2,037
Views
5
CrossRef citations to date
0
Altmetric
Original Article

Intelligent electronic tongue system for the classification of genuine and false honeys

ORCID Icon, , , , ORCID Icon & ORCID Icon
Pages 327-343 | Received 26 Sep 2022, Accepted 19 Dec 2022, Published online: 02 Jan 2023

References

  • da Silva, P. M.; Gauche, C.; Gonzaga, L. V.; Costa, A. C. O.; Fett, R. Honey: Chemical Composition, Stability and Authenticity. Food Chem. 2016, 196, 309–323. DOI: 10.1016/j.foodchem.2015.09.051.
  • Valinger, D.; Longin, L.; Grbeš, F.; Benković, M.; Jurina, T.; Kljusurić, J. G.; Tušek, A. J. Detection of Honey adulteration–the Potential of UV-vis and Nir Spectroscopy Coupled with Multivariate Analysis. LWT. 2021, 145, 111316. DOI: 10.1016/j.lwt.2021.111316.
  • Naila, A.; Flint, S. H.; Sulaiman, A. Z.; Ajit, A.; Weeds, Z. Classical and Novel Approaches to the Analysis of Honey and Detection of Adulterants. Food Control. 2018, 90, 152–165. DOI: 10.1016/j.foodcont.2018.02.027.
  • Bázár, G.; Romvári, R.; Szabó, A.; Somogyi, T.; Éles, V.; Tsenkova, R. Nir Detection of Honey Adulteration Reveals Differences in Water Spectral Pattern. Food Chem. 2016, 194, 873–880. DOI: 10.1016/j.foodchem.2015.08.092.
  • Guler, A.; Kocaokutgen, H.; Garipoglu, A. V.; Onder, H.; Ekinci, D.; Biyik, S. Detection of Adulterated Honey Produced by Honeybee (Apis mellifera L.) Colonies Fed with Different Levels of Commercial Industrial Sugar (c3 and c4 Plants) Syrups by the Carbon Isotope Ratio Analysis. Food Chem. 2014, 155, 155–160. DOI: 10.1016/j.foodchem.2014.01.033.
  • Puścion-Jakubik, A.; Borawska, M. H.; Socha, K. Modern Methods for Assessing the Quality of Bee Honey and Botanical Origin Identification. Foods. 2020, 9(8), 1028. DOI: 10.3390/foods9081028.
  • Naila, A.; Flint, S. H.; Sulaiman, A. Z.; Ajit, A.; Weeds, Z. Classical and Novel Approaches to the Analysis of Honey and Detection of Adulterants. Food Control. 2018, 90, 152–165. DOI: 10.1016/j.foodcont.2018.02.027.
  • Vlasov, Y.; Legin, A.; Rudnitskaya, A.; Di Natale, C.; D’amico, A. Nonspecific Sensor Arrays (“ Electronic Tongue”) for Chemical Analysis of Liquids (IUPAC Technical Report. Pure Appl. Chem. 2005, 77(11), 1965–1983. DOI: 10.1351/pac200577111965.
  • Apetrei, I.; Apetrei, C. Detection of Virgin Olive Oil Adulteration Using a Voltammetric e-tongue. Comput. Electron. Agric. 2014, 108, 148–154. DOI: 10.1016/j.compag.2014.08.002.
  • Ouyang, Q.; Yang, Y.; Wu, J.; Liu, Z.; Chen, X.; Dong, C.; Chen, Q.; Zhang, Z.; Guo, Z. Rapid Sensing of Total Theaflavins Content in Black Tea Using a Portable Electronic Tongue System Coupled to Efficient Variables Selection Algorithms. J. Food Compost. Anal. 2019, 75, 43–48. DOI: 10.1016/j.jfca.2018.09.014.
  • de Morais, T. C. B.; Rodrigues, D. R.; D. C. P. Souto, U. T.; Lemos, S. G. A Simple Voltammetric Electronic Tongue for the Analysis of Coffee Adulterations. Food Chem. 2019, 273, 31–38. DOI: 10.1016/j.foodchem.2018.04.136.
  • Winquist, F.; Lundström, I.; Wide, P. The Combination of an Electronic Tongue and an Electronic Nose. Sens. Actuators B Chem. 1999, 58(1–3), 512–517. DOI: 10.1016/S0925-4005(99)00155-0.
