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یک زیست‌حسگر الکتروشیمیایی هوشمند مبتنی بر روش‌های یادگیری ماشین بهینه برای اندازه‌گیری آلودگی نیترات در آب | ||
| تحقیقات سامانه‌ها و مکانیزاسیون کشاورزی | ||
| دوره 24، شماره 88، دی 1402، صفحه 91-106 اصل مقاله (1.86 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/amsr.2024.367532.1502 | ||
| نویسنده | ||
| کیوان آصف پور وکیلیان* | ||
| استادیار، گروه مهندسی مکانیک بیوسیستم، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران | ||
| چکیده | ||
| در دو دهۀ گذشته، زیست‌حسگرهای آنزیمی فراوانی برای تشخیص اختصاصی و انتخابی نیترات معرفی شده‌اند. این زیست‌حسگرها عموماً از واکنش اکسایش-کاهش نیترات به نیتریت برای اندازه‌گیری نیترات بهره می‌گیرند. از آنجا که فعالیت آنزیم مورد استفاده در ساختار زیست‌حسگر با گذشت زمان کاهش می‌یابد، کاربر زیست‌حسگر باید آنزیم تثبیت‌شده روی الکترود کار را به طور مکرر جایگزین کند، که هزینه‌های تشخیص را افزایش میدهد و قابلیت تجاری‌سازی آنها را محدود می‌کند. در این مطالعه، از شبکه‌های عصبی مصنوعی برای پیش‌بینی غلظت نیترات در نمونه‌ها با در نظر گرفتن داده‌های الکتروشیمیایی و کاهش فعالیت آنزیم در طول زمان استفاده شد. الگوریتم شاهین هریس به عنوان روش بهینه‌سازی فراابتکاری برای بهینه‌سازی پارامترهای وزن و بایاس شبکه‌های عصبی مصنوعی به کاررفته در واحد تصمیم‌گیری زیست‌حسگر استفاده شد. نتایج بررسی ها نشان داد که الگوریتم یادگیری بهینه‌شده منجر به پیش‌بینی امیدوارکنندۀ غلظت نیترات در سطح میکرومولار با ضریب تبیین 95/0 شد. علاوه بر این، زیست‌حسگر معرفی شده توانایی استفاده تا 30 روز پس از تثبیت آنزیم را دارد. مقایسۀ میان یافته‌های این مطالعه و مطالعات قبلی که از ماشین‌های بردار پشتیبان و سیستم‌های استنتاج فازی استفاده می‌کردند، نشان داد که شبکه‌های بهینه‌سازی شده با تکنیک‌های جدید فراابتکاری می‌توانند نتایج پیش‌بینی قابل‌اعتمادی‌تری ارائه دهند. | ||
| کلیدواژه‌ها | ||
| آنزیم؛ الگوریتم شاهین هریس؛ داده‌های الکتروشیمیایی؛ هوش جمعیتی | ||
| عنوان مقاله [English] | ||
| An Intelligent Electrochemical Biosensor based on Optimized Machine Learning Methods for Measuring Nitrate Pollution in Water | ||
| نویسندگان [English] | ||
| Keyvan Asefpour Vakilian | ||
| Assistant Professor, Department of Biosystems Engineering,, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. | ||
| چکیده [English] | ||
| During the last two decades, various types of enzymatic biosensors have been introduced for the specific and selective detection of nitrate. These biosensors generally use the redox reaction of nitrate-nitrite to measure nitrate. Since the activity of the enzyme used in the biosensor structure decreases over time, the user of the biosensor should frequently replace the enzyme immobilized onto the working electrode, which increases the detection costs and limits their commercialization. In this study, artificial neural networks (ANNs) have been used to predict nitrate concentration in samples by considering electrochemical data and the decrease in enzyme activity over time. The Harris hawks algorithm was used as a meta-heuristic optimization method to optimize weight and bias hyperparameters of ANNs used in the biosensor decision-making unit. The results showed that the optimized learning algorithm led to a promising prediction of nitrate concentration at the micromolar level with a coefficient of determination of 0.95. In addition, the introduced biosensor could be used up to 30 days after enzyme immobilization. A comparison between the findings of this study and previous studies, that used support vector machines and fuzzy inference systems, showed that ANNs optimized with novel meta-heuristic techniques can provide more reliable prediction results. | ||
| کلیدواژه‌ها [English] | ||
| Collective intelligence, electrochemical data, enzyme, Harris hawks algorithm | ||
| مراجع | ||
