Nutritional value and quality features of oils are the most important factors that should be considered in food industry. There is no pure edible oil with appropriate oxidative stability and nutritional properties. Therefore, vegetable oils are blended to improve their applications and to enhance their nutritional quality. Characterization of edible oils is important for quality control and identification of oil adulteration. In this work, we propose a simple, rapid, inexpensive and non-destructive approach for characterization of different types of vegetable oil blends according to the corresponding color histograms. Regression models were applied on four datasets of binary edible oil blends including; Palm Olein-Rapeseed, Palm Olein-Sunflower, Soybean-Sunflower and Soybean-Rapeseed. In all of the aforementioned data sets, despite the high performances of Support Vector Regression (SVR) and Levenberg-Marquardt Artificial Neural Network (LMANN) regression models in terms of coefficient of determination, Bayesian Regularized Artificial Neural Networks (BRANN) provided better results up to 97% for HSI color histograms in both the training and test sets. In order to reduce the numbers of independent variables for modelling, principle component analysis (PCA) algorithm was used. Finally, the results of image analysis were compared with those obtained by processing of FT-IR spectra of mixtures of edible oils. The results revealed that image analysis of mixtures of edible oils yield comparable results to those obtained by processing of FT-IR spectra for characterization of edible oils. Our results suggest that the proposed method is promising for characterization of different binary blends of edible oils.

Vegetable oils and fats have a big contribution in our diet as cooking or frying oil, salad oil or in food products formulation. They are important from nutritional and economical point of views. Their authenticity is a serious issue since old time. Some edible oils and fats such as olive oil, cocoa butter and milk fat are so expensive which makes tempting to adulterate them with other lower price vegetable oils and fats to achieve more profit. The need for authentication is a necessity of the food industry. Today, adulterations are more sophisticated. Therefore, it is necessary to use advanced and suitable methods to detect adulteration. Adulteration can cause several problems in edible oils application and industry. To detect edible oils and fats adulteration, it is possible to use both major and minor components as detection tool. Since each oil and fat may have an especial component at a known level, their presence and amounts should be considered as a detection tool. This paper is a brief review on adulteration of edible oils and fats and their detection methods. Several methods have been used to check the purity of edible oils and fats. There is a necessity for food related organization to develop and utilize reliable methods to detect such adulterations, which can make consumers and markets more certain on authenticity and purity of edible oils and fats.

Edible oils can be misdescribed by substituting one ingredient for a similar, but less expensive or over-declaring a quantitative ingredient. Thus, the identification of raw materials in edible oils is important for authentication. In This study, three methods (saponification value, sterol and fatty acids analysis) were used to compare for fraud detection in edible oils. Tests used to assess the quality of sesame oil and oils obtained from mixing sesame oil with sunflower and corn oils. The results showed that campesterol, Δ7-avenasterol, Δ5-avenasterol and Δ7-stigmastenol values can be used as detectors for corn oil mixed with sesame oil, even at a concentration of 5%. Also, in the detection of sesame oil fraud, measuring the fatty acids include oleic, linolenic, linoleic and behenic is more suitable even at a concentration of 5% of sunflower oil adding. In general, the best method for detecting of fraud at all concentrations of added sunflower oil was the saponification value method, while fatty acid composition and desmethylsterols composition was suitable for both corn and sunflower oils mixing.

In this study, qualitative traits and bioactive compounds of edible oil that extracted from three olive promising genotypes (T2, T7, and T18) and two cultivars (Zard and Koroneiki) from Tarom olive collection in Zanjan province were evaluated during two crop seasons (2017-2018). Edible oil quality traits including free fatty acids (FFA), peroxide value (PV) and extinction coefficients (K232 and K270), total chlorophyll, total carotenoids, total phenols, total flavonoids and antioxidant capacity were measured. The results of analysis of variance of year Í cultivar / genotype on oil qualitative traits showed that their interactions on all traits except K270 were significant at 1% probability level. The results showed that edible oil of T7 genotype (0.79% as oleic acid) and Zard cultivar (0.43% as oleic acid) in 1997 had the lowest and highest FFA contents, respectively. Edible oil of T7 in 2018 (17.22 m Eq / kg of oil) and T2 in 2017 (96.67 m Eq / kg) showed the highest and lowest PV levels, respectively. The K232 extinction coefficient of T18 oil was the lowest in both years (0.8 and 1.03 for 2017 and 2018, respectively). Edible oil of Zard cultivar had the highest amount of total phenol in 2017 (243.94 mg gallic acid per kg of oil). T7 genotype had the highest amounts of total flavonoids in 2018 )21.45 mg of quercetin per kg of oil(. Koroneiki cultivar with 90.55% had the highest antioxidant capacity in 2017 and T7 genotype with 41.65% had the lowest value of this index. T2 genotype in both years in terms of antioxidant capacity was in a statistical group with commercial cultivars.

