مدولاسیون دمایی حسگر گاز مبتنی بر اکسید قلع جهت تشخیص خلوص سرکه با استفاده از الگوریتم k نزدیکترین همسایگی
محورهای موضوعی : مهندسی الکترونیکوحید خرمشاهی 1 * , فاطمه صفری 2 , علی فاتحی فر 3
1 - مرکز تحقیقات مواد و انرژی، واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران
2 - مرکز تحقیقات مواد و انرژی، واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران
3 - دانشگاه آزاد اسلامی واحد دزفول
کلید واژه: اکسید قلع, مدولاسیون دما, بینی الکترونیکی, حسگر گاز, طبقه بندی.,
چکیده مقاله :
مدولاسیون دما در حسگرهای گاز مبتنی بر اکسیدهای فلزی، از جمله SnO₂، موجب تغییر دمای عملیاتی حسگر می‌شود. این تغییرات، حساسیت و گزینش‌پذیری حسگر را در تشخیص اسید استیک موجود در سرکه افزایش داده و امکان تعیین خلوص سرکه را فراهم می‌کنند. در مدولاسیون دمایی حسگرهای گاز، از چرخه‌های دمایی برای ثبت پاسخ حسگر به ترکیبات مختلف استفاده می‌شود. این پاسخ‌ها به‌عنوان ویژگی‌های طبقه‌بندی استخراج شده و امکان تمایز و شناسایی ترکیبات را فراهم می‌کنند. در این پژوهش، با استفاده از الگوریتم نزدیکترین همسایه، ویژگی‌های استخراج‌شده به‌طور مؤثری برای طبقه‌بندی نمونه‌های سرکه و تعیین خلوص آن‌ها به کار گرفته شد. به ریزگرمکن حسگر گاز MQ2 یک ولتاژ مربعی اعمال شد که موجب تغییر دمای حسگر و ایجاد الگوهای متمایز در پاسخ آن گردید. پاسخ ولتاژ حسگر به بخارات سرکه با خلوص‌های مختلف در سه مرحله ثبت شد. از هر الگوی ایجادشده در پاسخ حسگر، هفت ویژگی متمایز انتخاب شد که به‌عنوان ورودی‌های الگوریتم طبقه‌بندی مورد استفاده قرار گرفتند. دقت الگوریتم پیشنهادی در تشخیص خلوص سرکه برابر با ۸۶ درصد بوده و این روش قابلیت استفاده در سیستم‌های تشخیص خودکار را دارد.
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.
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