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ارزیابی الگوهای شناسایی برای تشخیص ناخالصی روغن کنجد توسط سامانۀ ماشینبویایی مبتنی بر تحلیل چند متغیره | ||
| تحقیقات سامانهها و مکانیزاسیون کشاورزی | ||
| مقاله 3، دوره 23، شماره 81، خرداد 1401، صفحه 37-56 اصل مقاله (2.21 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/amsr.2022.356371.1400 | ||
| نویسندگان | ||
| هادی حسینی1؛ سعید مینایی* 2؛ بابک بهشتی3 | ||
| 1دانشجوی دکتری گروه مهندسی مکانیک بیوسیستم، دانشکده علوم کشاورزی و صنایع غذایی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران | ||
| 2استاد گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران | ||
| 3استادیار گروه مهندسی مکانیک بیوسیستم، دانشکده علوم کشاورزی و صنایع غذایی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران | ||
| چکیده | ||
| روغن کنجد یکی از روغنهای بسیار با کیفیتِ خوراکی است که قیمت بالای آن، انگیزه را برای تولید نمونۀ تقلبی افزایش داده است. بنابراین، شناسایی ناخالصی به ویژه با ابزار یا روشهای غیرتماسی برای تشخیص خلوص روغن کنجد نسبت به نوع تقلبی آن، اهمیت زیادی دارد. در این پژوهش، میزان اسیدهای چرب نمونههای خالص روغن کنجد، کلزا، آفتابگردان و ترکیب آنها با سطوح ناخالصی (5، 10، 20، 30، 40 و50 درصد) به روش گازکروماتوگرافی تعیین و سپس، قابلیت سامانۀ ماشینبویایی طراحی شده بر پایۀ ده حسگر نیمههادی اکسید فلزی برای تشخیص و طبقهبندی نمونهها ارزیابی شد. پس از استخراج بردار ویژگیِ پاسخ حسگرها نسبت به ترکیبات فرار مواد آلی روغنها، دادههای پیشپردازش شده با روشهای تحلیل چندمتغیره تحلیل مؤلفههای اصلی، تحلیل تفکیک خطی، حداقل مربعات جزئی، K-نزدیکترین همسایه و ماشین بردارپشتیبان به منظور تشخیص و طبقهبندی نمونههای ناخالص، بررسی شدند. در روش PCA، واریانس مجموعه دادهها 68/95 درصد به دست آمد. برای انتخاب الگوی مناسب با دقت بالا، دادههای به دست آمده با روشهای LDA، KNN و SVM طبقهبندی شدند. نتایج نشان داد ماشین بردارپشتیبان با تابع کرنل پایۀ شعاعی در روش C-SVM دارای بیشترین دقت طبقهبندی بود و دقت آموزش و اعتبارسنجی به ترتیب 34/96 و 56/90 درصد به دست آمد. بعد از آن، مدلهای LDA و KNN به ترتیب دارای دقت طبقهبندی 30/92 و 83/89 درصد بودند. بر پایه نتایج به دست آمده، این سامانه به همراه الگورتیم طبقهبندی مناسب میتواند به عنوان روشی غیرمخرب برای طبقهبندی موفق نمونهها و تشخیص ناخالصیهای روغن کنجد به کار گرفته شود. | ||
| کلیدواژهها | ||
| بوسنجی؛ بینیالکترونیک؛ تحلیل مولفۀ اصلی؛ روغن خوراکی؛ غیرمخرب | ||
| عنوان مقاله [English] | ||
| Evaluation of Pattern Recognition for Detecting Adulteration in Sesame Oil using Machine Olfaction System Based on Multivariate Analysis | ||
| نویسندگان [English] | ||
| Hadi Hosseini1؛ Saeid Minaei2؛ Babak Beheshti3 | ||
| 1Department of Biosystems Engineering, Faculty of Agricultural Sciences and Food Industry, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 2Biosystems Eng. Dept. Tarbiat Modares Univ. Tehran, Iran | ||
| 3Department of Biosystems Engineering, Faculty of Agricultural Sciences and Food Industry, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| چکیده [English] | ||
| Sesame oil which is one of the most popular and expensive edible oils, is prone to adulteration. High price of Sesame oil has motivated adulterers to mix the high-quality Sesame oil with low-quality, less expensive vegetable oils. In this study, the fatty-acid profiles of sesame, rapeseed, sunflower oil samples as well as their mixtures (0, 5, 10, 20, 30, 40 and 50% levels) were determined using Gas Chromatography. Also, Machine olfaction system containing 10 MOS sensors was utilized for detection experiments. Sensor response patterns were used for analyzing and recognizing pattern of electronic-nose signals using multivariate data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analyses (LDA), Partial Least Squares (PLS), K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). Results of the SVM with RFB kernel in C-SVM method had the highest classification accuracy. The accuracy of training and validation were 96.34 and 90.56%, respectively, and next were LDA and KNN models with classification accuracies of 92.30% and 89.94%, respectively. In the light of these results, the proposed models along with the measurement system represent excellent tools for the detection of sesame seed oil adulteration with cheaper vegetable oils. | ||
| کلیدواژهها [English] | ||
| Edible Oil, Electronic Nose, Non-Destructive, Olfactometry, Principal Component Analysis | ||
| مراجع | ||
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