Temperature Modulation of a Tin Oxide-Based Gas Sensor for Detecting Vinegar Purity using the K-Nearest Neighbors Algorithm
Subject Areas : Electronic EngineeringVahid Khorramshahi 1 , Fatemeh Safari 2 , Ali Fatehifar 3
1 - Materials and Energy Research Center, Dezful Branch, Islamic Azad University, Dezful, Iran
2 - Materials and Energy Research Center, Dezful Branch, Islamic Azad University, Dezful, Iran
3 - 1Materials and Energy Research Center, Dezful Branch, Islamic Azad University, Dezful, Iran
Keywords: Tin oxide, temperature modulation, electronic nose, gas sensor, classification.,
Abstract :
Temperature modulation in gas sensors based on metal oxides, such as SnO₂, alters the operating temperature of the sensor. These changes enhance the sensor's sensitivity and selectivity in detecting acetic acid in vinegar, enabling the determination of vinegar purity. In temperature modulation for gas sensors, temperature cycles are employed to record the sensor's response to different compounds. These responses are then processed as classification features to differentiate and identify various compounds. In this study, the nearest neighbor (k-NN) algorithm was used to classify vinegar samples and assess their purity based on these features. A square voltage waveform was applied to the microheater of the MQ2 gas sensor, inducing temperature variations that generated distinct patterns in the sensor's response. The sensor's voltage response to vinegar vapors with varying purities was recorded in three stages. From each pattern, seven unique features were extracted to serve as inputs for the classification algorithm. The proposed algorithm demonstrated an accuracy of 86% in determining vinegar purity, making it suitable for use in automated detection systems.
[1] R. A. S. Lapa, J. F. C. Lima, R. Pérez-Olmos, and M. P. Ruiz, “Simultaneous automatic potentiometric determination of acidity, chloride and fluoride in vinegar,” Food Control, vol. 6, no. 3, pp. 155–159, 1995.
[2] M. Guerrero, “Multivariate characterization of wine vinegars from the south of Spain according to their metallic content,” Talanta, vol. 45, no. 2, pp. 379–386, Dec. 1997, doi: 10.1016/S0039-9140(97)00139-2.
[3] R. Castro Mejías, R. Natera Marín, M. De Valme García Moreno, and C. García Barroso, “Optimisation of headspace solid-phase microextraction for analysis of aromatic compounds in vinegar,” J. Chromatogr. A, vol. 953, no. 1–2, pp. 7–15, Apr. 2002, doi: 10.1016/S0021-9673(02)00122-X.
[4] E. Anklam, M. Lipp, B. Radovic, E. Chiavaro, and G. Palla, “Characterisation of Italian vinegar by pyrolysis–mass spectrometry and a sensor device (‘electronic nose’),” Food chemistry, vol. 61, no. 1–2, pp. 243–8, Jan. 1998.
[5] J. Tan and J. Xu, “Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: A review,” Artif. Intell. Agric., vol. 4, pp. 104–115, Jan. 2020, doi: 10.1016/j.aiia.2020.06.003.
[6] M. Wang and Y. Chen, “Electronic nose and its application in the food industry: a review,” Eur. Food Res. Technol., vol. 250, no. 1, pp. 21–67, Jan. 2024, doi: 10.1007/s00217-023-04381-z.
[7] R.-C. Liu, R. Li, Y. Wang, and Z.-T. Jiang, “Analysis of volatile odor compounds and aroma properties of European vinegar by the ultra-fast gas chromatographic electronic nose,” J. Food Compos. Anal., vol. 112, p. 104673, Sep. 2022, doi: 10.1016/j.jfca.2022.104673.
[8] E. Martín-Tornero, J. D. Barea-Ramos, J. Lozano, I. Durán-Merás, and D. Martín-Vertedor, “E-Nose Quality Evaluation of Extra Virgin Olive Oil Stored in Different Containers,” Chemosensors, vol. 11, no. 2, 2023, doi: 10.3390/chemosensors11020085.
[9] E. Zhang et al., “Application of an electronic nose for the diagnosis of ketosis in dairy cows,” Food Biosci., vol. 60, p. 104355, Aug. 2024, doi: 10.1016/j.fbio.2024.104355.
10] E. Aghdamifar, V. R. Sharabiani, E. Taghinezhad, M. Szymanek, and A. Dziwulska-Hunek, “E-nose as a non-destructive and fast method for identification and classification of coffee beans based on soft computing models,” Sensors Actuators B Chem., vol. 393, p. 134229, 2023, doi: https://doi.org/10.1016/j.snb.2023.134229.
[11] P. Jia, X. Li, M. Xu, and L. Zhang, “Classification techniques of electronic nose: a review,” Int. J. Bio-Inspired Comput., vol. 23, no. 1, pp. 16–27, 2024.
[12] A. T. John, K. Murugappan, D. R. Nisbet, and A. Tricoli, “An Outlook of Recent Advances in Chemiresistive Sensor-Based Electronic Nose Systems for Food Quality and Environmental Monitoring,” Sensors, vol. 21, no. 7, 2021, doi: 10.3390/s21072271.