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Ahmad, R., Bhat, K. S., Ahn, M. S., & Hahn, Y. B. (2017). Fabrication of a robust and highly sensitive nitrate biosensor based on directly grown zinc oxide nanorods on a silver electrode. New Journal of Chemistry, 41, 10992-10997. https://doi.org/10.1039/C7NJ02526B. Al Mamun, M., Wahab, Y. A., Hossain, M. M., Hashem, A., & Johan, M. R. (2021). Electrochemical biosensors with aptamer recognition layer for the diagnosis of pathogenic bacteria: Barriers to commercialization and remediation. TrAC Trends in Analytical Chemistry, 145, 116458. https://doi.org/10.1016/j.trac.2021.116458. Alahi, M. E. E., & Mukhopadhyay, S. C. (2018). Detection methods of nitrate in water: A review. Sensors and Actuators A: Physical, 280, 210-221. https://doi.org/10.1016/j.sna.2018.07.026. Asefpour Vakilian, K. (2020a). Determination of nitrogen deficiency-related microRNAs in plants using fluorescence quenching of graphene oxide nanosheets. Molecular and Cellular Probes, 52, 101576. https://doi.org/10.1016/j.mcp.2020.101576. Asefpour Vakilian, K. (2020b). Machine learning improves our knowledge about miRNA functions towards plant abiotic stresses. Scientific Reports, 10, 3041. https://doi.org/10.1038/s41598-020-59981-6. Asefpour Vakilian, K. (2022). A nitrate enzymatic biosensor based on optimized machine learning techniques. Proceedings of the 9th Iranian Joint Congress on Fuzzy and Intelligent Systems. Apr. 2-4, Bam, Iran. https://doi.org/10.1109/CFIS54774.2022.9756481. (in Persian) Asefpour Vakilian, K., & Massah, J. (2018a). A portable nitrate biosensing device using electrochemistry and spectroscopy. IEEE Sensors Journal, 18, 3080-3089. https://doi.org/10.1109/JSEN.2018.2809493. Asefpour Vakilian, K., & Massah, J. (2018b). A fuzzy-based decision making software for enzymatic electrochemical nitrate biosensors. Chemometrics and Intelligent Laboratory Systems, 177, 55-63. https://doi.org/10.1016/j.chemolab.2018.04.016. Bendikov, T. A., Kim, J., & Harmon, T. C. (2005). Development and environmental application of a nitrate selective microsensor based on doped polypyrrole films. Sensors and Actuators B: Chemical, 106, 512-517. https://doi.org/10.1016/j.snb.2004.07.018. Bhaiyya, M., Panigrahi, D., Rewatkar, P., & Haick, H. (2024). Role of machine learning assisted biosensors in point-of-care-testing for clinical decisions. ACS Sensors, 9, 4495-4519. https://doi.org/10.1021/acssensors.4c01582. Bui, M. P. N., Brockgreitens, J., Ahmed, S. & Abbas, A. (2016). Dual detection of nitrate and mercury in water using disposable electrochemical sensors. Biosensors and Bioelectronics, 85, 280-286. https://doi.org/10.1016/j.bios.2016.05.017. Can, F., Ozoner, S. K., Ergenekon, P., & Erhan, E. (2012). Amperometric nitrate biosensor based on Carbon nanotube/Polypyrrole/Nitrate reductase biofilm electrode. Materials Science and Engineering: C, 32, 18-23. https://doi.org/10.1016/j.msec.2011.09.004. Cataldo, D. A., Maroon, M., Schrader, L. E., & Youngs, V. L. (1975). Rapid colorimetric determination of nitrate in plant tissue by nitration of salicylic acid. Communications in Soil Science and Plant Analysis, 6, 71-80. https://doi.org/10.1080/00103627509366547. Chaudhary, V., Rustagi, S., & Kaushik, A. (2023). Bio-derived smart nanostructures for efficient biosensors. Current Opinion in Green and Sustainable Chemistry, 42, 100817. https://doi.org/10.1016/j.cogsc.2023.100817. Chou, S. S., Chung, J. C., & Hwang, D. F. (2003). A high performance liquid chromatography method for determining nitrate and nitrite levels in vegetables. Journal of Food and Drug Analysis, 11, 11. https://doi.org/10.38212/2224-6614.2702. Cui, F., Yue, Y., Zhang, Y., Zhang, Z., & Zhou, H. S. (2020). Advancing biosensors with machine learning. ACS Sensors, 5, 3346-3364. https://doi.org/10.1021/acssensors.0c01424. Gao, L., Barber‐Singh, J., Kottegoda, S., Wirtshafter, D., & Shippy, S. A. (2004). Determination of nitrate and nitrite in rat brain perfusates by capillary electrophoresis. Electrophoresis, 25, 1264-1269. https://doi.org/10.1002/elps.200305840. Gomes, G. F., De Almeida, F. A., Junqueira, D. M., da Cunha Jr, S. S., & Ancelotti Jr, A. C. (2019). Optimized damage identification in CFRP plates by reduced mode shapes and GA-ANN methods. Engineering Structures, 181, 111-123. https://doi.org/10.1016/j.engstruct.2018.11.081. Hashemi Shabankareh, S., Asghari, A., Azadbakht, M., & Asefpour Vakilian, K. (2023). Physical and physiological characteristics, as well as miRNA concentrations, are affected by the storage time of tomatoes. Food Chemistry, 429, 136792. https://doi.org/10.1016/j.foodchem.2023.136792. Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872. https://doi.org/10.1016/j.future.2019.02.028. Huber, C., Klimant, I., Krause, C., Werner, T., & Wolfbeis, O. S. (2001). Nitrate-selective optical sensor applying a lipophilic fluorescent potential-sensitive dye. Analytica Chimica Acta, 449, 81-93. https://doi.org/10.1016/S0003-2670(01)01363-0. Kalimuthu, P., Fischer-Schrader, K., Schwarz, G. & Bernhardt, P. V. (2013). Mediated electrochemistry of nitrate reductase from Arabidopsis thaliana. The Journal of Physical Chemistry B, 117, 7569-7577. https://doi.org/10.1021/jp404076w. Kalimuthu, P., Fischer-Schrader, K., Schwarz, G., & Bernhardt, P. V. (2015). A sensitive and stable amperometric nitrate biosensor employing Arabidopsis thaliana nitrate reductase. Journal of Biological Inorganic Chemistry, 20, 385-393. https://doi.org/10.1007/s00775-014-1171-0. Kalimuthu, P., Ringel, P., Kruse, T., & Bernhardt, P. V. (2016). Direct electrochemistry of nitrate reductase from the fungus Neurospora crassa. Biochimica et Biophysica Acta-Bioenergetics, 1857, 1506-1513. https://doi.org/10.1016/j.bbabio.2016.04.001. Khandelwal, M., & Armaghani, D. J. (2016). Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique. Geotechnical and Geological Engineering, 34, 605-620. https://doi.org/10.1007/s10706-015-9970-9. Kim, W., Bae, S., Park, K., Lee, S., Choi, Y., Han, S., & Koh, Y. (2011). Biochemical characterization of digestive enzymes in the black soldier fly, Hermetia illucens (Diptera: Stratiomyidae). Journal of Asia-Pacific Entomology, 14(1), 11-14. https://doi.org/10.1016/j.aspen.2010.11.003. Kokabi, M., Tahir, M. N., Singh, D., & Javanmard, M. (2023). Advancing healthcare: synergizing biosensors and machine learning for early cancer diagnosis. Biosensors, 13(9), 884. https://doi.org/10.3390/bios13090884. Kumar, V., Kumar, A., Chhabra, D., & Shukla, P. (2019). Improved biobleaching of mixed hardwood pulp and process optimization using novel GA-ANN and GA-ANFIS hybrid statistical tools. Bioresource Technology, 271, 274-282. https://doi.org/10.1016/j.biortech.2018.09.115. Lambeck, I., Chi, J. C., Krizowski, S., Mueller, S., Mehlmer, N., Teige, M., & Schwarz, G. (2010). Kinetic analysis of 14-3-3-inhibited Arabidopsis thaliana nitrate reductase. Biochemistry, 49, 8177-8186. https://doi.org/10.1021/bi1003487. Legnerová, Z., Solich, P., Sklenářová, H., Šatı́nský, D., & Karlı́ček, R. (2002). Automated simultaneous monitoring of nitrate and nitrite in surface water by sequential injection analysis. Water Research, 36, 2777-2783. https://doi.org/10.1016/S0043-1354(01)00513-9. Massah, J., & Asefpour Vakilian, K. (2019). An intelligent portable biosensor for fast and accurate nitrate determination using cyclic voltammetry. Biosystems Engineering, 177, 49-58. https://doi.org/10.1016/j.biosystemseng.2018.09.007. Mohammadi, P., & Asefpour Vakilian, K. (2023). Machine learning provides specific detection of salt and drought stresses in cucumber based on miRNA characteristics. Plant Methods, 19, 123. https://doi.org/10.1186/s13007-023-01095-x. Sohail, M., & Adeloju, S. B. (2016). Nitrate biosensors and biological methods for nitrate determination. Talanta, 153, 83-98. https://doi.org/10.1016/j.talanta.2016.03.002. Tabibi, Z., Massah, J., & Asefpour Vakilian, K. (2022). A biosensor for the sensitive and specific measurement of arsenite using gold nanoparticles. Measurement, 187, 110281. https://doi.org/10.1016/j.measurement.2021.110281. Turner, A. P. (2013). Biosensors: sense and sensibility. Chemical Society Reviews, 42, 3184-3196. https://doi.org/10.1039/C3CS35528D. Yue, X. F., Zhang, Z. Q., & Yan, H. T. (2004). Flow injection catalytic spectrophotometric simultaneous determination of nitrite and nitrate. Talanta, 62, 97-101. https://doi.org/10.1016/S0039-9140(03)00421-1. Zheng, X., Zhang, F., Wang, K., Zhang, W., Li, Y., Sun, Y., Sun, X., Li, C., Dong, B., Wang, L., & Xu, L. (2021). Smart biosensors and intelligent devices for salivary biomarker detection. TrAC Trends in Analytical Chemistry, 140, 116281. https://doi.org/10.1016/j.trac.2021.116281. | ||
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