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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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