  • Winquist, F.; Krantz-Rülcker, C.; Lundström, I. Electronic Tongues and Combinations of Artificial Senses. Sens. Update. 2002, 11(1), 279–306. DOI: 10.1002/seup.200211107.
  • Winquist, F. Voltammetric Electronic tongues–basic Principles and Applications. Microchim. Acta. 2008, 163, 3–10. 1-2 DOI: 10.1007/s00604-007-0929-2.
  • Ciursa, P.; Oroian, M. Voltammetric e-tongue for Honey Adulteration Detection. Sensors. 2021, 21(15), 5059. DOI: 10.3390/s21155059.
  • Maione, C.; Barbosa, F.; Barbosa, R. M. Predicting the Botanical and Geographical Origin of Honey with Multivariate Data Analysis and Machine Learning Techniques: A Review. Comput. Electron. Agric. 2019, 157(January), 436–446. DOI: 10.1016/j.compag.2019.01.020.
  • Bougrini, M.; Tahri, K.; Saidi, T.; El Alami El Hassani, N.; Bouchikhi, B.; El Bari, N. Classification of Honey according to Geographical and Botanical Origins and Detection of Its Adulteration Using Voltammetric Electronic Tongue. Food Analytical Methods. 2016, 9(8), 2161–2173. DOI: 10.1007/s12161-015-0393-2.
  • Cai, J.; Wu, X.; Yuan, L.; Han, E.; Zhou, L.; Zhou, A. Determination of Chinese Angelica Honey Adulterated with Rice Syrup by an Electrochemical Sensor and Chemometrics. Anal. Methods. 2013, 5(9), 2324–2328. DOI: 10.1039/c3ay00041a.
  • Gan, Z.; Yang, Y.; Li, J.; Wen, X.; Zhu, M.; Jiang, Y.; Ni, Y. Using Sensor and Spectral Analysis to Classify Botanical Origin and Determine Adulteration of Raw Honey. J. Food Eng. 2016, 178, 151–158. DOI: 10.1016/j.jfoodeng.2016.01.016.
  • Juan-Borrás, M.; Soto, J.; Gil-Sánchez, L.; Pascual-Maté, A.; Escriche, I. Antioxidant Activity and physico-chemical Parameters for the Differentiation of Honey Using a Potentiometric Electronic Tongue. J. Sci. Food Agric. 2016, 97(7), 2215–2222. DOI: 10.1002/jsfa.8031.
  • Oroian, M.; Paduret, S.; Ropciuc, S. Honey Adulteration Detection: Voltammetric e-tongue versus Official Methods for Physicochemical Parameter Determination. J. Sci. Food Agric. 2018, 98(11), 4304–4311. DOI: 10.1002/jsfa.8956.
  • Oroian, M.; Ropciuc, S. Romanian Honey Authentication Using Voltammetric Electronic Tongue. Correlation of Voltammetric Data with physico-chemical Parameters and Phenolic Compounds. Comput. Electron. Agric. 2019, 157, 371–379. DOI: 10.1016/j.compag.2019.01.008.
  • Pauliuc, D.; Dranca, F.; Oroian, M. Raspberry, Rape, Thyme, Sunflower and Mint Honeys Authentication Using Voltammetric Tongue. Sensors. 2020, 20(9), 2565. DOI: 10.3390/s20092565.
  • Tiwari, K.; Tudu, B.; Bandyopadhyay, R.; Chatterjee, A. Identification of Monofloral Honey Using Voltammetric Electronic Tongue. J. Food Eng. 2013, 117(2), 205–210. DOI: 10.1016/j.jfoodeng.2013.02.023.
  • Sobrino-Gregorio, L.; Bataller, R.; Soto, J.; Escriche, I. ,Food Control 91. 2018. Monitoring Honey Adulteration with Sugar Syrups Using an Automatic Pulse Voltammetric Electronic Tongue, 254–260. 10 January 2022. doi:10.1016/j.foodcont201804003
  • Veloso, A. C.; Sousa, M. E.; Estevinho, L.; Dias, L. G.; Peres, A. M. Honey Evaluation Using Electronic Tongues: An Overview. Chemosensors. 2018, 6(3), 1–25. DOI: 10.3390/chemosensors6030028.