[13] A. Sierra-Padilla, J. J. García-Guzmán, D. López-Iglesias, J. M. Palacios-Santander, and L. Cubillana-Aguilera, “E-Tongues/Noses Based on Conducting Polymers and Composite Materials: Expanding the Possibilities in Complex Analytical Sensing,” Sensors, vol. 21, no. 15, 2021, doi: 10.3390/s21154976.
[14] H. L. Gan, Y. B. C. Man, C. P. Tan, I. NorAini, and S. A. H. Nazimah, “Characterisation of vegetable oils by surface acoustic wave sensing electronic nose,” Food Chem., vol. 89, no. 4, pp. 507–518, 2005, doi: https://doi.org/10.1016/j.foodchem.2004.03.005.
[15] S. Ampuero and J. O. Bosset, “The electronic nose applied to dairy products: a review,” Sensors Actuators B Chem., vol. 94, no. 1, pp. 1–12, 2003, doi: https://doi.org/10.1016/S0925-4005(03)00321-6.
[16] M. Tonezzer et al., “Electronic noses based on metal oxide nanowires: A review,” Nanotechnol. Rev., vol. 11, no. 1, pp. 897–925, 2022.
[17] W. S. Al-Dayyeni et al., “A review on electronic nose: coherent taxonomy, classification, motivations, challenges, recommendations and datasets,” IEEE Access, vol. 9, pp. 88535–88551, 2021.
[18] B. Bhangare, K. R. Sinju, N. S. Ramgir, S. Gosavi, and A. K. Debnath, “Noble metal sensitized SnO2/RGO nanohybrids as chemiresistive E-nose for H2, H2S and NO2 detection,” Mater. Sci. Semicond. Process., vol. 147, p. 106706, 2022.
[19] Z. Khatoon, H. Fouad, O. Y. Alothman, M. Hashem, Z. A. Ansari, and S. A. Ansari, “Doped SnO2 nanomaterials for e-nose based electrochemical sensing of biomarkers of lung cancer,” ACS omega, vol. 5, no. 42, pp. 27645–27654, 2020.
[20] Z. Li et al., “E-nose based on a high-integrated and low-power metal oxide gas sensor array,” Sensors Actuators B Chem., vol. 380, p. 133289, 2023.
[21] B. Mahata, S. Acharyya, P. Banerji, and P. K. Guha, “Assessment of fish adulteration using SnO2 nanopetal-based gas sensor and machine learning,” Food Chem., vol. 438, p. 138039, 2024.
[22] A. Taurino, S. Capone, C. Distante, M. Epifani, R. Rella, and P. Siciliano, “Recognition of olive oils by means of an integrated sol–gel SnO2 Electronic Nose,” Thin Solid Films, vol. 418, no. 1, pp. 59–65, 2002, doi: https://doi.org/10.1016/S0040-6090(02)00596-5.
[23] Z. Hu, X. Li, H. Wang, C. Niu, Y. Yuan, and T. Yue, “A novel method to quantify the activity of alcohol acetyltransferase Using a SnO2-based sensor of electronic nose,” Food Chem., vol. 203, pp. 498–504, 2016, doi: https://doi.org/10.1016/j.foodchem.2016.02.087.
[24] F. Bravo-Hualpa et al., “SnO2-TiO2 and SnO2-MoO3 Based Composite Gas Sensors to Develop an E-nose for Peruvian Pisco Varieties Differentiation,” J. Electrochem. Soc., vol. 169, no. 1, p. 17511, 2022.
[25] H. Yu, X. Tan, S. Sun, L. Zhang, C. Gao, and S. Ge, “Engineering paper-based visible light-responsive Sn-self doped domed SnO2 nanotubes for ultrasensitive photoelectrochemical sensor,” Biosens. Bioelectron., vol. 185, p. 113250, 2021, doi: https://doi.org/10.1016/j.bios.2021.113250.
[26]Z. Huang et al., “Tin Oxide (SnO2) Nanoparticles: Facile Fabrication, Characterization, and Application in UV Photodetectors,” Nanomaterials, vol. 12, no. 4, 2022, doi: 10.3390/nano12040632.
[27] C. Wang et al., “High-effective SnO2-based perovskite solar cells by multifunctional molecular additive engineering,” J. Alloys Compd., vol. 886, p. 161352, 2021, doi: https://doi.org/10.1016/j.jallcom.2021.161352.
[28] S. R. Morrison, “Mechanism of semiconductor gas sensor operation,” Sensors and Actuators, vol. 11, no. 3, pp. 283–287, Apr. 1987, doi: 10.1016/0250-6874(87)80007-0.
[29] S. M. Hosseini-Golgoo and F. Hossein-Babaei, “Assessing the diagnostic information in the response patterns of a temperature-modulated tin oxide gas sensor,” Meas. Sci. Technol., vol. 22, no. 3, p. 35201, 2011.
