M. Kaveh; Y. Abbaspour Gilandeh; R. Amiri Chayjan; R. Mohammadigol
Abstract
Introduction Garlic (Allium sativum L.) is an important Allium crop in the world. Due to its therapeutic properties, it was cultivated in many countries. Furthermore, garlic is usually used as a flavoring agent; it may be used in the shape of powder or granule as a valuable condiment for foods. In addition ...
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Introduction Garlic (Allium sativum L.) is an important Allium crop in the world. Due to its therapeutic properties, it was cultivated in many countries. Furthermore, garlic is usually used as a flavoring agent; it may be used in the shape of powder or granule as a valuable condiment for foods. In addition to its use in food products, it was also widely used as an anticancer agent. Shallot (Allium hiertifolium Boiss. L) is a perennial and bulbous plant. It is from Alliaceae family and is an important medicinal plant. The shallot is native of Iran, and grows in the high pastures. Shallot is consumed in dry areas in most parts of the country. Also shallots have been well known in Iranian folk medicine and its bulbs have been widely used for treating rheumatic and inflammatory disorders. In addition, this plant is used in the preparation of significant amounts of potassium, phosphorus, calcium, magnesium, sodium, pickles and as an additive to yogurt and pickles. ANN as a modern approach has successfully been used to solve an extensive variety of problems in the science and engineering, exclusively for some space where the conventional modeling procedure fail. A well-trained ANN can be used as a predictive model for a special use, which is a data processing system inspired by biological neural system. When mathematical equations are difficult to extrapolate, and fuzzy logic is better when decisions must be made with the estimated values below the incomplete information. The fuzzy logic theory effectively addresses the uncertainty problems that solve the ambiguity. Materials and Methods The aim of this study was to predict moisture ratio of garlic and shallot during the drying process with fluidized bed dryer using mathematical model, artificial neural networks and fuzzy logic methods. Tests were carried out on three levels of inlet air temperature (40, 55 and 70 °C) and three inlet air velocities (0.5, 1.5 and 2.5 m s-1). To estimate the drying kinetic of garlic and shallot, five mathematical models were used to fit the experimental data of thin layer drying. Three factors (air temperature, air velocity and drying time) to forecast moisture ratio in fluidized bed dryer as independent variables for artificial neural networks and fuzzy logic was considered. Cascade forward back propagation (CFBP) and feed forward back propagation (FFBP) with Levenberg-Marquardt (LM), Bayesian learning (BR) algorithms for ANN and the Mamdani Fuzzy Inference System using triangular membership function were used for training patterns. Results and Discussion Consequently, the Page and Midilli et al. model was selected as the best mathematical model to describe the drying kinetics of the garlic and shallot slices, respectively. The results of artificial neural networks model for predicting MR showed that the R2 of 0.9994 and 0.9996; and and RMSE of 0.0036 and 0.0014 were obtained for garlic and shallot, respectively. Also, The fuzzy inference system presented the R2 of 0.9997 and 0.9998; and and RMSE of 0.0027 and 0.0011 for garlic and shallot, respectively. Comparing the results obtained from mathematical models, artificial neural networks and fuzzy logic, showed that the RMSE in the fuzzy logic was lower than artificial neural network and mathematical models. Conclusion Three factors (air temperature, air velocity and drying time) were considered for forecasting moisture ratio in fluidized bed dryer as independent variables using mathematical model, artificial neural networks and fuzzy logic. Cascade forward back propagation (CFBP) and feed forward back propagation (FFBP) with Levenberg-Marquardt (LM), Bayesian learning (BR) algorithms and the Mamdani Fuzzy Inference System using triangular membership function were used for training the patterns. Comparing the results obtained from mathematical models, artificial neural networks and fuzzy logic, showed that the root mean square error in fuzzy logic was lower than others.
S. Sadeghzade Namavar; J. Amiri Parian; R. Amiri Chayjan
Abstract
Introduction The artichoke is part of the foods from the vegetable group that provide important nutrients like vitamin A and C, potassium and fiber which used as a food and medicine. In the pharmaceutical sector, dried extracts are used in the preparation of pills and capsules. Dried extracts can be ...
