A biomimetic sensor for the classification of honeys of different floral origin and the detection of adulteration

Sensors

Volumn 11, Issue 8, 2011, Pages 7799-7822

  Zakaria, Ammar ^a   Shakaff, Ali Yeon Md ^a   Masnan, Maz Jamilah ^a   Ahmad, Mohd Noor ^a   Adom, Abdul Hamid ^a   Jaafar, Mahmad Nor ^a   Ghani, Supri A ^a   Abdullah, Abu Hassan ^a   Aziz, Abdul Hallis Abdul ^a   Kamarudin, Latifah Munirah ^a   Subari, Norazian ^a   Fikri, Nazifah Ahmad ^a  

^a UNIVERSITY MALAYSIA PERLIS   (Malaysia)

Author keywords

Bio mimicking sensor; Electronic nose; Electronic tongue; Floral origin and adulteration; Honey classification

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOMIMETICS; CHEMICAL ANALYSIS; ELECTRONICS; FLOWER; FOOD ANALYSIS; GENETIC PROCEDURES; HONEY; INFRARED SPECTROSCOPY; MALAYSIA; MASS FRAGMENTOGRAPHY; METHODOLOGY; NEW ZEALAND; PH; POTENTIOMETRY; STATISTICAL MODEL;

BIOMIMETICS; BIOSENSING TECHNIQUES; CHEMISTRY TECHNIQUES, ANALYTICAL; ELECTRONICS; FLOWERS; FOOD ANALYSIS; GAS CHROMATOGRAPHY-MASS SPECTROMETRY; HONEY; HYDROGEN-ION CONCENTRATION; LINEAR MODELS; MALAYSIA; NEURAL NETWORKS (COMPUTER); NEW ZEALAND; POTENTIOMETRY; SPECTROSCOPY, FOURIER TRANSFORM INFRARED;

EID: 80052185229     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s110807799     Document Type: Article

References (48)

