UTILIZAÇÃO DO NARIZ ELETRÔNICO (e-nose) COMO FERRAMENTA DE ANÁLISE RÁPIDA DE ALIMENTOS
DOI:
https://doi.org/10.35172/rvz.2020.v27.524Palavras-chave:
análise de alimentos, método alternativo, nariz eletrônico, sensoresResumo
O Nariz Eletrônico é um equipamento de análise de odores empregado em diversas áreas, como em análise de alimentos e controle ambiental. Sua função consiste em mimetizar o nariz humano por meio de sensores que reagem com componentes voláteis e gases liberados de uma amostra, resultando na modificação de um circuito elétrico que é interpretada por um software. O seu uso na análise de alimentos é promissor devido a sua facilidade de manuseio, baixo custo e rapidez, indicando benefícios para a rotina industrial e laboratorial. Sua aplicabilidade abrange a detecção de fraudes, deterioração, contaminações e sabores/odores, viabilizando a avaliação da qualidade de diversos produtos, além de discriminar patógenos e deteriorantes de alimentos. Apesar de promissora, esta tecnologia é considerada nova, necessitando de estudos mais aprofundados para que seja amplamente utilizado na rotina de análise de alimentos. Portanto, o objetivo deste estudo é fornecer uma revisão de literatura relacionada à aplicabilidade do e-nose na análise de alimentos.
Referências
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2. Loutfi A, Coradeschi S, Mani GK, Shankar P, Rayappan JBB. Electronic noses for food quality: A review. J Food Eng. 2015;144:103–11.
3. Ghasemi-Varnamkhasti M, Mohtasebi SS, Siadat M, Balasubramanian S. Meat quality assessment by electronic nose (Machine Olfaction Technology). Sensors. 2009;9(8):6058–83.
4. Wang Q, Li L, Ding W, Zhang D, Wang J, Reed K, et al. Adulterant identification in mutton by electronic nose and gas chromatography-mass spectrometer. Food Control. 2019;98(November 2018):431–8.
5. Firestein S. How the olfactory system makes sense of scents. Nature [Internet]. 2001;413(6852):211–8.
6. Netto MMO, Gonçalves WB, Li RWC, Gruber J. Biopolymer based ionogels as active layers in low-cost gas sensors for electronic noses. Sensors Actuators, B Chem. 2020;315(November 2019):128025.
7. NASA. Electronic Nose: NASA researchers are developing an exquisitely sensitive artificial nose for space exploration. [Internet]. [cited 2020 Jun 6]. Available from: https://science.nasa.gov/science-news/science-at-nasa/2004/06oct_enose
8. NASA. An Introduction to JPL’s ENose [Internet]. [cited 2020 Jun 6]. Available from: https://enose.jpl.nasa.gov/
9. Schaller E, Bosset JO, Escher F. “Electronic noses” and their application to food. LWT - Food Sci Technol. 1998;31(4):305–16.
10. Di Francesco F, Lazzerini B, Marcelloni F, Pioggia G. An electronic nose for odour annoyance assessment. Atmos Environ. 2001;35(7):1225–34.
11. Gostelow P, Parsons SA, Stuetz RM. Odour measurements for sewage treatment works. Water Res. 2001;35(3):579–97.
12. Sohn JH, Smith RJ, Yoong E. Process studies of odour emissions from effluent ponds using machine-based odour measurement. Atmos Environ. 2006;40(7):1230–41.
13. Wilson AD. Review of Electronic-nose Technologies and Algorithms to Detect Hazardous Chemicals in the Environment. Procedia Technol. 2012;1:453–63.
14. Arroyo T, Lozano J, Cabellos JM, Gil-Diaz M, Santos JP, Horrillo C. Evaluation of wine aromatic compounds by a sensory human panel and an electronic nose. J Agric Food Chem. 2009;57(24):11543–9.
15. Prieto N, Rodriguez-Méndez ML, Leardi R, Oliveri P, Hernando-Esquisabel D, Iñiguez-Crespo M, et al. Application of multi-way analysis to UV-visible spectroscopy, gas chromatography and electronic nose data for wine ageing evaluation. Anal Chim Acta. 2012;719:43–51.
