A hybrid sensing approach for pure and adulterated honey classification

Sensors (Switzerland)

Volumn 12, Issue 10, 2012, Pages 14022-14040

  Subari, Norazian ^a   Saleh, Junita Mohamad ^a   Shakaff, Ali Yeon Md ^b   Zakaria, Ammar ^a  

^a SCHOOL OF CHEMICAL ENGINEERING   (Malaysia)

^b UNIVERSITY MALAYSIA PERLIS   (Malaysia)

Author keywords

Data fusion; Electronic nose; FTIR; Honey classification; Pure honey

Indexed keywords

CLASSIFICATION ACCURACY; CLASSIFICATION RESULTS; ELECTRONIC NOSE; ELECTRONIC NOSE (E-NOSE); FTIR; LINEAR DISCRIMINANT ANALYSIS; PURE HONEY; STATISTICAL CLASSIFICATION;

DATA FUSION; FOURIER TRANSFORM INFRARED SPECTROSCOPY; PRINCIPAL COMPONENT ANALYSIS; SUGAR INDUSTRY;

FOOD PRODUCTS;

ARTICLE; BEET; CHEMISTRY; CLASSIFICATION; DISCRIMINANT ANALYSIS; ELECTRONIC NOSE; EQUIPMENT; EVALUATION STUDY; FOOD CONTAMINATION; GENETIC PROCEDURES; HONEY; INFRARED SPECTROSCOPY; METHODOLOGY; PRINCIPAL COMPONENT ANALYSIS; SUGAR INTAKE; SUGARCANE;

BETA VULGARIS; BIOSENSING TECHNIQUES; DIETARY SUCROSE; DISCRIMINANT ANALYSIS; ELECTRONIC NOSE; FOOD CONTAMINATION; HONEY; PRINCIPAL COMPONENT ANALYSIS; SACCHARUM; SPECTROSCOPY, FOURIER TRANSFORM INFRARED;

EID: 84868251512     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s121014022     Document Type: Article

References (57)