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Introduction The artichoke is part of the foods from the vegetable group that provide important nutrients like vitamin A and C, potassium and fiber which used as a food and medicine. In the pharmaceutical sector, dried extracts are used in the preparation of pills and capsules. Dried extracts can be prepared from the dehydration of a concentrated extractive solution from herbal materials (leaves, roots, seeds, etc.), resulting in a dried powder. The spray drying is widely used in the preparation of dried powders from extracts of medicinal plants, fruit pulps. One of the newly developed spray drying techniques is an ultrasonic vacuum method, which strengths of spray drying by incorporation of ultrasonic atomizer and vacuum chamber. Nowadays, image processing has been applied to food images, as acquired by different microscopic systems, to obtain numerical data about the morphology and microstructure of the analyzed foods. For this purpose, microscopy and image processing techniques could be considered as proper tools to evaluate qualitatively and quantitatively the food microstructure, making possible to carry out numerical correlations between microstructure data, as obtained from the images, and the textural properties of food powders. The textural characteristics of the obtained dried powders are determined by means of a perfect detection by scanning electron microscopy (SEM) pictures, and analyzed with a statistical approach for image texture studies, which calls the gray level co-occurrence matrix (GLCM) technique. The object of this study was to illustrate the application of image processing to the study of texture properties from extract powder using GLCM texture analysis and some vacuum spray dryer conditions effect on the texture features of mass particles and single particle SEM images. Materials and Methods After preparing water extract solution from artichoke leaves, extracts were dried under four conditions of vacuum spray drying (according to Table 1). To study the texture of the obtained dried extract powders, different representative features are extracted from the GLCM matrix. The angular second moment (ASM), which is defined as a measure of the homogeneity of the image, the contrast parameter (CT), which represents the amount of local variations given by differences in the gray values in the image. The correlation value (CR), which is a measure of gray tone linear dependencies in the image depending on the direction of the measure (different θs). The inverse difference moment value (IDM), which, similar to ASM, quantifies the homogeneity of the image, however, using a different equation, the entropy parameter (ET), which is a measure that is inversely related to the order given by the gray tones in the image. Rangefilt and stdfilt calculates the local range and local standard deviation of an image respectively. Entropyfilt calculates the local entropy of a grayscale image also. Parameters (ASM, CT, CR and IDM were analyzed in four directions (0º, 45º, 90º, and 135º). Results and Discussion The results of analysis of variance showed that, the difference between the textural features of a single particle and mass particles in four different conditions vacuum spray dryer was significant statistically. Texture analysis was demonstrated that larger ASM, CR, and IDM values indicate less roughness, whereas larger CT and ET values indicate more roughness. At lower inlet temperature and higher vacuum pressure, water diffusion in the material to be slower and allowing the deformation process in the particles to be more pronounced. Consequently, it was possible to observe that generated smaller particles are rougher and less spherical. When the concentration is increased, due to the constant concentration of the additive, the ratio of excipient (lactose) to extraction decreased, as a result were formed a greater number of particles with rougher surfaces. According to these conditions, the values of CT, ET, rangefilt and stdfilt were larger while ASM, CR, and IDM values were smaller. By analyzing the effect of the angle on the oriented textural characteristics, the contrast and correlation parameter were maximum at the angles of 45 and 135 degrees and 0 and 90 degrees respectively. Conclusion Image processing could be auxiliary tools for understanding and characterizing complex systems such as food and biological materials. In this study imaging-based technique was developed to evaluate the texture properties of artichoke leaf extract powder at different conditions of vacuum spray drying. The use of higher temperatures and lower vacuum pressures contributed to faster evaporation rate and production of smoother and larger particles, thereby increasing ASM, CR, and IDM values and reducing CT, ET, Rangefilt and stdfilt. Furthermore, the contrast and entropy parameters showed inverse trends in comparison with correlation, energy and homogeneity. Decrease of solution concentration resulted in the more presence of lactose in the composition of extract/excipient improves the textural properties of powders. The direction parameter had also affected on GLCM textural features. Two oriented textural characteristics (contrast and correlation) also showed significant differences with respect to the nature of particle texture in different directions of measurement. The obtained data extracted from image analysis may provide valuable information to understand the role of structure with respect to product functionality.
I. Golpour; J. Amiri Parian; R. Amiri Chayjan; J. Khazaei
Abstract
Identification of rice cultivars is very important in modern agriculture. Texture properties could be used to identify of rice cultivars among of the various factors. The digital images processing can be used as a new approach to extract texture features. The objective of this research was to identify ...
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Identification of rice cultivars is very important in modern agriculture. Texture properties could be used to identify of rice cultivars among of the various factors. The digital images processing can be used as a new approach to extract texture features. The objective of this research was to identify rice cultivars using of texture features with using image processing and back propagation artificial neural networks. To identify rice cultivars, five rice cultivars Fajr, Shiroodi, Neda, Tarom mahalli and Khazar were selected. Finally, 108 textural features were extracted from rice images using gray level co-occurrence matrix. Then cultivar identification was carried out using Back Propagation Artificial Neural Network. After evaluation of the network with one hidden layer using texture features, the highest classification accuracy for paddy cultivars, brown rice and white rice were obtained 92.2%, 97.8% and 98.9%, respectively. After evaluation of the network with two hidden layers, the average accuracy for classification of paddy cultivars was obtained to be 96.67%, for brown rice it was 97.78% and for white rice the classification accuracy was 98.88%. The highest mean classification accuracy acquired for paddy cultivars with 45 features was achieved to be 98.9%, for brown rice cultivars with 11 selected features it was 93.3% and it was 96.7% with 18 selected features for rice cultivars.
