of Aquatic Food Product Technology 20, 108-113. Haugen, J.E., & Kvaal, K., 1998. Electronic nose and artificial neural network. Meat Sci 49, pp.273-S86. Kuwat,T., Subekti M. T., Aji, P., Hidayat S.N.,. Rohman A., 2015. Development of Electronic Nose with Low-Cost Dynamic Headspace for Classifying Vegetable Oils and Animal Fats. Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.771.50. Prakoso, W.,S., A., 2017. Pemanfaatan Electronic Nose Untuk Deteksi Escherichia Coli dan Salmonella Thypimurium Berdasarkan Sensor Gas. Thesis. Universitas Gadjah Mada, Yogyakarta, Indonesia. 2017. Thesis. Universitas Gadjah Mada, Yogyakarta, Indonesia Rosyad, F, D., & Lelono, 2015. Klasifikasi Kemurnian Daging Sapi Berbasis Electronic nose dengan metode Principal Component Analysis, IJEIS Vol.6 No.1, April 2016 pp.47-58. Tait, E., Perry, J.D., Stanforth, S.P. & Dean, J.R., 2014, “Identification of Volatile Organic Compounds Produced by Bacteria Using HS-SPME-GC – MS”, Journal of Chromatography, Volume 52, 363–373. Wilson, A.D., 2015. Advances in electronic-nose technologies for the detection of volatile biomarker metabolites in the human breath. Metabolites. 2015 Mar 2;5(1):140-63. doi: 10.3390/metabo5010140. Yu Y. X., Sun X.H., Pan Y.J., Zhao Y.Y., 2015. Research on Food-borne Pathogen Detection Based on Electronic Nose. Chemistry online (in Chinese), 154-9. Yu, Y.,X. & Zhao, Y. 2012. “Electronic nose integrated with chemometrics for rapid identification of foodborne pathogen”, Chemometrics in Practical Applications, Intech. Last Date access 17 Agust 2017

Ge-NOSE: Electronic Nose for Sniffing Food-Borne Bacteria

Year 2019, Volume: 8 , 56 - 59, 10.12.2019

Pudji Astutı Prima Febri Astantrı Wredha Prakoso Claude Mona Aırın Tri Untarı Kuwat Trıyana

Abstract

Gastronomy practice has become major attraction in tourism also promote food importation globally. So, controlling bacterial contamination to comply biosecurity regulations is one of imperative task for quarantine services. However detection method of bacteria causing food poisoning is laborious. Electronic nose technology has ability to recognise volatile compounds (VOCs) emitted by biological materials. Recently, the Elecetronic Nose is one of the best choice since it does not need reagen, cheap and fast. To proof-of concept, an investigation was carried out employing Ge-Nose (Universitas Gadjah Mada, Indonesia) to captured volatile emission of four food-borne bacteria: E.coli (ATCC 25922), S.thypimurium (ATCC 14028), L.monocytogenes 4b (ATCC 13932) and B.cereus (ATCC 10876). All of sample were then incubated at 37°C for 2, 8, 16, 24, 32, 40, and 48 hours then analysed using different methods such as Linear Discriminan Analysis (LDA), Quadratic Discriminant Analysis (QDA), and SupportVectorMachine(SVM). The result showed, using LDA methods, accuracy value of E.coli was 97.80±2.20%; S.thypimurium: 94.60±5.40%: ; L.monocytogenes 98.00±2.00%: and B.cereus 95.00±5.00%. Using QDA methods, the accuarcy value of E.coli was 94.80±5.20%; S.thypimurium: 95.60±4.40%: ; L.monocytogenes 92.00±8.00%: and B.cereus 95.00±5.00%; whereas SVM methods, it has been showed: E.coli was 97.00±3.00%; S.thypimurium: 92.40±7.60%: ; L.monocytogenes 89.00±11.00%: and B.cereus 89.00±11.00%. Highest accuracy classification average (98%) was achieved. Therefore, Ge-NOSE's discriminate power is able to deliver faster, accurate yet simple and inexpensive diagnostic result.