16. Berna AZ, Trowell S, Clifford D, Cynkar W, Cozzolino D. Geographical origin of Sauvignon Blanc wines predicted by mass spectrometry and metal oxide based electronic nose. Anal Chim Acta. 2009;648(2):146–52.
17. Dutta R, Hines EL, Gardner JW, Kashwan KR, Bhuyan M. Tea quality prediction using a tin oxide-based electronic nose: An artificial intelligence approach. Sensors Actuators, B Chem. 2003;94(2):228–37.
18. Bhattacharyya N, Bandyopadhyay R, Bhuyan M, Tudu B, Ghosh D, Jana A. Electronic nose for black tea classification and correlation of measurements with “Tea taster” marks. IEEE Trans Instrum Meas. 2008;57(7):1313–21.
19. Nurjuliana M, Che Man YB, Mat Hashim D, Mohamed AKS. Rapid identification of pork for halal authentication using the electronic nose and gas chromatography mass spectrometer with headspace analyzer. Meat Sci. 2011;88(4):638–44.
20. El Barbri N, Llobet E, El Bari N, Correig X, Bouchikhi B. Electronic nose based on metal oxide semiconductor sensors as an alternative technique for the spoilage classification of red meat. Sensors. 2008;8(1):142–56.
21. Winquist F, Hornsten EG, Sundgren H, Lundstrom I. Performance of an electronic nose for quality estimation of ground meat. Meas Sci Technol. 1993;4(12):1493–500.
22. Yano Y, Yokoyama K, Tamiya E, Karube I. Direct evaluation of meat spoilage and the progress of aging using biosensors. Anal Chim Acta. 1996;320(2–3):269–76.
23. Olsen E, Vogt G, Ekeberg D, Sandbakk M, Pettersen J, Nilsson A. Analysis of the early stages of lipid oxidation in freeze-stored pork back fat and mechanically recovered poultry meat. J Agric Food Chem. 2005;53(2):338–48.
24. Miao H, Liu Q, Bao H, Wang X, Miao S. Effects of different freshness on the quality of cooked tuna steak. Innov Food Sci Emerg Technol [Internet]. 2017;44:67–73.
25. GholamHosseini H, Luo D, Liu H, Xu G. Intelligent processing of e-nose information for fish freshness assessment. Proc 2007 Int Conf Intell Sensors, Sens Networks Inf Process ISSNIP. 2007;173–7.
26. Gholam Hosseini H, Luo D, Xu G, Liu H, Benjamin D. Intelligent Fish Freshness Assessment. J Sensors. 2008;2008:1–8.
27. Eriksson Å, Waller KP, Svennersten-Sjaunja K, Haugen JE, Lundby F, Lind O. Detection of mastitic milk using a gas-sensor array system (electronic nose). Int Dairy J. 2005;15(12):1193–201.
28. Balasubramanian S, Panigrahi S, Logue CM, Marchello M, Sherwood JS. Identification of Salmonella-inoculated beef using a portable electronic nose system. J Rapid Methods Autom Microbiol. 2005;13(2):71–95.
29. Younts S, Alocilja EC, Osburn WN, Marquie S, Grooms DL. Differentiation of Escherichia coli 0157:H7 from non–0157:H7 E. coli serotypes using a gas sensor–based, computer–controlled detection system. Trans ASAE. 2002;45(5):1681–5.
30. Pereira JG, Gonçalves WB, Teixeira WSR, Sampaio ANCE, Mioni M R, Martins AO, Megid J, Gruber J. Fast detection of foodborne pathogens: an interdisciplinary approach. In: 30o Congresso Brasileiro de Microbiologia; 2019, Maceió; 2019.
31. Pereira JG, Gonçalves WB, Teixeira WSR, Sampaio ANCE, Mioni M R, Martins AO, Megid J, Gruber J. Electronic nose based on ionogel doped with Fe3O4 particles applied for discrimination of spoilage and pathogenic microorganisms in raw meats. In: 30o Congresso Brasileiro de Microbiologia; 2019, Maceió; 2019.
32. Keshri G, Magan N, Voysey P. Use of an electronic nose for the early detection and differentiation between spoilage fungi. Lett Appl Microbiol. 1998;27(5):261–4.
33. Gardner JW, Craven M, Dow C, Hines EL. The prediction of bacteria type and culture growth phase by an electronic nose with a multi-layer perceptron network. Meas Sci Technol. 1998;9(1):120–7.