  • Leon-Medina, J. X.; Vejar, M. A.; Tibaduiza, D. A., Signal Processing and Pattern Recognition in Electronic Tongues: A Review, In Burgos, D. A. T., Vejar, M. A., Pozo, F. (Eds.), Pattern Recognition Applications in Engineering, IGI Global Diego Tibaduiza, Maribel Anaya, Francesc Pozo, Hershey, PA, USA: IGI Global, 2020, pp. 84–108. 10 January 2022. doi:10.4018/978-1-7998-1839-7.ch004.
  • Leon-Medina, J. X.; Cardenas-Flechas, L. J.; Tibaduiza, D. A. A data-driven Methodology for the Classification of Different Liquids in Artificial Taste Recognition Applications with A Pulse Voltammetric Electronic Tongue. Int. J. Distribut. Sensor Net. 2019, 15(10), . arXiv. DOI: 10.1177/1550147719881601.
  • Leon-Medina, J. X.; Anaya, M.; Pozo, F.; Tibaduiza, D. 27 August. Nonlinear Feature Extraction through Manifold Learning in an Electronic Tongue Classification Task, Sensors 20 (17). https://www.mdpi.com/1424-8220/20/17/4834. 10 January 2022 2020.
  • Villamil-Cubillos, L. F.; Leon-Medina, J. X.; Anaya, M.; Tibaduiza, D. A. 14 November . Evaluation of Feature Selection Techniques in a Multifrequency Large Amplitude Pulse Voltammetric Electronic Tongue, Eng. Proceed. 2 (1). https://www.mdpi.com/2673-4591/2/1/62. 10 January 2022 2020
  • Leon-Medina, J. X.; Pineda-Muñoz, W. A.; Burgos, D. A. T. Joint Distribution Adaptation for Drift Correction in Electronic Nose Type Sensor Arrays. IEEE Access. 2020, 8, 134413–134421. DOI: 10.1109/ACCESS.2020.3010711.
  • Chin, N. L.; Sowndhararajan, K., A Review on Analytical Methods for Honey Classification, Identification and Authentication, In V. de Alencar Arnaut de Toledo, E. D. Chambó (Eds.), Honey Analysis, IntechOpen. 34 10 January 2022, Rijeka, 2020, V. de Alencar Arnaut de Toledo, E. D. Chambó (Eds.). 5 Vagner De Alencar, Arnaut De Toledo, Emerson Dechechi, Chambó. doi:10.5772/intechopen.90232
  • David, M.; Hategan, A. R.; Berghian-Grosan, C.; Magdas, D. A. 2022. The Development of Honey Recognition Models Based on the Association between atr-ir Spectroscopy and Advanced Statistical Tools, Int. J. Mol. Sci. 23 (17). https://www.mdpi.com/1422-0067/23/17/9977. 22 October 2022
  • Suciu, R. C.; Guyon, F.; Magdas, D. A. Application of Emission – Excitation Matrices in Parallel with Factor Analysis with Other Chemometric Techniques for Honey Classification, J. Food Compost. Anal. 107 (2022) 104401. https://www.sciencedirect.com/science/article/pii/S0889157522000199. 10 October 2022
  • Antônio, D. C.; de Assis, D. C. S.; Botelho, B. G.; Sena, M. M. Detection of Adulterations in a Valuable Brazilian Honey by Using Spectrofluorime Try and Multiway Classification, Food Chem. 370 (2022) 131064. https://www.sciencedirect.com/science/article/pii/S0308814621020707. 10 October 2022.
  • Phillips, T.; Abdulla, W. 2022. A New Honey Adulteration Detection Approach Using Hyperspectral Imaging and Machine Learning, European Food Research and Technology. 20 November 2022. doi:10.1007/s00217-022-04113-9. URL. DOI: 10.1007/s00217-022-04113-9.