Keywords

Electronic Nose , E.coli , S.thypimurium , L.monocytogenes , B.cereus

References

Arnold J.W & Senter, S.D., 2012. Use of digital aroma technology and SPME GC–MS to compare volatile compounds produced by bacteria isolated from processed poultry, Journal of the Science of Food and Agriculture 78 (3) (1998) 343–348. Astantri, P.F. 2017. Pemanfaatan Electronic Nose (E-nose) Untuk Deteksi Listeria monocytogenes dan Bacillus cereus Berbasis Sensor Gas. Thesis. Universitas Gadjah Mada, Yogyakarta, Indonesia Capone, S., Epifani, M., Quaranta F., Siciliano, P., Taurino A., & Vasanelli, L., 2001. Monitoring of rancidity of milk by means of an electronic nose and a dynamic PCA analysis. Sensors and Actuators B: Chemical 78, 174-9. Dwidjoseputro, D., 1998. Dasar-dasar Mikrobiologi. Djambatan: Jakarta. Hal:59-62 Evans P., Persaud, K.C., Mcneish A.S., Sneath, R.W., Hobson, N., Magan, N., 2000. Evaluation of a radial basis function neural network for the determination of wheat quality from electronic nose data. Sensors and Actuators B: Chemical 69, 348-58. Gates, K,W., 2011. Rapid Detection and Characterization of Foodborne Pathogens by Molecular Techniques. Journal of Aquatic Food Product Technology 20, 108-113. Haugen, J.E., & Kvaal, K., 1998. Electronic nose and artificial neural network. Meat Sci 49, pp.273-S86. Kuwat,T., Subekti M. T., Aji, P., Hidayat S.N.,. Rohman A., 2015. Development of Electronic Nose with Low-Cost Dynamic Headspace for Classifying Vegetable Oils and Animal Fats. Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.771.50. Prakoso, W.,S., A., 2017. Pemanfaatan Electronic Nose Untuk Deteksi Escherichia Coli dan Salmonella Thypimurium Berdasarkan Sensor Gas. Thesis. Universitas Gadjah Mada, Yogyakarta, Indonesia. 2017. Thesis. Universitas Gadjah Mada, Yogyakarta, Indonesia Rosyad, F, D., & Lelono, 2015. Klasifikasi Kemurnian Daging Sapi Berbasis Electronic nose dengan metode Principal Component Analysis, IJEIS Vol.6 No.1, April 2016 pp.47-58. Tait, E., Perry, J.D., Stanforth, S.P. & Dean, J.R., 2014, “Identification of Volatile Organic Compounds Produced by Bacteria Using HS-SPME-GC – MS”, Journal of Chromatography, Volume 52, 363–373. Wilson, A.D., 2015. Advances in electronic-nose technologies for the detection of volatile biomarker metabolites in the human breath. Metabolites. 2015 Mar 2;5(1):140-63. doi: 10.3390/metabo5010140. Yu Y. X., Sun X.H., Pan Y.J., Zhao Y.Y., 2015. Research on Food-borne Pathogen Detection Based on Electronic Nose. Chemistry online (in Chinese), 154-9. Yu, Y.,X. & Zhao, Y. 2012. “Electronic nose integrated with chemometrics for rapid identification of foodborne pathogen”, Chemometrics in Practical Applications, Intech. Last Date access 17 Agust 2017

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Details

Primary Language	English
Subjects	Engineering
Journal Section	Articles
Authors	Pudji Astutı Prima Febri Astantrı Wredha Prakoso Claude Mona Aırın Tri Untarı Kuwat Trıyana
Publication Date	December 10, 2019
Published in Issue	Year 2019 Volume: 8

Cite

APA	Astutı, P., Astantrı, P. F., Prakoso, W., … Aırın, C. M. (2019). Ge-NOSE: Electronic Nose for Sniffing Food-Borne Bacteria. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 8, 56-59.