Food Technology
Reza Jaberi; Ahmad Pedram Nia; Sara Naji-Tabasi; Amir Hossein Elhami Rad; Masoud Shafafi
Abstract
Introduction: Fats are widely used in food formulations to improve nutrients and quality of food products. In recent years, consumer awareness of the relationship between diet and health has increased, which results in increasing concerns about fats in products, in terms of high levels of saturated fatty ...
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Introduction: Fats are widely used in food formulations to improve nutrients and quality of food products. In recent years, consumer awareness of the relationship between diet and health has increased, which results in increasing concerns about fats in products, in terms of high levels of saturated fatty acids. Therefore, various attempts have been conducted to find appropriate method to produce solid fats with unsaturated fatty acid content. The production of low-fat cake is of special nutritional importance due to the effects of fats and the incidence of various diseases such as hypercholesterolemia and cardiovascular. In cake making, oil is very important, first of all, it has an effect on keeping the air in the cake dough. It causes porosity and increases the volume of the cake. The conversion of liquid oils to gel-like structures that have the properties of a solid fat (rheological properties, viscoelasticity, dispersibility, and softness) without using large amounts of saturated fats is an important development in the food industry, and structures produced by oleogelation are called oleogels. Therefore, the main purpose of this study was to produce low-calorie muffins by removing fat and replacing it with an oleogel system prepared from a soluble complex of xanthan gum and egg white by indirect foam molding. Materials and Methods: First, aerogels were produced indirectly using egg white protein complex and xanthan gum, and then oleogel was produced by adsorption of oil. In this method, first, water suspensions containing 0.5 wt% of xanthan and 5 wt% of egg white protein were prepared. In the next step, the oleogel was used to produce muffin with reduced oil in three levels of 10, 30 and 50%. In this study, texture characteristics, color, porosity, water activity, moisture, specific volume of muffin samples were investigated. In the control sample, cake dough contained 100% wheat flour, 50% sugar powder, 30% liquid vegetable oil, 2% baking powder, 0.2% vanilla, 36% eggs and 12% invert syrup. To prepare samples containing oleogels, oil was removed and different concentrations of oil reduction at the level of 50, 30 and 10% in the formulation were added to each treatment. Results and Discussion: Muffin with 10% reduced oil had the highest volume and density and had similar texture characteristics to the control sample. By reducing the amount of fat to 50% of the initial amount in muffin, the volume decreased and the firmness of the texture increased significantly (p <0.05). The reduction of the percentage of porosity confirmed that the texture stiffness in higher values of substitution. The sensory evaluation showed that the 10% reduced oil sample had the highest consumer acceptance. According to the results of this study, it can be suggested that preparing muffins with oleogels can reduce the problems caused by fats, while improved sensory and qualitative properties and be produced as a functional food.
Mahdi Irani; Masoud Shafafi; Hasan Irani
Abstract
This paper presents a novel approach to monitor food process based on Modular Neural Networks (MNNs) and fuzzy inference system. The proposed MNN consists of three separate modules, each using different image features as input including: edge detection, wavelet transform, and Hough transform. The sugeno ...
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This paper presents a novel approach to monitor food process based on Modular Neural Networks (MNNs) and fuzzy inference system. The proposed MNN consists of three separate modules, each using different image features as input including: edge detection, wavelet transform, and Hough transform. The sugeno fuzzy inference system was used to combine the outputs from each of these modules to classify the images of quince during osmotic dehydration process. To test the method, for classification, database was made of 108 quince samples’ images (12 classes). In experiments, the developed architecture achieved 91.6% recognition accuracy. Next step, solid gain, water loss and moisture content of quince samples were considered as MNNs outputs, whereas osmotic dehydration time and classified images were MNNs inputs. The minimum %MRE (18.153) with 89% prediction ability for water loss (WL) was obtained when applying two hidden layers with 6 neurons per each two layers. The lowest %MRE (35.5335) with 93% prediction ability for solid gain (SG) was obtained when using 6 and 8 neurons per first and second layer, respectively. And finally %MRE was at least (7.4759) with 96% prediction ability for moisture content (MC) by 6 and 5 neurons per first and second layer, respectively. The results show that this model could be commendably implemented for quantitative modeling and monitoring of food quality changes during osmotic dehydration process.
