Abstract
An automated system for measuring the content of aromatic aldehydes in alcohol solutions has been developed. The main advantages of neural networks are compared with other mathematical methods, such as noise sustainability and the possibility of distributed data processing, the ability to process spectral dependencies in a wide range of measurements. An artificial neural network was created to process the output signals of the sensors, taking into account mutual cross-sensitivity and selective sensors to reduce the error of determining the concentration of volatile compounds. It has been shown that simple sensors can be integrated into an automated quality monitoring system for model vanillin mixtures. Simulation models were developed using sensors based on the electronic theory of sorption on the surface of semiconductors. The measuring complex can be adjusted to different measurement algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Waterhouse, A.L., Ebeler, S.E. (eds.) Chemistry of wine flavor. American Chemical Society; Distributed by Oxford University Press, Washington, DC (1998)
Jackson, R.S. (ed.) Speciality wines. Elsevier, Acad. Press, Amsterdam (2011)
Jackson, R.S.: Wine science principles and applications. Elsevier Acad. Press, Amsterdam (2008)
Restani, P., Uberti, F., Tarantino, C., Ballabio, C., Gombac, F., Bastiani, E., Bolognini, L., Pavanello, F., Danzi, R.: Validation by a collaborative interlaboratory study of an ELISA method for the detection of caseinate used as a fining agent in wine. Food Anal. Meth. 5, 480–486 (2012). https://doi.org/10.1007/s12161-011-9270-9
Marsili, R. (ed.) Flavor, Fragrance, and Odor Analysis, CRC Press, Cambridge (2001). https://doi.org/10.1201/9780203908273
Jackson, R.S.: Wine tasting: A Professional Handbook. Elsevier/Academic Press is an imprint of Elservier, Amsterdam (2017)
Uthurry, C.A., Lepe, J.A.S., Lombardero, J., García Del Hierro, J.R.: Ethyl carbamate production by selected yeasts and lactic acid bacteria in red wine. Food Chem. 94, 262–270 (2006). https://doi.org/10.1016/j.foodchem.2004.11.017
Mira de Orduña, R., Liu, S.-Q., Patchett, M.L., Pilone, G.J.: Ethyl carbamate precursor citrulline formation from arginine degradation by malolactic wine lactic acid bacteria. FEMS Microbiol. Lett. 183, 31–35 (2000). https://doi.org/10.1111/j.1574-6968.2000.tb08929.x
Karadjova, I., Izgi, B., Gucer, S.: Fractionation and speciation of Cu, Zn and Fe in wine samples by atomic absorption spectrometry. Spectrochim. Acta Part B 57, 581–590 (2002). https://doi.org/10.1016/S0584-8547(01)00386-X
Ajtony, Z., Szoboszlai, N., Suskó, E.K., Mezei, P., György, K., Bencs, L.: Direct sample introduction of wines in graphite furnace atomic absorption spectrometry for the simultaneous determination of arsenic, cadmium, copper and lead content. Talanta 76, 627–634 (2008). https://doi.org/10.1016/j.talanta.2008.04.014
Pan, X.-D., Tang, J., Chen, Q., Wu, P.-G., Han, J.-L.: Evaluation of direct sampling method for trace elements analysis in Chinese rice wine by ICP–OES. Euro. Food Res. Technol. 236, 531–535 (2013). https://doi.org/10.1007/s00217-012-1888-3
Jiao, Z., Dong, Y., Chen, Q.: Ethyl carbamate in fermented beverages: presence, analytical chemistry, formation mechanism, and mitigation proposals: ethyl carbamate in fermented beverages…. Compr. Rev. Food Sci. Food Saf. 13, 611–626 (2014). https://doi.org/10.1111/1541-4337.12084
Villamor, R.R., Evans, M.A., Mattinson, D.S., Ross, C.F.: Effects of ethanol, tannin and fructose on the headspace concentration and potential sensory significance of odorants in a model wine. Food Res. Int. 50, 38–45 (2013). https://doi.org/10.1016/j.foodres.2012.09.037
Muñoz-González, C., Rodríguez-Bencomo, J.J., Moreno-Arribas, M.V., Pozo-Bayón, M.Á.: Beyond the characterization of wine aroma compounds: looking for analytical approaches in trying to understand aroma perception during wine consumption. Anal. Bioanal. Chem. 401, 1497–1512 (2011). https://doi.org/10.1007/s00216-011-5078-0
Franc, C., David, F., de Revel, G.: Multi-residue off-flavour profiling in wine using stir bar sorptive extraction–thermal desorption–gas chromatography–mass spectrometry. J. Chromatogr. A 1216, 3318–3327 (2009). https://doi.org/10.1016/j.chroma.2009.01.103
