PREDICTION OF MECHANICAL PROPERTIES OF CUMIN SEED USING ARTIFICIAL NEURAL NETWORKS

In this paper, two artificial neural networks (ANNs) are applied to acquire the relationship between the mechanical properties and moisture content of cumin seed, using the data of quasi-static loading test. In establishing these relationship, the moisture content, seed size, loading rate and seed orientation were taken as the inputs of both models. The force and energy required for fracturing of cumin seed, under quasi-static loading were taken as the outputs of two models. The activation function in the output layer of models obeyed a linear output, whereas the activation function in the hidden layers were in the form of a sigmoid function. Adjusting ANN parameters such as learning rate and number of neurons and hidden layers affected the accuracy of force and energy prediction. Comparison of the predicted and experimented data showed that the ANN models used to predict the relationships of mechanical properties of cumin seed have a good learning precision and good generalization, because the root mean square errors of the predicated data by ANNs were rather low (4.6 and 7.7% for the force and energy, respectively).

PRACTICAL APPLICATIONS

Cumin seed is generally used as a food additive in the form of powder for imparting flavor to different food preparations and for a variety of medicinal properties. Physical properties of cumin seeds are essential for the design of equipment for handling, harvesting, aeration, drying, storing, grinding and processing. For powder preparation especially the fracture behavior of the seeds are essential. These properties are affected by numerous factors such as size, form and moisture content of the grain and deformation speed. A neural network model was developed that can be used to predict the relationships of mechanical properties. Artificial neural network models are powerful empirical models approach, which can be compared with mathematical models.     

INFLUENCE OF HYDROXYPROPYL METHYLCELLULOSE ON THE RHEOLOGICAL AND MICROSTRUCTURAL CHARACTERISTICS OF WHOLE WHEAT FLOUR DOUGH AND QUALITY OF PURI

Puri is a traditional unleavened fried product prepared from whole wheat flour. Hydroxypropyl methylcellulose (HPMC) was used to study its effect on rheological characteristics of whole wheat flour dough and puri making quality. Addition of HPMC at 0.5 and 1.0% w/w increased the water absorption and dough stability whereas the resistance to extension and extensibility decreased. Pasting temperature, peak viscosity and cold paste viscosity gradually decreased. The moisture and fat contents of puri increased marginally. Quality parameters and sensory acceptability were monitored after 0 and 8 h of storage. Addition of 0.5% HPMC gave higher sensory scores. Microscopic observations during puri processing showed that the starch granules in the control dough were clearly visible in the protein matrix, which reduced on frying due to partial gelatinization. Microstructure of puri with HPMC showed higher gelatinization of starch. It also helped in moisture retention and hence, resulted in highly pliable and soft-textured puri .

PRACTICAL APPLICATIONS

Puri is a traditional unleavened fried product that is prepared by mixing whole wheat flour and water, sheeted to a desirable thickness and fried. Use of hydroxypropyl methylcellulose (HPMC) affected the whole wheat flour dough and puri making quality. It helped in moisture retention and hence, resulted in highly pliable and soft-textured puri . Microstructure of puri with HPMC showed higher gelatinization of starch.     

Predicting the drying kinetics of salted codfish (Gadus Morhua): semi‐empirical,diffusive and neural network models

This work aims to compare the accuracy of several drying modelling techniques namely semi‐empirical, diffusive and artificial neural network (ANN) models as applied to salted codfish (Gadus Morhua). To this end, sets of experimental data were collected to adjust parameters for the models. Modelling of codfish drying was performed by resorting to Page and Thompson semi‐empirical models and to a Fick diffusion law. The ANN employed a neural network multilayer ‘feed‐forward’, consisting of one input layer, with four neurons, one hidden layer, formed by five neurons and one output layer with a convergence criterion for training purposes. The simulations showed good results for the ANN (correlation coefficient between 0.987 and 0.999) and semi‐empirical models (correlation coefficient ranging from 0.992 to 0.997 for Page’s model, and from 0.993 to 0.996 for Thompson’s model), while improvements were required to obtain better predictions by the diffusion model (correlation coefficients ranged from 0.864 to 0.959).