Comparison between artificial neural networks and mathematical models for estimating equilibrium moisture content in raisin

Empirical models and Artificial Neural Networks (ANNs) were utilized for the prediction of Equilibrium Moisture Content (EMC) in raisin.  Six empirical models including GAB, Smith, Henderson, Oswin, Halsey and D’Arsy-watt were applied for this estimation.  Two types of Multi Layer Perceptron (MLP) neural networks entitled Feed Forward Back Propagation (FFBP) and Cascade Forward Back Propagation (CFBP) were used.  In order to train the input patterns, two training algorithms consist of Levenberg-Marquardt (LM) and Bayesian regularization (BR) were used.  Thermal and relative humidity limits were 30-80℃ and 10.51%-83.62%, respectively.  The best result for mathematical models belonged to D’Arsy-Watt with R2 and the mean relative error of 0.9943% and 10.84%, respectively.  The best outcome for the use of ANN also appertained to FFBP network with LM training algorithm, topology of 2-3-3-1 and threshold function order of TANSIG-TANSIG-PURELIN.  With this optimized network, R2 and the mean relative error was 0.9969% and 8.32%, respectively.  These results show the supremacy of ANN, in comparison with empirical models.  In order to predict the EMC in raisins, empirical models can therefore be replaced with the ANN.Keywords: ANN, back propagation, sorption isotherm, EMC, Iran Citation: Chayjan R. Amiri, and M. Esna-Ashari.  Comparison between artificial neural networks and mathematical models for estimating equilibrium moisture content in raisin.  Agric Eng Int: CIGR Journal, 2010, 12(1): 158-166

Modelización de la difusividad de la humedad, la energía de activación y el consumo específico de energía para el grano de maíz húmedo en un secador convectivo de lecho fijo y fluidizado

Thin layer drying characteristics of high moisture corn under fixed, semi fluidized and fluidized bed conditions with high initial moisture content (66.82% wb) in a laboratory fluidized bed convective dryer was studied at air temperatures of 50, 65, 80 and 95°C. In order to find a suitable drying curve, seven thin layer-drying models were fitted to the experimental data of moisture ratio. Among the applied mathematical models, Midilli et al. model was the best for drying behavior prediction in corn thin layer drying. This model presented high values for correlation coefficient (R2). Fick´s second law was used to compute moisture diffusivity with some simplifications. Computed values of moisture diffusivity varied at the boundary of 4.87 × 10–11 – 2.90 × 10–10 m2 s–1 and 1.02 × 10–10 – 1.29 × 10–9 m2 s–1 during the first and second drying falling-rate, respectively. Values of effective moisture diffusivity for corn were also increased as input air temperature was increased. Value of activation energy varied from a minimum of 18.57 to a maximum of 50.74 kJ mol–1 from 50 to 95°C with drying conditions of fixed to fluidized bed. Specific energy consumption (SEC) for thin-drying of high moisture corn was found to be in the range of 0.33 × 106 – 1.52 × 106 kJ kg–1 from 50 to 95°C with drying condition of fluidized and fixed bed, respectively. Increase in air temperature in each air velocity caused decrease in SEC value. These corn properties would be necessary to design the best dryer system and to determine the best point of drying process.Se estudiaron las características del secado en capa delgada del grano de maíz húmedo en condiciones de lecho fijo, semi-fluidizado y fluidizado con alto contenido de humedad inicial (66,82%), en un secador de convección de lecho fluidizado de laboratorio a las temperaturas del aire de 50, 65, 80 y 95°C. Con el fin de encontrar una curva de secado apropiada, se ajustaron siete modelos matemáticos de secado en capa delgada a los datos experimentales de la ratio de humedad. Entre los modelos aplicados, el de Midilli et al., con un alto coeficiente de correlación (R2), fue el mejor para predecir el secado del maíz en capa delgada. Se utilizó la segunda ley de Fick para calcular, con algunas simplificaciones, la difusividad de la humedad, que dio unos valores entre 4,87 × 10–11 – 2.90 y 1,02 × 10–11 – 1.29 m2 s–1 durante la primera y segunda fase de secado de rapidez decreciente, respectivamente. Los valores de la difusividad efectiva de la humedad para el maíz también aumentaron al aumentar la temperatura de entrada del aire. El valor de la energía de activación varió desde un mínimo de 18,57 a un máximo de 50,74 kJ mol–1 entre 50 y 95°C, con condiciones de secado del lecho fijo a fluidizado. El consumo específico de energía (SEC) para secado en capa delgada del grano de maíz húmedo fue entre 0,33 × 106 y 1,52 × 106 kJ kg–1 entre 50 y 95°C, en lecho fluidizado y fijo, respectivamente. Un aumento de la temperatura en la velocidad del aire disminuye el valor de SEC. Es necesario conocer estas propiedades del maíz para diseñar el mejor sistema de secado y para determinar el mejor punto del proceso de secado

Modeling Drying Characteristics of Terebinth Fruit Under Infrared Fluidized Bed Condition

Advantages of infrared fluid bed drying include high heat and mass transfer coefficients, short process time, high quality and low energy consumption. Since heat and mass transfer and quality changes during drying of terebinth fruit with infrared fluid bed method is not described in the literature. Goals of this research were study the effects of different infrared drying conditions on the drying kinetic and physical parameters of terebinth fruit. To predict moisture during drying process, five mathematical models were used. Experiments were conducted at different levels of hot air velocity (0.93, 1.76 and 2.6 m/s), temperature (40, 55, and 70°C) and infrared radiation power (500, 1000 and 1500 W). Results showed that Demir et al. model had the best performance for predicting of moisture ratio. Effective moisture diffusivity for terebinth samples (6.2×10-11 to 7.3×10-10 m2/s) was achieved. Activation energy of the samples (44.4 to 59.13 kJ/mol) was computed. Maximum rupture force (118.4 N) was calculated at air velocity of 2.6 m/s, infrared power of 1500 W and air temperature of 70°C. The results proved that in addition to short process time, monitoring of terebinth fruit characteristics such as mechanical properties during drying process can be achieved

Analysis of Microscopic Image Textural Features of Artichoke Leaf Extract Powder Produced from Vacuum Spray Drying

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. Conclusions 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

Recognition of Paddy, Brown Rice and White Rice Cultivars Based on Textural Features of Images and Artificial Neural Network

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

Comparison of Mathematical Modeling, Artificial Neural Networks and Fuzzy Logic for Predicting the Moisture Ratio of Garlic and Shallot in a Fluidized Bed Dryer

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). 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. Conclusions 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