  • Boateng, A. A.; Sumaila, S.; Lartey, M.; Oppong, M. B.; Opuni, K. F.; Adutwum, L. A. Evaluation of Chemometric Classification and Regression Models for the Detection of Syrup Adulteration in Honey, LWT 163 (2022) 113498. https://www.sciencedirect.com/science/article/pii/S0023643822004339 . 22 october 2022
  • Rachineni, K.; Rao Kakita, V. M.; Awasthi, N. P.; Shirke, V. S.; Hosur, R. V.; Chandra Shukla, S. Identifying Type of Sugar Adulterants in Honey: Combined Application of Nmr Spectroscopy and Supervised Machine Learning Classification. Curr Res. Food Sci. 2022, 5, 272–277. https://www.sciencedirect.com/science/article/pii/S2665927122000089. 20 July 2022.
  • Tan, J.; Xu, J. Applications of Electronic Nose (e-nose) and Electronic Tongue (e-tongue) in Food quality-related Properties Determination: A Review. Artific. Intellig. Agric. 2020, 4, 104–115. DOI: 10.1016/j.aiia.2020.06.003.
  • Wei, Z.; Wang, J. Tracing Floral and Geographical Origins of Honeys by Potentiometric and Voltammetric Electronic Tongue. Comput. Electron. Agric. 2014, 108, 112–122. DOI: 10.1016/j.compag.2014.07.014.
  • Elgrishi, N.; Rountree, K. J.; McCarthy, B. D.; Rountree, E. S.; Eisenhart, T. T.; Dempsey, J. L. A Practical Beginner’s Guide to Cyclic Voltammetry. J. Chem. Educ. 2018, 95(2), 197–206. DOI: 10.1021/acs.jchemed.7b00361.
  • Gutiérrez, J. M.; Haddi, Z.; Amari, A.; Bouchikhi, B.; Mimendia, A.; Cetó, X.; Del Valle, M. Hybrid Electronic Tongue Based on Multisensor Data Fusion for Discrimination of Beers. Sens. Actuators B Chem. 2013, 177, 989–996. DOI: 10.1016/j.snb.2012.11.110.
  • Otles, S. Handbook of Food Analysis Instruments; Boca Raton, FL: CRC Press, 2016.
  • El Hassani, N. E. A.; Tahri, K.; Llobet, E.; Bouchikhi, B.; Errachid, A.; Zine, N.; El Bari, N. Emerging Approach for Analytical Characterization and Geographical Classification of Moroccan and French Honeys by Means of a Voltammetric Electronic Tongue. Food Chem. 2018, 243, 36–42. DOI: 10.1016/j.foodchem.2017.09.067.
  • Pozo, F.; Vidal, Y.; Salgado, Ó. Wind Turbine Condition Monitoring Strategy through Multiway Pca and Multivariate Inference. Energies. 2018, 11(4), 749. DOI: 10.3390/en11040749.
  • Gracia, A.; González, S.; Robles, V.; Menasalvas, E. A Methodology to Compare Dimensionality Reduction Algorithms in Terms of Loss of Quality. Inform. Sci. 2014, 270, 1–27. DOI: 10.1016/j.ins.2014.02.068.
  • Van der Maaten, L.; Hinton, G. Visualizing Data Using t-sne. J. Mach. Learn. Res. 2008, 9(11), 2579–2605.
  • Belkin, M.; Niyogi, P. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Computat. 2003, 15(6), 1373–1396. DOI: 10.1162/089976603321780317.
  • Tenenbaum, J. B.; De Silva, V.; Langford, J. C. A Global Geometric framework for Nonlinear Dimensionality Reduction. Science (New York, N.Y.). 2000, 290(5500), 2319–2323. DOI: 10.1126/science.290.5500.2319.
  • Roweis, S. T.; Saul, L. K. Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science. Science (New York, N.Y.). 2000, 290(5500), 2323–2326. DOI: 10.1126/science.290.5500.2323.
  • Leon-Medina, J. X. 16 August. Desarrollo de un sistema de clasificación de sustancias basado en un arreglo de sensores tipo lengua electrónica, Ph.D. thesis, Universidad Nacional de Colombia, https://repositorio.unal.edu.co/handle/unal/79962 (2021). 20 July 2022.
  • Vitola, J.; Pozo, F.; Tibaduiza, D.; Anaya, M. A Sensor Data Fusion System Based on k-nearest Neighbor Pattern Classification for Structural Health Monitoring Applications. Sensors. 2017, 17(2), 417. DOI: 10.3390/s17020417.