نوع مقاله : مقاله پژوهشی
نویسنده
استادیار، گروه مهندسی مکانیک بیوسیستم، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران
10.22092/amsr.2024.367532.1502
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.
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