1. Winder, W.W.; Cancalon, P.F.; Nagy, S. Methods for determining the adulteration of citrus juices. Trends Food Sci. Technol. 1992, 3, 278-286.
2. Gil, M.I.; Cherif, J.; Ayed, N.; Artes, F.; Thomas-Barberfin, F.A. Influence of cultivar, maturity stage and geographical location on the juice pigmentation of Tunisian pomegranates. Zeitschrift für Lebensmittel-Untersuchung und -Forschung 1995, 201, 361-364.
3. Luthy, J. Detection strategies for food authenticity and genetically modified foods. Food Control. 1999, 10, 359-361.
4. Aljadi, A.M.; Kamaruddin, M.Y. Evaluation of the phenolic contents and antioxidant capacities of two Malaysian floral honeys. Food Chem. 2004, 85, 513-518.
5. Silva, L.R.; Videra, R.; Monteria, A.P.; Valento, P.; Andrade, P.B. Honey from Luso region (Portugal): Physicochemical characteristics and mineral contents. Microchem. J. 2009, 93, 73-77.
6. Anklam, E. A review of the analytical methods to determine the geographical and botanical origin of honey. Food Chem.1998, 63, 540-562.
7. Plutowaska, B.; Chmiel, 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.
8. 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.
9. Kropf, U.; Korosec, M.; Bertoncelj, J.; Ogrinc, N.; Necemer, M.; Kump, P.; Golob, T. Determination of the geographical origin of Slovenian black locust, lime and chestnut honey. Food Chem. 2010, 121, 839-846.
10. Roussel, S.; Bellon-maurel, V.; Roger, J.M.; Grenier, P. Authenticating white grape must variety with classification models based on aroma sensors, FT-IR and UV spectrometry. J. Food Eng. 2003, 60, 407-419.
11. Gardner, J.W.; Bartlett, P.N. A brief history of electronic noses. Sens. Actuators B: Chem. 1994, 18-19, 211-220.
12. Benady, M.; Simon, J.E.; Charles, D.J.; Miles, G.E. Fruit Ripeness Determination by Electronic Sensing of Aromatic Sensing. Trans. Am. Soc. Agric. Eng. 1995, 38, 251-257.
13. Simon, J.E.; Hertzoni, A.; Bordelon, B.; Miles, G.E.; Charles, D.J. Electronic sensing of aromatic volatiles for quality sorting of blueberries. J. Food Sci. 1996, 61, 967-969.
14. Schnitzler, W.H.; Broda, S.; Schaller, R.; Zeller, S. Characterization of Internal Quality of Vegetables by Headspace Gas Chromatography and "Electronic Nose". In Proceeding of the XXV International Horticultural Congress. Part 7. Quality of Horticultural Products: Starting Material, Auxiliary Products, Quality Control, Acta-Horticulturae, Brussels, Belgium, 2-7 August 2000; pp. 316-368.
15. Hirschfelder, M.; Ulrich, D.; Hanrieder, D.; Hoberg, E. Rapid discrimination of strawberry varieties using a gas sensor array. Gartenbauwissenschraft 1998, 63, 185-190.
16. Bellon, V. Fermentation control using ATR and an FT-IR spectrometer. Sensor. Actuators B: Chem. 1993, 12, 57-64.
17. Baeten, V.; Aparicio, R. Edible oils and fats authentication by Fourier transform Raman spectrometry. Biotechnol. Agron. Soc. Environ. 2000, 4, 196-203.
18. Dupuy, N.; Duponchel, L.; Huvenne, J.; Sombret, B.; Legrand, P. Classification of edible fats and oils by principal component analysis of Fourier transform infrared spectra. Food Chem. 1996, 57, 245-251.
19. Leon, L.; Kelly, J.D.; Downey, G. Detection of apple juice adulteration using near-infrared transflectance spectroscopy. Appl. Spec.2005, 59, 593-599.
20. Krpan, M.; Markovic, K.; Saric, G.; Skoko, B.; Hruskar, M.; Vahcic, N. Antioxidant activities and total phenolics of acacia honey. Czech J. Food Sci. 2009, 27, S245.
21. Sudhanshu, S.; Satyendra, G.; Arun, S. Physical, biochemical and antioxidant properties of some Indian honeys. Food Chem. 2010, 118, 391-397.
22. Khalil, M.I.; Sulaiman, S.A.; Alam, N.; Moniruzzaman, M.; Bai'e, S.; Man, C.N.; Jamalullail, S.M.S.; Gan, S.H. Gamma irradiation increases the antioxidant properties of Tualang Honey stored under different conditions. Molecules 2012, 17, 674-687.
23. Zakaria, A.; Shakaff, A.Y.M.; Masnan, M.J.; Ahmad, M.N.; Aziz, A.H.A.; Kamarudin, L.M. A biomimetic sensor for the classification of honeys of different floral origin and the detection of adulteration. Sensors 2011, 11, 7799-7822.
24. Markom, M.A.; Shakaff, A.Y.M.; Adom, A.H.; Ahmad, M.N.; Hidayat, W.; Abdullah, A.H. Intelligent electronic nose system for Basal stem rot disease detection. Comput. Electron. Agric. 2009, 66, 140-146.
25. Cajka, T.; Hajslova, J.; Pudil, F.; Riddellova, K. Traceability of honey origin based on volatiles pattern processing by artificial neural networks. J. Chrom. 2009, 1216, 1458-1462.
26. Lee, S.K.; Kimb, J.H.; Sohna, H.J.; Yanga, J.W. Changes in aroma characteristics during the preparation of red ginseng estimated by electronic nose, sensory evaluation and gas chromatography/mass spectrometry. Sensor. Actuators B: Chem. 2005, 106, 7-12.
27. INTOPSY. Available online: http://www.intopsys.com/products/IOS%20C320%20Datasheet.pdf (accessed on 6 October 2012).
28. SENSORSMAG. Available online: http://archives.sensorsmag.com/articles/0800/56/main (accessed on 6 October 2012).
29. Manzoli, A.; Steffens, C.; Paschoalin, R.T.; Correa, A.A.; Alves, W.F.; Leite, F.L.; Herrmann, P.S.P. Low-cost gas sensors produces by the graphite line-patterning technique applied to monitoring banana ripeness. Sensors 2011, 11, 6425-6434.
30. Baldwin, E.A; Bai, J.; Plotto, A.; Dea, S. Electronis noses and tongues: Application for the food and pharmaceutical industries. Sensors 2011, 11, 4744-4766.