34. Elliott-Martin RJ, Mottram TT, Gardner JW, Hobbs PJ, Bartlett PN. Preliminary investigation of breath sampling as a monitor of health in dairy cattle. J Agric Eng Res. 1997;67(4):267–75.
35. Medelius PJ. Nano sensors for gas detection in space and ground support applications. Proc MNT Aerosp Appl CANEUS2006. 2006;2006:1–5.
2. Loutfi A, Coradeschi S, Mani GK, Shankar P, Rayappan JBB. Electronic noses for food quality: A review. J Food Eng. 2015;144:103–11.
3. Ghasemi-Varnamkhasti M, Mohtasebi SS, Siadat M, Balasubramanian S. Meat quality assessment by electronic nose (Machine Olfaction Technology). Sensors. 2009;9(8):6058–83.
4. Wang Q, Li L, Ding W, Zhang D, Wang J, Reed K, et al. Adulterant identification in mutton by electronic nose and gas chromatography-mass spectrometer. Food Control. 2019;98(November 2018):431–8.
5. Firestein S. How the olfactory system makes sense of scents. Nature [Internet]. 2001;413(6852):211–8.
6. Netto MMO, Gonçalves WB, Li RWC, Gruber J. Biopolymer based ionogels as active layers in low-cost gas sensors for electronic noses. Sensors Actuators, B Chem. 2020;315(November 2019):128025.
7. NASA. Electronic Nose: NASA researchers are developing an exquisitely sensitive artificial nose for space exploration. [Internet]. [cited 2020 Jun 6]. Available from: https://science.nasa.gov/science-news/science-at-nasa/2004/06oct_enose
8. NASA. An Introduction to JPL’s ENose [Internet]. [cited 2020 Jun 6]. Available from: https://enose.jpl.nasa.gov/
9. Schaller E, Bosset JO, Escher F. “Electronic noses” and their application to food. LWT - Food Sci Technol. 1998;31(4):305–16.
10. Di Francesco F, Lazzerini B, Marcelloni F, Pioggia G. An electronic nose for odour annoyance assessment. Atmos Environ. 2001;35(7):1225–34.
11. Gostelow P, Parsons SA, Stuetz RM. Odour measurements for sewage treatment works. Water Res. 2001;35(3):579–97.
12. Sohn JH, Smith RJ, Yoong E. Process studies of odour emissions from effluent ponds using machine-based odour measurement. Atmos Environ. 2006;40(7):1230–41.
13. Wilson AD. Review of Electronic-nose Technologies and Algorithms to Detect Hazardous Chemicals in the Environment. Procedia Technol. 2012;1:453–63.
14. Arroyo T, Lozano J, Cabellos JM, Gil-Diaz M, Santos JP, Horrillo C. Evaluation of wine aromatic compounds by a sensory human panel and an electronic nose. J Agric Food Chem. 2009;57(24):11543–9.
15. Prieto N, Rodriguez-Méndez ML, Leardi R, Oliveri P, Hernando-Esquisabel D, Iñiguez-Crespo M, et al. Application of multi-way analysis to UV-visible spectroscopy, gas chromatography and electronic nose data for wine ageing evaluation. Anal Chim Acta. 2012;719:43–51.
16. Berna AZ, Trowell S, Clifford D, Cynkar W, Cozzolino D. Geographical origin of Sauvignon Blanc wines predicted by mass spectrometry and metal oxide based electronic nose. Anal Chim Acta. 2009;648(2):146–52.
17. Dutta R, Hines EL, Gardner JW, Kashwan KR, Bhuyan M. Tea quality prediction using a tin oxide-based electronic nose: An artificial intelligence approach. Sensors Actuators, B Chem. 2003;94(2):228–37.
18. Bhattacharyya N, Bandyopadhyay R, Bhuyan M, Tudu B, Ghosh D, Jana A. Electronic nose for black tea classification and correlation of measurements with “Tea taster” marks. IEEE Trans Instrum Meas. 2008;57(7):1313–21.
19. Nurjuliana M, Che Man YB, Mat Hashim D, Mohamed AKS. Rapid identification of pork for halal authentication using the electronic nose and gas chromatography mass spectrometer with headspace analyzer. Meat Sci. 2011;88(4):638–44.
