The Use of Microwave and K* Algorithm in Determination of Alcohol Concentration in Liquids
Source abstract
Abstract
Abstract Alcohol is widely used in various fields today. Alcohol overdose and its improper use can cause many health problems. Drinking liquids with high alcohol concentration, especially methanol, causes poisoning, and the use of colognes with high alcohol concentration causes various skin conditions and respiratory diseases. Therefore, for public health it is important to determine the type and concentration of alcohol in beverages and liquid products. In this paper, K* algorithm is used to detect and classify liquids containing high concentration of alcohol by measuring the scattering parameter of the liquids in the microwave frequency band. For this purpose, scatter parameter values of aqueous solutions of ethanol, methanol, 1-propanol and 2-propanol at different concentrations have been used as training sets. Commercial liquids with known ethanol concentrations and liquids with other known alcohol concentrations have been tested to prove the accuracy of the proposed approach. As the results show the proposed approach can classify liquids containing alcohol and their alcohol concentrations successfully and with high accuracy.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Chemical Sensor Technologies · Spectroscopy and Chemometric Analyses · Spectroscopy Techniques in Biomedical and Chemical Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (27)
Show source-provided references
1. Valavanidis, A., Fiotakis, K., and Vlachogianni, T., Airborne particulate matter and human health: Toxicological assessment and importance of size and composition of particles for oxidative damage and carcinogenic mechanisms, J. Environ. Sci. Health,Part C, 2008, vol. 26, no. 4, pp. 339–362.
2. Pires, D., de Kraker, M.E.A., Tartari, E., Abbas, M., and Pittet, D., ‘Fight antibiotic resistance—It’s in your hands’: Call from the World Health Organization for 5th May 2017, Clin. Infect. Dis., 2017, vol. 64, no. 12, pp. 1780–1783.10.1093/cid/cix226 ↗
3. Slaughter, R., Mason, R., Beasley, D., Vale, J., and Schep, L., Isopropanol poisoning, Clin. Toxicol., 2014, vol. 52, no. 5, pp. 470–478.10.3109/15563650.2014.914527 ↗
4. Chan, A.P. and Chan, T.Y., Methanol as an unlisted ingredient in supposedly alcohol-based hand rub can pose serious health risk, Int. J. Environ. Res. Public Health, 2018, vol. 15, no. 7, p. 1440.10.3390/ijerph15071440 ↗
5. Lachenmeier, D.W., Rehm, J., and Gmel, G., Surrogate alcohol: What do we know and where do we go? Alcohol.: Clin. Exp. Res., 2007, vol. 31, no. 10, pp. 1613–1624.10.1111/j.1530-0277.2007.00474.x ↗
6. Rehm, J., Kailasapillai, S., Larsen, E., Rehm, M.X., Samokhvalov, A.V., Shield, K.D., Roerecke, M., and Lachenmeier, D.W., A systematic review of the epidemiology of unrecorded alcohol consumption and the chemical composition of unrecorded alcohol, Addiction, 2014, vol. 109, no. 6, pp. 880–893.10.1111/add.12498 ↗
7. Destanoğlu, O. and İsmail, A., Determination and evaluation of methanol, ethanol and higher alcohols in legally and illegally produced alcoholic beverages, J. Turk. Chem. Soc., Sec. A: Chem., 2019, vol. 6, no. 1, pp. 21–28.
8. Organization, W. H., Global Status Report on Alcohol and Health 2018, W.H.O., 2019.
9. Zamani, N., Rafizadeh, A., Hassanian-Moghaddam, H., Akhavan-Tavakoli, A., Ghorbani-Samin, M., Akhgari, M., and Shariati, S., Evaluation of methanol content of illegal beverages using GC and an easier modified Chromotropic acid method; a cross sectional study, Subst. Abuse. Treat. Prev. Policy, 2019, vol. 14, no. 1, p. 56.10.1186/s13011-019-0244-z ↗
10. Chowdhary, P., Raj, A., and Bharagava, R.N., Environmental pollution and health hazards from distillery wastewater and treatment approaches to combat the environmental threats: A review, Chemosphere, 2018, vol. 194, pp. 229–246.10.1016/j.chemosphere.2017.11.163 ↗
11. Network, D.R. and Bishop, R.E., Chemical and Biological Hazards Posed by Drilling Exploratory Shale Gas Wells in Pennsylvania’s Delaware River Basin Report for the Delaware River Basin Commission Exploratory Well Hearing, 2010.