1. Buettener, A.; Beauchamp, J. Chemical input-Sensory output: Diverse modes of physiology- flavour interaction. Food Qual. Prefer. 2010, 21, 915-924.
2. Robertsson, L.; Iliev, B.; Palm, R.; Wide, P. Perception modeling for human-like artificial sensor systems. Int. J. Human Comput. St. 2007, 65, 446-459.
3. Linder, R.; Poppl, S.J. A new neural network approach classifies olfactory signals with high accuracy. Food Qual. Prefer. 2003, 14, 435-440.
4. Bleibaum, R.N.; Stone, H.; Tan, T.; Labreche, S.; Saint-Martin, E.; Isz, S. Comparison of sensory and consumer results with electronic nose and tongue sensors for apple juices. Food Qual. Prefer. 2002, 13, 409-422.
5. Zakaria, A.; Shakaff, A.Y.M.; Adom, A.H.; Ahmad, M.; Masnan, M.J.; Aziz, A.H.A.; Fikri, N.A.; Abdullah, A.H.; Kamarudin, L.M. Improved classification of orthosiphon stamineus by data fusion of electronic nose and tongue sensors. Sensors 2010, 10, 8782-8796.
6. Steinmetz, V.; Sevila, F.; Bellon-Maurel, V. A Methodology for sensor fusion design: Application to fruit quality assessment. J. Agric. Eng. Res. 1999, 74, 21-31.
7. Apetrei, C.; Apetrei, I.M.; Villanueva, S.; de Saja, J.A.; Gutierrez-Rosales, F.; Rodriquez-Mendez, M.L. Combination of an e-nose, an e-tongue and an e-eye for the characterization of olive oils with different degree of bitterness. Anal. Chim. Acta 2010, 663, 91-97.
8. Laureati, M.; Buratti, Bassoli, A.; Borgonovo, G.; Pagliarini, E. Discrimination and characterization of three cultivars of Perilla frutescens by means of sensory descriptors and electronic nose and tongue analysis. Food Res. Int. 2010, 43, 959-964.
9. Ghasemi-Varnamkhasti, M.; Mohtasebi, S.S.; Siadat, M. Biomimetic-based odor and taste sensing systems to food quality and safety characterization: An overview on basic principles and recent achievements. J. Food. Eng. 2010, 100, 377-387.
10. Cosio, M.S.; Ballabio, D.; Benedetti, S.; Gigliotti, C. Evaluation of different storage conditions of extra virgin olive oils with an innovative recognition tool built by means of electronic nose and electronic tongue. Food Chem. 2007, 101, 485-491.
11. Zhu, X.; Li, S.; Shan, Y.; Zhang, Z.; Li, G.; Su, D.; Liu, F. Detection of adulterants such as sweeteners materials in honey using near-infrared spectroscopy and chemometrics. J. Food Eng. 2010, 101, 92-97.
12. Silva, L.R.; Videira, R.; Monteiro, A.P.; Valentao, P.; Andrade, P.B. Honey from Luso region (Portugal): Physicochemical characteristics and mineral contents. Microchem. J. 2009, 93, 73-77.
13. Gallardo-Velazquez, T.; Osorio-Revilla, G.; Loa, M.Z.; Rivera-Espinoza, Y. Application of FTIR-HATR spectroscopy and multivariate analysis to the quantification of adulterants in Mexican honeys. Food Res. Int. 2009, 42,313-318.
14. Anklam, E. A review of the analytical methods to determine the geographical and botanical origin of honey. Food Chem. 1998, 63, 540-562.
15. Plutowaska, B.; Chmiel, T.; Dymerski, T.; Wardencki, W. A headspace solid-phase microextraction method development and its application in the determination of volatiles in honeys by gas chromatography. Food Chem. 2011, 126, 1288-1298.
16. Cuevas-Glory, L.F.; Pino, J.A.; Santiago, L.S.; Sauri-Duch, E. A review of volatile analytical methods for determining the botanical origin of honey. Food Chem. 2007, 103, 1032-1043.
17. Kropf, U.; Korosec, M.; Bertoncelj, Jasna., Ogrinc, N.; Necemer, M.; Kump, P.; Golob, T. Type=contributor>Determination of the geographical origin of Slovenian black locust, lime and chestnut honey. Food Chem. 2010, 121, 839-846.
18. Stephan, A.; Bucking, M.; Steinhart, H. Novel analytical tools for food flavours. Food Res. Int. 2000, 33, 199-209.
19. Lee, S.K.; Kim, J.H.; Sohn, H.J.; Yang, J.W. Changes in aroma characteristics during the preparation of red ginseng estimated by electronic nose, sensory evaluation and gas chromatography/mass spectrometry. Sens. Actuat. B Chem. 2005, 106, 7-12.
20. Aishima, T. Correlating sensory attributes to gas chromatography-mass spectrometry profiles and e-nose responses using partial least squares regression analysis. J. Chromatogr. A 2004, 1054, 39-46.
21. Kolb, B.; Ettre, L.S. Statis Headspace-Gas Chromatography: Theory and Practice, 2nd ed.; Willey-Interscience: Hoboken, NJ, USA, 2006.
22. Alissandrakis, E.; Tarantilis, P.A.; Harizanis, C.; Pollissiou, M. Aroma investigation of monofloral Greek citrus honey using solid-phase microextraction coupled to gas chromatographic-mass spectrometric analysis. Food Chem. 2007, 100, 396-404.