The evaluation of the amount and type of edible oils can significantly help to raise the people’s awareness about how to use this type of food product. The aim of this study was to determine consumption pattern of various edible oils and its related factors based on PRECEDE model.
This cross sectional descriptive analytic study was performed on 300 urban and rural women referring to health centers in Qom province. The participants were selected via multi-stage sampling method. The data were collected using standard questionnaire of PRECEDE model and analyzed by SPSS V.20 using independent t-test, paired t-test, ANOVA, and Pearson correlation coefficient.
The results of this study showed that 46.4% of urban women and 53.6% of rural women used solid oil. There was a significant difference between urban and rural households in terms of the consumption of tallow oil (p Implementation of training programs using training models such as the PRECEDE model can help to modify and correct the pattern of consumption of edible oils, especially in rural areas and promote the level of health of the community.

The presence of trace amount of Tetra -butyl hydroquinone or its metabolite, Tetra -butyl benzoquinone (TBQ), may inhibit cell proliferation and cause biologic abnormalities due to the high prevalence of thiolate groups of proteins or cell walls. The aim of this study was to fabrication an electrochemical nanosensor based on molecular imprinted polymer to detection of TBQ in edible oil. This study was a methodologic study. The statistical population included edible oil samples containing TBQ. The effect of different factors such as amount of MIP and MWCNT for production of ceramic carbon electrodes as well as pH of preconcentration solution and the incubation time of the prepared nanosensor in the solution on the oxidation signal of TBQ was optimized by response surface methodology. Differential pulsed voltammetry has been used to determine the TBQ in oil samples. The Morphologies of MIP and prepared sensors were described by scanning electron microscopy. Optimal conditions for the separation and determination of TBQ in edible oil, including 10 mg of multiwall carbon nanotube, 30 mg of MIP for preparing a modified carbon ceramic electrode, and 8 minutes as incubation time in a 0.1 M phosphate buffer solution with pH 10 was obtained. The proposed method is capable of detecting TBQ in edible oil samples at a concentration range of 6 - 680 nM with a detection limit of 3.1 nM. Based on the results, the proposed sensor can be used as a suitable tool for determination of TBQ in edible oil samples in industries and laboratories.

Considering the widespread use of oils in the preparation of foods, controlling the amount of ingredients in edible oils that can be problematic for human health is extremely important. Due to the complex matrix of oils, analysis of the different compounds in them is usually not possible without isolating and removing the matrix effects. In this regard, a variety of extraction and isolation methods are presented by researchers. Therefore, in this paper it has been tried to introduce in detail the most important extraction methods used in the analysis of edible oils and how they are applied.

Designating a system of policies for basic goods subsidy adjustments and welfare effects of subsidy reduction is widely accentuate by Iranian policy makers and economists. This article tries to answer one of the main questions of this matter such as "what is the equivalent amount of money for compensating welfare loss of household through eliminating bread, sugar and oil subsidies?" By using microeconomic methodology and with regards to the theoretical literature of welfare indices, demand systems and household clustering, we cluster household base on homogeneity of consumption behaviors and estimate linear approximate of almost ideal demand systems (LA\AIDS) for different clusters in five independent groups and also compute welfare indices (equivalent income, compensated variation and the cost of living index). After that, we analyze results of bread, sugar and edible oils price adjustment in five groups of households. For cluster one to three, relative effects of bread price is greater than sugar and edible oils and for clusters four and five, relative effect for edible oils is greater than bread and sugar. Finally, we introduce "Policy Effects Matrix" for bread and conclude that its price adjustment policy is decreasing from cluster one to five.

According to official statistics, domestic production of edible oils provides only less than 10 percent of the country's consumption needs, and therefore every year a significant amount of crude and prepared edible oils and fats is imported to the country spending a considerable amount of foreign exchange. Over the past years rapeseed, as a new product, has been implemented by a limited number of farmers in Khorasan Razavi province. Since the authorities seek to achieve self-sufficiency in edible oils, more attention should be paid to developing oilseeds cultivation, especially rapeseed due to its high percentage of oil and its high quality. In this study, factors that influence the adoption of rapeseed cultivation were determined using Logit model. The study conducted in Khorasan Razavi province in 2008. Using two-stage random sampling, 300 farmers were selected for this study. It is concluded that the variables including area of irrigated cultivation, the extent at which farmers implement advisors ‘ recommendations, communication with farmers who cultivate rapeseed, influence of cultivating rapeseed on Grain yield and agricultural income have a significant contribution to rapeseed cultivation.

Three electrode system simulates the human tongue taste system and can be used to assess the quality of food. The sense of taste is one of the five senses and tongue recognizes certain molecules. In recent years, three electrode system with an array of electrodes has been used to identify various molecules. In this research, a three electrode system is used based on voltammetric methods with three graphite electrodes (Pencil Graphite (PG)), Screen Printed (SP) and Glassy Carbon (GC) to identify heavy metals (cadmium , Lead, tin and nickel) in olive oil. Heavy metals are added to the edible oil in three concentrations of 0.05, 0.1 and 0.25 ppm and then the output of the device is classified by chemometric method. According to PCA results, the PG electrode contains 96% of the variance between the data in olive edible oils. Also, SP electrode contains 91% and GC contains 100% of the variance between the data in olive oil. The SVM method showed a high ability to classify heavy metals in edible oils. Also, The PLS method was also able to predict 99% of the data in olive oil for all electrodes. Finally, according to the results, it can be said that the built-in three electrode system has a high accuracy in identifying heavy metals in edible oils.