12. Moermond, C.T., Kase, R., Korkaric, M., and Ågerstrand, M., CRED: Criteria for reporting and evaluating ecotoxicity data, Environ. Toxicol. Chem., 2016, vol. 35, no. 5, pp. 1297–1309.10.1002/etc.3259 ↗
13. Kharkovsky, S. and Zoughi, R., Microwave and millimeter wave nondestructive testing and evaluation—Overview and recent advances, IEEE Instrum. Meas. Mag., 2007, vol. 10, no. 2, pp. 26–38.10.1109/MIM.2007.364985 ↗
14. Li, Z., Haigh, A., Soutis, C., Gibson, A., and Sloan, R., Microwaves sensor for wind turbine blade inspection, Appl. Compos. Mater., 2017, vol. 24, no. 2, pp. 495–512.10.1007/s10443-016-9545-9 ↗
15. Tekbas, M., Toktas, A., and Ustun, D., A formulaic model calculating the permittivity of testing materials placed on a circular patch antenna, in 2019 XXIVth Int. Semin./Workshop Direct Inverse Probl. Electromagn. Acoust. Wave Theory (DIPED),2019, IEEE, 2019, pp. 88–92.10.1109/DIPED.2019.8882582 ↗
16. Jose, K., Varadan, V., and Varadan, V., Wideband and noncontact characterization of the complex permittivity of liquids, Microwave Opt. Technol. Lett., 2001, vol. 30, no. 2, pp. 75–79.10.1002/mop.1225 ↗
17. Venkatesh, M. and Raghavan, G., An overview of dielectric properties measuring techniques, Can. Biosyst. Eng., 2005, vol. 47, no. 7, pp. 15–30.
18. Li, Z., Haigh, A., Soutis, C., Gibson, A., and Sloan, R., Evaluation of water content in honey using microwave transmission line technique, J. Food Eng., 2017, vol. 215, pp. 113–125.10.1016/j.jfoodeng.2017.07.009 ↗
19. Gusiatin, Z.M., Kulikowska, D., Klik, B.K., and Hajdukiewicz, K., Ecological risk assessment of sewage sludge from municipal wastewater treatment plants: A case study, J. Environ. Sci. Health,Part A, 2018, vol. 53, no. 13, pp. 1167–1176.
20. Ewa, I., Oladipo, O., and Dim, L., Cluster analysis of elemental concentrations of cored Nigerian river sediments, J. Environ. Sci. & Health,Part A, 1992, vol. 27, no. 1, pp. 1–11.
21. Mirmohseni, A., Shojaei, M., Feizi, M.A.H., Azhar, F.F., and Rastgouye-Houjaghan, M., Application of quartz crystal nanobalance and principal component analysis for detection and determination of nickel in solution, J. Environ. Sci. & Health,Part A, 2010, vol. 45, no. 9, pp. 1119–1125.
22. Mirmohseni, A., Razzaghi, M., Pourata, R., Rastgouye-Hojagan, M., and Zavareh, S., Selective determination of ethyl acetate, acetone, ethanol, and methyl ethyl ketone using quartz crystal nanobalance combined with principle component analysis, J. Environ. Sci. & Health,Part A, 2009, vol. 44, no. 9, pp. 847–853.
23. Vatandas, M., Koc, A.B., and Koc, C., Ultrasonic velocity measurements in ethanol–water and methanol–water mixtures, Eur. Food Res. Technol., 2007, vol. 225, nos. 3–4, pp. 525–532.10.1007/s00217-006-0448-0 ↗
24. Jepsen, P.U., Jensen, J.K., and Møller, U., Characterization of aqueous alcohol solutions in bottles with THz reflection spectroscopy, Opt. Express, 2008, vol. 16, no. 13, pp. 9318–9331.10.1364/OE.16.009318 ↗
25. Cleary, J.G. and Trigg, L.E., K*: An instance-based learner using an entropic distance measure, in Machine Learning Proceedings 1995, Elsevier, 1995, pp. 108–114.
26. Shannon, C. E., A mathematical theory of communication, Bell Syst. Tech. J., 1948, vol. 27, no. 3, pp. 379–423.10.1002/j.1538-7305.1948.tb01338.x ↗
27. Piramuthu, S. and Sikora, R.T., Iterative feature construction for improving inductive learning algorithms, Expe-rt Syst. Appl., 2009, vol. 36, no. 2, pp. 3401–3406.10.1016/j.eswa.2008.02.010 ↗