جستجوی مقالات مرتبط با کلیدواژه « olive oil » در نشریات گروه « مکانیزاسیون کشاورزی »

Extra 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.

There are numerous food quality evaluation and adulteration detection approaches which include destructive and non-destructive 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 13126-2. Eight semi-conductor 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 non-destructive, 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.

Histogram 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: K-nearest neighbors, support vector machine, artificial neural network and Ada-boost. 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.

Due to increasing adulteration in foods, especially in olive oil and its significant effects on people's health and financial losses, a simple, cheap and non-destructive 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's output leads to higher classification accuracy by using KNN and SVM method for olive oil classification and also fraud detection (5% adulteration).

The fatty acids profiles of virgin olive oil of some of the provinces of Iran were examined to investigate their similarities and differences. Indices used to classify these oils include fatty acids, oleic to linoleic ratios, monounsaturated fatty acids (MUFAs), polyunsaturated fatty acid (PUFAs), saturated fatty acids (SFAs), MUFA to PUFA ratios and Cox index. The sensory attributes of the olive oil samples were evaluated to determine their virgin category of olive oil. Analysis of variance (ANOVA), principal component analysis (PCA), and hierarchical clustering analysis (HCA) were used to evaluate the data. The olive oil samples were divided into two groups based on PCA and HCA. The samples of Golestan, Zanjan and Qazvin provinces (Go1, Qa1 and Z1) were in the higher quality group and the other samples were in the lower quality group. Considering the sensory characteristics results, all samples except for Qom and Fars were extra virgin. The findings of this study will help the consumer in selection of olive oil.

The main fraud way in olive oil adulteration is mixing with other vegetable oils such as maize, canola, sunflower and soy oil. Therefore, the aim of this study was to evaluate a portable system to detect adulteration in olive oil using combination of machine vision and capacitive methods. To identify adulteration in olive oil by using frequency and color features, Principal Component Analysis (PCA) and Linear discriminant analysis (LDA) were thus applied. The best neural network model with 36-6-1 structure had R2 of 0.944 and mean square error of 0.006, for prediction of adulterated olive oil with sunflower oil. Also, the best model of neural network for olive-canola mixture had the structure of 36-10-1, R2 and mean square error of 0.946 and 0.003, respectively. Finally, a combination of frequency and color properties was used to develop the models. The R2 and mean square error of mixed samples of olive-sunflower were obtained as 0.962 and 0.008 for 38-2-1 network. For mixed samples olive-canola, the R2 and mean square error were obtained 0.961 and 0.0013 for the structure of 38-16-1 neural network.

Knowledge of the attributes of a cultivar is important to selection of the appropriate cultivar for modern olive culture. These include productivity, resistance to pests and natural conditions, high oil content, oil quality, size and shape of fruit, and high proportion of meat to core. The type and amount of fatty acid in olive oil determines its quality and the economic value of the oil. The composition of olive is significantly affected by the cultivar and climatic conditions. The present study determined the physical and chemical characteristics of olive and olive oil extracted from two Iranian varieties of olive (Zard and Dezful), bothgrown in the regions of Shiraz and Kazeroon, two major olive producing areas in Fars province. After determining the physical characteristics of the fruit, the olive oil was mechanically extracted. Physical characteristics of the fruit (weight, density, length, diameter of fruit and core, and meat-to-core ratio) were recorded. The fatty acid composition of the oil was determined by gas chromatography. The results showed that the physical and chemical properties of both cultivars were in accordance with national and international standards. The Dezful cultivar showed a longer length of core, but there were no significant differences between cultivars for other physical characteristics. The length of fruit in Shiraz and the meat-to-core ratio was higher than offruit in Kazeroon. There was no significant differencesbetween areas for the other physical characteristics. The biochemical indicators between cultivars indicated that the oleic, stearic, and arachidic acid content of the Zard cultivar was higher than for the Dezful cultivar and the palmitic, palmitoleic, and linoleic acid content of the Dezful was higher than the Zard. There was a positive correlation between oleic acid content and temperature. Shiraz olives had the highest oil and oleic acid contents and the linoleic acid, palmitic, and palmitoleic acid content ofKazeroon oliveswere higher. The Zard cultivar in Shiraz had the highest oleic acid content.The superior quality of the Zard cultivar in Shiraz, which had the highest oleic acid content (74.2%) and lowest linoleic acid content, recommends it as the best variety. These results indicate that the quality of the olive oil depends both on the olive cultivar and the geographical location.

Olive oil is one of the oldest known vegetable oils, ranking sixth in world production. Since it is extracted from fresh fruit and used without aging, the conditions of oil production are very important. During the extraction of virgin olive oil, the milling and malaxation steps are vital for increasing oil yield and quality. In this study, the effect of milling (single or double), temperature of malaxation (25, 35, 45, 60°C) and duration of malaxation (15, 30, 45, 60min) on yield and quality (acidity, peroxide index, color, carbonyl values) of virgin olive oils (cultivar Roghani) were investigated. The results showed that double milling increased oil yield, but that the type of milling had no significant effect on oil quality. Increasing temperature and duration of malaxation increased oil yield, however oil production increased substantially up to 35°C and 45 min but did not show a significant difference after that point. An examination of the qualitative characteristics of oil showed that, under the conditions tested, there was no significant difference in quality. The oil produced was of the highest quality. It was concluded that the optimal extraction method for the production of good quality oil with a satisfactory yield is to employ double milling with malaxation for 45 min at 35°C.