20. El Barbri N, Llobet E, El Bari N, Correig X, Bouchikhi B. Electronic nose based on metal oxide semiconductor sensors as an alternative technique for the spoilage classification of red meat. Sensors. 2008;8(1):142–56.
21. Winquist F, Hornsten EG, Sundgren H, Lundstrom I. Performance of an electronic nose for quality estimation of ground meat. Meas Sci Technol. 1993;4(12):1493–500.
22. Yano Y, Yokoyama K, Tamiya E, Karube I. Direct evaluation of meat spoilage and the progress of aging using biosensors. Anal Chim Acta. 1996;320(2–3):269–76.
23. Olsen E, Vogt G, Ekeberg D, Sandbakk M, Pettersen J, Nilsson A. Analysis of the early stages of lipid oxidation in freeze-stored pork back fat and mechanically recovered poultry meat. J Agric Food Chem. 2005;53(2):338–48.
24. Miao H, Liu Q, Bao H, Wang X, Miao S. Effects of different freshness on the quality of cooked tuna steak. Innov Food Sci Emerg Technol [Internet]. 2017;44:67–73.
25. GholamHosseini H, Luo D, Liu H, Xu G. Intelligent processing of e-nose information for fish freshness assessment. Proc 2007 Int Conf Intell Sensors, Sens Networks Inf Process ISSNIP. 2007;173–7.
26. Gholam Hosseini H, Luo D, Xu G, Liu H, Benjamin D. Intelligent Fish Freshness Assessment. J Sensors. 2008;2008:1–8.
27. Eriksson Å, Waller KP, Svennersten-Sjaunja K, Haugen JE, Lundby F, Lind O. Detection of mastitic milk using a gas-sensor array system (electronic nose). Int Dairy J. 2005;15(12):1193–201.
28. Balasubramanian S, Panigrahi S, Logue CM, Marchello M, Sherwood JS. Identification of Salmonella-inoculated beef using a portable electronic nose system. J Rapid Methods Autom Microbiol. 2005;13(2):71–95.
29. Younts S, Alocilja EC, Osburn WN, Marquie S, Grooms DL. Differentiation of Escherichia coli 0157:H7 from non–0157:H7 E. coli serotypes using a gas sensor–based, computer–controlled detection system. Trans ASAE. 2002;45(5):1681–5.
30. Pereira JG, Gonçalves WB, Teixeira WSR, Sampaio ANCE, Mioni M R, Martins AO, Megid J, Gruber J. Fast detection of foodborne pathogens: an interdisciplinary approach. In: 30o Congresso Brasileiro de Microbiologia; 2019, Maceió; 2019.
31. Pereira JG, Gonçalves WB, Teixeira WSR, Sampaio ANCE, Mioni M R, Martins AO, Megid J, Gruber J. Electronic nose based on ionogel doped with Fe3O4 particles applied for discrimination of spoilage and pathogenic microorganisms in raw meats. In: 30o Congresso Brasileiro de Microbiologia; 2019, Maceió; 2019.
32. Keshri G, Magan N, Voysey P. Use of an electronic nose for the early detection and differentiation between spoilage fungi. Lett Appl Microbiol. 1998;27(5):261–4.
33. Gardner JW, Craven M, Dow C, Hines EL. The prediction of bacteria type and culture growth phase by an electronic nose with a multi-layer perceptron network. Meas Sci Technol. 1998;9(1):120–7.
34. Elliott-Martin RJ, Mottram TT, Gardner JW, Hobbs PJ, Bartlett PN. Preliminary investigation of breath sampling as a monitor of health in dairy cattle. J Agric Eng Res. 1997;67(4):267–75.
35. Medelius PJ. Nano sensors for gas detection in space and ground support applications. Proc MNT Aerosp Appl CANEUS2006. 2006;2006:1–5.
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2020-12-06
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Nunes de Moraes G, Pereira JG. UTILIZAÇÃO DO NARIZ ELETRÔNICO (e-nose) COMO FERRAMENTA DE ANÁLISE RÁPIDA DE ALIMENTOS. RVZ [Internet]. 6º de dezembro de 2020 [citado 17º de dezembro de 2024];27:1-10. Disponível em: https://rvz.emnuvens.com.br/rvz/article/view/524
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