31. Mariety, L.; Signolle, J.P.; Amiel, C.; Travert, J. Discrimination, classification, identification, of microorganisms using FTIR spectroscopy and chemometrics. Vib. Spec. 2001, 26, 151-159.
32. Rios-Corripio, M.A.; Rios-Leal, E.; Rojas-Lopez, M.; Delgado-Macuil, R. FTIR characteristization of Mexican honey and its adulteration with sugar syrups by using chemometric methods. J. Phys.: Conf. Ser. 2011, 274, 1-5.
33. Mordenchai, S.; Sahu, R.K.; Hammody, Z.; Mark, S.; Kantarovich, K.; Guterman, H.; Podshywalov, A.; Goldstein, J.; Argov, S. Possible common biomarkers from FTIR microspectroscopy of cervical cancer and melonoma. J. Mic. 2004, 215, 86-91.
34. Romeo, M.J.; Wood, B.R.; Quinn, M.A.; Mcnaughton, D. The removal of blood components from cervical smears: Implications for cervical diagnosis using FTIR spectroscopy. Vib. Spec. 2002, 72, 69-76.
35. Jusman, Y.; Sulaiman, S.N.; Isa, N.A.M.; Yusoff, I.A.; Othman, N.H.; Adnan, R. Capability of New Features from FTIR Spectral of Cervical Cells for Cervical Precancerous Diagnostic System Using MLP Networks. In Proceedings of 2009 IEEE Region 10 Conference, (TENCON) 2009, Singapore, Singapore, 23-26 January 2009; pp. 1-6.
36. Jusman, Y.; Isa, N.A.M.; Sulaiman, S.N.; Adnan, R. Performance of Cervical Cell Spectra for Cervical Pre-Cancerous Screening System Using HMLP Network. In Proceedings of the International Conference on Intelligent Network and Computing (ICINC 2010), Kuala Lumpur, Malaysia, 26-28 November 2010; pp. 405-409.
37. Velazquez, T.G.; Revilla, G.O.; Loa, M.Z.; Espinoza, Y.R. Application of FTIR-HART spectroscopy and multivariate analysis to the quantification of adulterants in Mexican honeys. Food Res. Int. 2009, 42, 313-318.
38. Johnson, R.A.; Wichern, D.W. Applied Multivariate Statistical Analysis, 3rd ed.; Prentice Hall: Englewood Cliffs, NJ, USA, 1992.
39. Dillon, W.R.; Goldstein, M. Multivariate Analysis: Methods and Applications; John Wiley & Sons: New York, NY, USA, 1984.
40. Gardner, J.W. Detection of vapours and odours from a multi-sensor array using pattern recognition. Part 1. Principal components and cluster analysis. Sensor. Actuators B: Chem. 1991, 4, 108-116.
41. Yang, Y.; Cheng, N.P.; Be, Y. A Method of Honey Plant Classification Based on IR Spectrum: Extract Feature Wavelength Using Genetic Algorithm and Classify Using Linear Discriminate Analysis. In Proceedings of Third International Symposium on Intelligent Information Technology and Security Informatics, Jinggangshan, China, 2-4 April 2010; pp. 241-245.
42. Gardner, J.W.; Boilot, P.; Hines, E.L. Enhancing electronic nose performance by sensor selection using a new integer-based genetic algorithm. Sensor. Actuators B: Chem. 2005, 106, 114-121.
43. Shaffer, R.E.; Rose-Pehrsson, S.L.; McGill, R.A. A comparison study of chemical sensor array pattern recognition algorithms. Anal. Chim. Acta 1999, 384, 305-317.
44. Hidayat, W.; Shakaff, A.Y.M.; Ahmad, M.N.; Adom, A.H. Classification of agarwood oil using an electronic nose. Sensors 2010, 10, 4675-4685.
45. Abdullah, M.Z.; Saleh, J.M.; Syahir, A.S.F.; Azemi, B.M.N. Discrimination and classification of fresh-cut starfruits (Averrhoa carambole L.) using automated machine vision system. J. Food Eng. 2006, 76, 506-523.
46. NIST. Available online: http://www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm (accessed on 6 October 2012).
47. Jorma, L.; Martti, J.; Erna, K. Informal Identification of Outliers in Medical Data. In Proceeding of the Fifth International Workshop on Intelligent Data Analysis in Medicine and Pharmacology IDAMAP-2000, Berlin, Germany, 20-25 August 2000.
48. Barnett, V.; Lewis, T. Outliers in Statistical Data, 2nd ed.; John Wiley & Sons, New York, NY, USA, 1985.
49. Li, C.; Heinemann, P.; Sherry, R. Neural network and Bayesian network fusion models to fuse electronic nose and surface wave sensor data for apple defect detection. Sensor. Actuators B: Chem. 2007, 125, 301-310.
50. Boilot, P.; Hines, E.L.; Gongora, M.A.; Folland, R.S. Electronic noses inter-comparison, data fusion and sensor selection in discrimination of standard fruit solutions. Sensor. Actuators B: Chem. 2003, 88, 80-88.
51. Zakaria, A.; Shakaff, A.Y.M.; Adom, A.H.; Ahmad, M.; Masnan, M.J.; Aziz, A.H.A. Improved classification of orthosiphon stamineus by data fusion of electronic nose and tongue sensors. Sensors 2010, 10, 8782-8796.
52. Grover, R.; Brooker, G.; Whyte, H.F.D. A Low Level Fusion of Millimeter Wave Radar and Night-Vision Imaging for Enhanced Characterization of a Cluttered Environment. In Proceedings of Australian Conference on Robotics and Automation, Sydney, Australia, 14-15 November 2001.
53. Steinmetz, V.; Sevila, F.; Maurel, V.B. A Methodology for sensor fusion design: Application to fruit quality assessment. J. Agric. Eng. Res. 1999, 74, 21-31.
54. Ross, A.; Govindarajan, R. Feature level fusion using hand and face biometrics. Proc. SPIE 2005, 5779, 196-204.
55. Vera, L.; Aceña, L.; Guasch, J.; Boqué, R.; Mestres, M.; Busto, O. Discrimination and sensory description of beers through data fusion. Talanta 2011, 87, 136-142.
56. Li, R.; Wang, P.; Hu, W. A novel method for wine analysis based on sensor fusion technique. Sensor. Actuators B: Chem. 2000, 66, 246-250.
57. Vladimir, P. Multi-level image fusion. Proc. SPIE 2003, 5099, 87-96.