Classification of different floral origin honey samples using a machine olfaction system

Document Type : Research Paper



Honey is a sweet and viscous liquid made by bees from nectar of flowers. The emitted smell by honey depending on flower variety can be different. These factors led to use of machine olfactory system based on metal oxide sensors(MOS) in order to classify different floral origin honeys. Seven samples of different floral origins of honey with a total of 70 samples from each of 10 samples were tested. Principal component analyze (PCA), linear discriminant analyze (LDA) and artificial neural network (ANN) were methods to classify and analyze the extracted features from the machine olfactory system signal that were used. To classify floral origin honey using the machine olfaction, the results was included a 97% variance by PCA, 87.3% and 88.5% accuracy classification, respectively of LDA and ANN. As a conclusion, it was found that the electronic nose could provide good classification among of honeys.


Main Subjects

Ampuero, S; Bogdanov, S; Bosset, J-O (2004) Classification of unifloral honeys with an MS-based electronic nose using different sampling modes: SHS, SPME and INDEX. European Food Research and Technology, 218(2), 198-207.
Anklam, E (1998) A review of the analytical methods to determine the geographical and botanical origin of honey. Food Chemistry, 63(4), 549-562. DOI: 10.1016/s0308-8146(98)00057-0.
Arshak, K.; Moore, E.; Lyons, G.M.; Harris, J.; Clifford, S. A review of gas sensors employed in electronic nose applications. Sensor Review. 2004, 24(2), 181–198.
Benedetti, S; Mannino, S; Sabatini, A G; Marcazzan, G L (2004) Electronic nose and neural network use for the classification of honey. Apidologie, 35(4), 397-402.
Brezmes, J; Llobet, E; Vilanova, X; Saiz, G; Correig, X. (2000) Fruit ripeness monitoring using an electronic nose. Sensors and Actuators B 69(3), 223-229.
Cotte, J F; Casabianca, H; Chardon, S; Lheritier, J; Grenierloustalot, M F (2003) Application of carbohydrate analysis to verify honey authenticity. Journal of Chromatography, A 102 (1-2), 145-155.
Cotte, J F; Casabianca, H; Giroud, B; Albert, M; Lheritier, J; Grenier- Loustalot, M. F. (2004) Characterization of honey amino acid profiles using high-pressure liquid chromatography to control authenticity. Analytical & Bioanalytical Chemistry, 378(5), 1342-1350.
Cozzolino, D., Corbella, E., & Smyth, H. (2011). Quality control of honey using infrared spectroscopy: a review. Applied Spectroscopy Reviews, 46(7), 523–538.
Drake, M A; Gerard, P D; Kleinhenz, J P; Harper, W J (2003) Application of an electronic nose to correlate with descriptive sensory analysis of aged Cheddar cheese. Lebensmittel- Wissenschaft Und-Technologie – Food Science and Technology, 36(1): 13-20.
Eklöv, T; Johansson, G; Winquist, F; Lundström, I (1998) Monitoring sausage fermentation using an electronic nose. Journal of the Science of Food & Agriculture, 76, 525-532.
Frane Čačić Kenjerić, Saverio Mannino, Simona Bennedetti, Ljiljana Primorac & Daniela Čačić Kenjerić .(2009). Honey botanical origin determination by electronic nose, Journal of Apicultural Research, 48, 2, 99-103.
Ghasemi-Varnamkhasti, M.; Mohtasebi, S.S.; Rodriguez-Mendez, M.L.; Lozano, J.; Razavi, S.H.; Ahmadi, H. Potential application of electronic nose technology in brewery. Trends in Food Science & Technology. 2011, 22, 165-174.
Haykin, S. Neural Networks: A Comprehensive Foundation. Prentice Hall, New Jersey.1999.
Hermosín, I; Chicón, R M; Cabezudo, M D (2003) Free amino acid composition and botanical origin of honey. Food Chemistry. 83(2), 263-268.
Jurs, P.C.; Bakken, G.A.; McClelland, H.E. Computational Methods for the Analysis of Chemical Sensor Array Data from Volatile Analytes. Chemical Reviews. 2000, 100, 2649 2678.
Heidarbeigi, K., Mohtasebi, S. S., Foroughirad, A., Ghasemi-Varnamkhasti, M., Rafiee, S., & Rezaei, K. (2015). Detection of adulteration in saffron samples using electronic nose.  International Journal of Food Properties, 18(7), 1391-1401.
Lammertyn, J; Veraverbeke, E A; Irudayaraj, J (2004) zNose™ technology for the classification of honey based on rapid aroma profiling. Sensors and Actuators, B98(1), 54-62.
Mateo, R; Bosch-Reig, F (1998) Classification of Spanish unifloral honeys by discriminant analysis of electrical conductivity, colour, water content, sugars, and pH, Journal of Agricultural and Food Chemistry, 46(2), 393-400.
O'connell, M; Valdora, G; Peltzer, G; Negri, R M (2001) A practical approach for fish freshness determination using a portable electronic nose. Sensors and Actuators, B 80(2), 149-154.
Ojeda De Rodríguez, G; Sulbarán De Ferrer, B; Ferrer, A; Rodríguez, B (2004) Characterization of honey produced in Venezuela. Food Chemistry, 84(4), 499-502.
O'riordan, P J; Delahunty, C M (2003) Characterisation of commercial Cheddar cheese flavour. 1: traditional and electronic nose approach to quality assessment and market classification International Dairy Journal, 13(5), 355-370.
Pearce T.C., Gardner J.W., Friel S., Barlett P.N., Blair N. (2003) Electronic nose for monitoring the flavour of beers, Analyst, 118, 371–377.
Peris, M., & Escuder-Gilabert, L. (2009). A 21st century technique for food control: electronic noses. Analytica Chimica Acta, 638(1), 1–15.
Persaud, K; Dodd, G (1982) Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose. Nature, 299, 352-355.
Tomás-Barberán, F; Martos, I; Ferres, F; Radovic, S B; Anklam, E (2001) HPLC flavonoid profiles as markers for the botanical origin of European unifloral honeys. Journal of the Science of Food and Agriculture, 81(5), 485-496.
Tudu, B., Kow, B., Bhattacharyya, N., & Bandyopadhyay, R. (2008, November). Comparison of multivariate normalization techniques as applied to electronic nose based pattern classification for black tea. In Sensing Technology, 2008. ICST 2008. 3rd International Conference on (pp. 254-258). IEEE.
Wang, J., Kliks, M. M., Jun, S., Jackson, M., & Li, Q. X. (2010). Rapid analysis of glucose, fructose, sucrose, and maltose in honeys from different geographic regions using Fourier transform infrared spectroscopy and multivariate analysis. Journal of Food Science, 75(2), C208–C214.