23. Acquarone, C.; Buera, P.; Elizalde, B. Pattern of pH and electrical conductivity upon honey dilution as a complementary tool for discriminating geographical origin of honeys. Food Chem. 2007, 101, 695-703.
24. Dias, L.A.; Peres, A.M.; Vilas-Boas, M.; Rocha, M.A.; Estevinho, L.; Machado, A.A.S.C. An electronic tongue for honey classification. Microchim. Acta 2008, 163, 97.
25. Hidayat, W.; Shakaff, A.Y.M.; Ahmad, M.N.; Adom, A.H. Classification of agarwood oil using an electronic nose. Sensor 2010, 10, 4675-4685.
26. Markom, M.A.; Shakaff A.Y.M.; Adom, A.H.; Ahmad, M.N.; Hidayat W.; Abdullah, A.H.; Fikri, N.A. Intelligent electronic nose system for basal stem rot disease detection. Comput. Electron. Agr. 2009, 66, 140-146.
27. Persaud, K.; Dodd, G. Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose. Nature 1982, 299, 352-355.
28. Legin, A.M.; Rudnitskaya, A.; Yu. G.; Vlasov, A.V.; Di Natale, C.; D'Amico, A. The features of the electronic tongue in comparison with the characteristics of the discrete ion-selective sensors. Sens. Actuat. B Chem. 1999, 58, 464-468.
29. Vlasov, Y.; Legin, A.; Rudnitskaya, A.; D'Amico, A.; Di Natale, C. Electronic tongue-new analytical tool for liquid analysis on the basis of non-specific sensors and methods of pattern recognition. Sens. Actuat. B Chem. 2000, 65, 235-236.
30. Toko, K. Taste sensor. Sens. Actuat. B Chem. 2000, 64, 205-215.
31. Woertz, K.; Tissen, C.; Kleinebudde, P.; Breitkreutz, J. Taste sensing systems (electronic tongues) for pharmaceutical application. Int. J. Pharmaceut. 2010, In press.
32. Dias, L.G.; Peres, A.M.; Barcelos, T.P.; Morais, J.S.; Machado, A.A.S.C. Semi-quantitative and quantitative analysis of soft drinks using an electronic tongue. Sens. Actuat. B Chem. 2010, In Press.
33. Hansen, T.; Petersen, M.A.; Byrne, D.V. Sensory based quality control utilizing an electronic nose and GC-MS analyses to predict end-product quality from raw materials. Meat Sci. 2005, 69, 621-634.
34. Berrueta, L.A.; Alonso-Slaces, R.M.; Heberger, K. Supervised pattern recognition in food analysis. J. Chromatogr. A 2007, 1158, 196-214.
35. Gardner, J.W.; Bartlett, P.N. Electronic Noses: Principles and Applications; Oxford University Press: Oxford, UK, 1999.
36. Chatfield, C.; Collins, A.J. Introduction to Multivariate Analysis; Chapman and Hall: London, UK, 1980.
37. Hardle, W.; Simar, L. Applied Multivariate Statistical Analysis; Springer: Berlin, Germany, 2007.
38. Hagan, M.T.; Demuth, H.B.; Beale. M.H. Neural Network Design; International Thomson Publishing Inc.: Boston, MA, USA, 1996.
39. Di Natale, C.; Paolesse, R.; Macagnano, A.; Mantini, A.; D'Amico, A.; Legin, A.; Lvova, L.; Rudnitskaya, A.; Vlasov, Y. Electronic nose and electronic tongue integration for improved classification of clinical and food samples. Sens. Actuat. B Chem. 2000, 64, 15-21.
40. Tabachnick, B.G.; Fidell, L.S. Using Multivariate Statistics; Pearson Education, Upper Saddle River, NJ, USA, 2007.
41. Wei, Z.; Wang, J.; Wang, Y. Classification of monofloral honeys from different floral origins and geographical origins based on rheometer. J. Food. Eng. 2010, 96, 469-479.
42. Won, S.; Lee, D.; Ko, S.H.; Kim, J.; Rhee, H. Honey major protein characterization and its application to adulteration detection. Food Res. Int. 2008, 41, 952-956.
43. Guler, A.; Bakan, A.; Nisbet. C.; Yavuz, O. Determination of important biochemical properties of honey to discriminate pure and adulterated honey with sucrose (Succharum officinarum L.) syrup. Food Chem. 2007, 105, 1119-1125.
44. Mendes, E.; Proenca, E.B.; Ferreira, I.M.P.L.V.O.; Ferreira, M.A. Quality evaluation of Portuguese honey. Carbohyd. Polym. 1998, 37, 219-223.
45. Saxena, S.; Gautam, S.; Sharma, A. Physical, biochemical and antioxidant properties of some Indian honeys. Food Chem. 2010, 118, 391-397.
46. Cajka, T.; Hajslova, J.; Pudil, F.; Riddellova, K. Traceability of honey origin based on volatiles pattern processing by artificial neural networks. J. Chromatogr. A 2009, 1216, 1458-1462.
47. Lammertyn, J.; Veraverbeke, E.A.; Irudayaraj, J. zNoseTM technology for the classification of honey based on rapid aroma profiling. Sens. Actuat. B Chem. 2004, 98, 54-62.
48. Delwiche, J.F. The impact of perceptual interactions on perceived flavor. Food Qual. Prefer. 2004, 15, 137-146.