انتخاب الگوریتم بهینه‌ی طبقه‌بندی و تشخیص تقلب در روغن زیتون با استفاده از ماشین‌بویایی

روغن زیتون فرابکر همیشه مورد توجه و خواست استفاده‌کنندگان می‌باشد؛ از این‌رو در روغن‌های زیتونِ بکر و فرابکر، تقلب، با افزودن روغن‌های با ارزش غذایی و قیمت کمتر مثل کانولا، آفتابگردان، تفاله زیتون و غیره مشاهده می‌گردد. در این پژوهش با استفاده از فناوری ماشین بویایی روغن زیتون فرابکر، از نمونه‌های تقلبی تهیه شده با روغن‌های متداول در بازار و با هفت مدل طبقه‌بندی مختلف تشخیص داده شد. نمونه‌ها در شش دسته‌ی خالص و 5، 10، 20، 35 و 50 درصد تقلب و هر تیمار در هفت نمونه تهیه و آزمایش‌ها در هفت تکرار انجام گرفت. سامانه بویایی از هشت حسگر متفاوت تشکیل شده که برای هر کدام نمودار تغییر ولتاژ بر حسب زمان تهیه و از آن نمودار چهار ویژگی کمینه، بیشینه، میانگین و اختلاف بیشینه و کمینه در مدل‌های طبقه‌بندی استفاده گردید. به این ترتیب تعداد 32 ویژگی برای هشت حسگر استخراج و مورد تجزیه و تحلیل قرار گرفت. به جهت تاثیر دمای نمونه‌ها در خروجی سامانه، تمام آزمایش‌ها در دمای ثابت انجام گرفتند. طبقه‌بندی نتایج با چهار الگوریتم طبقه‌بندی kهمسایگی نزدیک، ماشین بردار پشتیبان، شبکه عصبی مصنوعی و آدابوست صورت پذیرفت. 70% داده‌ها برای آموزش و 30% برای آزمون استفاده گردید. از بین 32 ویژگی، ویژگی کمینه مقدار خروجی سنسور tgs-822 بیشترین تاثیر را در دقت طبقه‌بندی داشتند. نتایج نشان داد روش طبقه‌بندی همسایگی نزدیک با بهترین دقت (89.89%) و پس از آن روش ماشین بردار پشتیبان (86.52%) بیشترین دقت طبقه‌بندی را دارا بودند.

Estimation of the Best Classification Algorithm and Fraud Detection of Olive Oil by Olfaction Machine

IntroductionExtra Virgin Olive Oil (EVOO) is one of the most common and popular edible oils which is an important part of the Mediterranean diet. It is a rich source of sterol, phenol compounds and vitamins A and E. EVOO has useful effects on human body and significant reduction of cardiovascular diseases due to these benefits, EVOO is expensive so unfortunately adulteration in EVOO by mixing it with other cheap and low cost and low value oils such as canola, sunflower, palm and etc. is very common. Adulteration leads to health and financial losses and sometimes cause serious illness. Olive oil has various quality levels which depend on different factors such as olive cultivar, storage, oil extracting process etc.Materials and MethodsThere are numerous food quality evaluation and adulteration detection approaches which include destructive and nondestructive methods. Control sample (EVOO) was applied from DANZEH food industry , Lowshan, Gilan Province. For ensuring that control sample is extra virgin, a sample was tested in Rahpooyan e danesh koolak Lab. Tehran, Iran; according to Institute of standards and industrial research of Iran ISIRI number: 4091 and INSO 131262. Eight semiconductor gas sensors FIS, MQ3, MQ3, MQ4, MQ8, MQ135, MQ136, TGS136, TGS813 AND TGS822 applied in used olfaction machine. In this study there were 6 treatments: 1 Pure EVOO, 2 EVOO with 5% adulteration, 3 EVOO with 10% adulteration, 4 EVOO with 20% adulteration, 5 EVOO with 35% adulteration and 6 EVOO with 50% adulteration. Adulteration created with ordinary frying oil (including sunflower, canola, and maize oils). Each treatment prepared in seven samples and each sample test was repeated seven times. In this study, olfaction machine, a nondestructive, simple and user friendly System applied. As mentioned, the olfaction machine includes eight different sensors, so each test has eight graphs. Four features (1 Sensor output (mV) in start of odor pulse (refer to fig. 3) 2 Sensor output at the end of odor pulse 3 Average of sensor output during odor pulse and 4 Difference of sensor output at the end and start of start of odor pulse); So 32 features extracted and analyzed and finally effective sensors reported.Results and DiscussionHistogram and box plot of raw data showed that the data are not normal and need some preprocessing operations. Preprocessing facilitates data analyzing and classifying extracted features. After preprocessing, the standard data, divided into two classes: train data (70%) and test data (30%). Data classified with 4 different classifier models which include: Knearest neighbors, support vector machine, artificial neural network and Adaboost. Results showed that KNN method, with 89.89% and SVM with 86.52% classified with higher accuracy. Similarly, the confusion matrix showed the reasonable results of classifying operation. Also, three effective sensors in classifying determined TGS2620, MQ5 and MQ4 respectively, and on the other side, sensors such as MQ3 and MQ8 have the minimum effect on classifying so it is possible to remove these sensors from the sensor array without effective impress on results. This may cause decrease in the olfaction machine price and reduce analyzing time.ConclusionsDue to increasing adulteration in foods, especially in olive oil and its significant effects on people apos;s health and financial losses, a simple, cheap and nondestructive quality evaluation extended. Results showed that the olfaction machine with metal oxide semiconductor (especially including TGS 2620, MQ5 and MQ4 sensors) can use for classification and adulteration detection of extra virgin olive oil. Evaluation of this system apos;s output leads to higher classification accuracy by using KNN and SVM method for olive oil classification and also fraud detection (5% adulteration).