Ragazzosanchez, J., Chalier, P., Chevalier, D., Calderonsantoyo, M., Ghommidh, C.: Identification of different alcoholic beverages by electronic nose coupled to GC. Sens. Actuators B: Chemical 134, 43–48 (2008). https://doi.org/10.1016/j.snb.2008.04.006
Gómez-Alonso, S., García-Romero, E., Hermosín-Gutiérrez, I.: HPLC analysis of diverse grape and wine phenolics using direct injection and multidetection by DAD and fluorescence. J. Food Compos. Anal. 20, 618–626 (2007). https://doi.org/10.1016/j.jfca.2007.03.002
Macías, M., Manso, A., Orellana, C., Velasco, H., Caballero, R., Chamizo, J.: Acetic acid detection threshold in synthetic wine samples of a portable electronic nose. Sensors 13, 208–220 (2012). https://doi.org/10.3390/s130100208
Lvova, L., Kirsanov, D.: Multisensor systems for analysis of liquids and gases: trends and developments. Front. Chem. 6, 591 (2018). https://doi.org/10.3389/fchem.2018.00591
Oliinyk, B.V., Isaieva, K., Manilov, A.I., Nychyporuk, T., Geloen, A., Joffre, F., Skryshevsky, V.A., Litvinenko, S.V., Lysenko, V.: Silicon-based optoelectronic tongue for label-free and nonspecific recognition of vegetable oils. ACS Omega 5, 5638–5642 (2020). https://doi.org/10.1021/acsomega.9b03196
Rumelhart, D.E., McClelland, J.L.: University of California, S.D., PDP Research Group: Parallel distributed processing. explorations in the microstructure of cognition (1987)
Chow, T.W.S., Cho, S.-Y.: Neural networks and computing: learning algorithms and applications. Imperial College Press (2007). https://doi.org/10.1142/p487
Bouyoucef, E., Chebira, A., Rybnik, M, Madani, K.: Multiple neural network model generator with complexity estimation and self-organization abilities. Int. Sci. J. Comput. 4(3), 20–29. ISSN 1727-6209 (2005)
Haykin, S.S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River (1999)
Ghafar-Zadeh, E., Sawan, M.: CMOS Capacitive Sensors for Lab-on-Chip Applications: A Multidisciplinary Approach. Springer, Dordrecht (2010)
Guadarrama, A., Fernández, J.A., Íñiguez, M., Souto, J., de Saja, J.A.: Array of conducting polymer sensors for the characterisation of wines. Anal. Chim. Acta 411, 193–200 (2000). https://doi.org/10.1016/S0003-2670(00)00769-8
Litvinenko, S.V., Kozinetz, A.V., Skryshevsky, V.A.: Concept of photovoltaic transducer on a base of modified p–n junction solar cell. Sens. Actuators A 224, 30–35 (2015). https://doi.org/10.1016/j.sna.2015.01.014
Gupta, Y.: Selection of important features and predicting wine quality using machine learning techniques. Procedia Comput. Sci. 125, 305–312 (2018). https://doi.org/10.1016/j.procs.2017.12.041
Tmienova, N., Sus, B.: Hardware data encryption complex based on programmable microcontrollers. In: CEUR Workshop Proceedings, pp. 199–208 (2018). http://www.ceur-ws.org/Vol-2318/paper17.pdf
Bauzha, O., Sus, B., Zagorodnyuk, S., Stuchynska, N.: Electrocardiogram measurement complex based on microcontrollers and wireless networks. In: International Scientific-Practical Conference on Problems of Infocommunications Science and Technology, PIC S and T, pp. 345–349 (2019)
Sus, B., Tmienova, N., Revenchuk, I., Vialkova, V.: Development of virtual laboratory works for technical and computer sciences. In: Damaševičius, R., Vasiljevienė, G. (eds.) Information and Software Technologies, pp. 383–394. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30275-7_29
Chaikivskyi, T., Bauzha, O., Sus, B. B., Tmienova, N., Zagorodnyuk, S.: 3D simulation of virtual laboratory on electron microscopy. In: CEUR Workshop Proceedings 2533, pp. 282–291 (2019). http://ceur-ws.org/Vol-2533/paper26.pdf
Chaikivskyi, T., Sus, B., Hunkalo, A.: Microcontroller-based multi-channel sensor system for monitoring the quality of spirit beverages. In: 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), pp. 59–63 (2020) https://doi.org/10.1109/TCSET49122.2020.235391
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chaikivskyi, T., Sus, B., Bauzha, O., Zagorodnyuk, S. (2021). Intelligent Neural Network Sensory System for the Analysis of Volatile Compounds in Beverages. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing V. CSIT 2020. Advances in Intelligent Systems and Computing, vol 1293. Springer, Cham. https://doi.org/10.1007/978-3-030-63270-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-63270-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63269-4
Online ISBN: 978-3-030-63270-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)