[30] S. M. Hosseini-Golgoo, F. Salimi, A. Saberkari, and S. Rahbarpour, “Comparison of information content of temporal response of chemoresistive gas sensor under three different temperature modulation regimes for gas detection of different feature reduction methods,” in Journal of Physics: Conference Series, 2017, vol. 939, no. 1, p. 12005.
[31] C. Krutzler, A. Unger, H. Marhold, T. Fricke, T. Conrad, and A. Schütze, “Influence of MOS gas-sensor production tolerances on pattern recognition techniques in electronic noses,” IEEE Trans. Instrum. Meas., vol. 61, no. 1, pp. 276–283, 2011.
[32] K. Yan and D. Zhang, “Improving the transfer ability of prediction models for electronic noses,” Sensors Actuators B Chem., vol. 220, pp. 115–124, 2015, doi: https://doi.org/10.1016/j.snb.2015.05.060.
[33] G.-Y. Miao, S.-S. Chen, Y.-J. Wang, Z. Guo, and X.-J. Huang, “SnO2 Nanostructures Exposed with Various Crystal Facets for Temperature-Modulated Sensing of Volatile Organic Compounds,” ACS Appl. Nano Mater., vol. 5, no. 8, pp. 10636–10644, 2022.
[34] A. Schütze and T. Sauerwald, “Dynamic operation of semiconductor sensors,” in Semiconductor Gas Sensors, Elsevier, 2020, pp. 385–412. doi: 10.1016/B978-0-08-102559-8.00012-4.
[35] T. Iwata, M. Saeki, Y. Okura, and T. Yoshikawa, “Gas discrimination based on enhanced gas-species related information obtained by a single gas sensor with novel temperature modulation,” Sensors Actuators B Chem., vol. 354, p. 131225, Mar. 2022, doi: 10.1016/j.snb.2021.131225.
[36] W. An and C. Y. Yang, “Review on Temperature Modulation Technology of Gas Sensors,” in Electrical Information and Mechatronics and Applications, 2012, vol. 143, pp. 567–571. doi: 10.4028/www.scientific.net/AMM.143-144.567.
[37] F. Rastrello, P. Placidi, and A. Scorzoni, “A System for the Dynamic Control and Thermal Characterization of Ultra Low Power Gas Sensors,” IEEE Trans. Instrum. Meas., vol. 60, no. 5, pp. 1876–1883, 2011, doi: 10.1109/TIM.2010.2089130.
[38] E. Brauns, E. Morsbach, S. Kunz, M. Baeumer, and W. Lang, “Temperature modulation of a catalytic gas sensor,” Sensors, vol. 14, no. 11, pp. 20372–20381, 2014.
[39] A. Far, B. Guo, F. Flitti, and A. Bermak, “Temperature modulation for tin-oxide gas sensors,” in 4th IEEE International Symposium on Electronic Design, Test and Applications (delta 2008), 2008, pp. 378–381.
[40] M. Leidinger, T. Sauerwald, T. Conrad, W. Reimringer, G. Ventura, and A. Schütze, “Selective detection of hazardous indoor VOCs using metal oxide gas sensors,” Procedia Eng., vol. 87, pp. 1449–1452, 2014.
[41] V. Khorramshahi, J. Karamdel, and R. Yousefi, “Acetic acid sensing of Mg-doped ZnO thin films fabricated by the sol–gel method,” J. Mater. Sci. Mater. Electron., vol. 29, no. 17, pp. 14679–14688, 2018, doi: 10.1007/s10854-018-9604-0.
[42] V. Khorramshahi, J. Karamdel, and R. Yousefi, “High acetic acid sensing performance of Mg-doped ZnO/rGO nanocomposites,” Ceram. Int., vol. 45, no. 6, pp. 7034–7043, Apr. 2019, doi: 10.1016/j.ceramint.2018.12.205.
[43] A. Boujnah, A. Boubaker, S. Pecqueur, K. Lmimouni, and A. Kalboussi, “An electronic nose using conductometric gas sensors based on P3HT doped with triflates for gas detection using computational techniques (PCA, LDA, and kNN),” J. Mater. Sci. Mater. Electron., vol. 33, no. 36, pp. 27132–27146, 2022.
[44] M. Abbatangelo, E. Núñez-Carmona, V. Sberveglieri, E. Comini, and G. Sberveglieri, “k-NN and k-NN-ANN combined classifier to assess mox gas sensors performances affected by drift caused by early life aging,” Chemosensors, vol. 8, no. 1, p. 6, 2020.
[45] M. Ismail and S. A. D. Prasetyowati, “Classification Of Alcohol Type Using Gas Sensor And K-Nearest Neighbor,” J. Nas. Tek. Elektro, pp. 59–64, 2022.
[46] W. Xia, T. Song, Z. Yan, K. Song, D. Chen, and Y. Chen, “A Method for Recognition of Mixed Gas Composition Based on PCA and KNN,” in 2021 19th International Conference on Optical Communications and Networks (ICOCN), 2021, pp. 1–3.