Modelling of the Selected Physical Properties of the Fava Bean with Various Moisture Contents UsingFuzzy Logic Design

Abstract

The current paper indicates the systematic determination of the optimal conditions for the selected physical properties of the fava bean. The effects of varying moisture content of the Barkat fava bean grown in Golestan, Iran, in the range of 9.3-31.3% (Input) on the 15 selected physical properties of the crop, including geometric values as such length; width; thickness; arithmetic and geometric mean diameter; sphericity index surface and the area of the image; gravity and frictional parameters like the weight of 1000 seeds; true density; bulk density; volume and porosity as well as friction (filling and vacating angle stability) as the outputs were predicted. Afterwards, a model relying on fuzzy logic for the prediction of the 15 outputs had been presented. To build the model, training and testing using experimental results from the Barkat fava bean were conducted. The data used as the input of the fuzzy logic model are arranged in a format of one input parameter that covers the percentage of the moisture contents of the beans. In relation to the varying moisture content (input), the outcomes (15 physical parameters) were predicted. The correlation coefficients obtained between the experimental and predicted outputs as well as the Mean Standard Deviation indicated the competence of fuzzy logic design in predicting the selected physical properties of fava bean seeds. PRACTICAL APPLICATIONToday, because of the high demand for crops to be used extensively in the human diet, enhancements in the efficiency of the processing are getting more attention. In this way, finding and/or the determination of the optimal conditions for processing with minimum waste looks very substantial. Therefore, the use of prediction methods in food processing is considered to be a tool for improving the efficiency and the quality of the produced products. In this regard, the fuzzy logic design as a novel prediction tool, along with response surface methodology (RSM) and Artificial Neural Network (ANN), are applied extensively. Therefore Fuzzy Logic Design is optimized to predict the some of the selected physical properties of fava bean, as a function of seed's moisture content. Therefore predicting the behavior of this crop against different moisture contents can improve the quality and performance of the products with the minimum wastes during very short time

Modeling of Linear and Non-linear Compression Processes of Sunflower Bulk Oilseeds

“…In the literature, it is obvious that many models have been applied extensively on the modeling of oilseeds or food processing engineering aimed at understanding the aerodynamics and biophysical or physical properties as well as optimizing the processing parameters. Some of these models include response surface methodology, artificial neural network, adaptive neuro-fuzzy inference system, fuzzy logic design [43][44][45][46][47][48][49]. However, the tangent curve mathematical model applied in this present study and previously published studies show reliability for describing the linear and non-linear compression processes of bulk oilseeds based on the experimental or model boundary conditions [35][36][37].…”

Fuzzy Knowledge-Based Model for Prediction of the Terminal Velocities of the Chickpea and Dry Bean Seeds

“…Fuzzy set theory has been applied to a wide range of applications, such as process control, management, economic decision-making, operations research, event classification and image processing. Farzaneh et al, (2017) used a fuzzy system to predict physical properties of the fava bean seeds including geometric values as such length; width; thickness; arithmetic and geometric mean diameter; sphericity index surface and the area of the image. Yang et al (2005) showed the feasibility of image processing and fuzzy logic control in the development of a precision farming herbicide application system.…”

“…The correlation coefficients of relationships were found 0.809 for chickpea and 0.826 for dry bean. Farzaneh et al, (2017) studied with fuzzy logic system to predict an extensive range of physical properties of fava beans in the selected moisture contents of the input seeds (9.3-31.3 %). The high correlation coefficient value (0.999) between experimental and predicted values by fuzzy logic was found.…”

Flavor quality evaluation system of Xinjiang milk knots by using SOM neural network and the fuzzy AHP

“…The implementation of the ANN allowed the quality of the sample to be distinguished at 100% efficiency in the prediction of the beverage under study. On the other hand, Farzaneh's studies show that the ANN method not only has many applications in food research to solve different directions’ problems, but also has the characteristics of fast convergence, good effect, and low cost (Dolatabadi et al, 2016; Farzaneh, Bakhshabadi, et al, 2017; Farzaneh & Carvalho, 2017; Farzaneh, Ghodsvali, et al, 2017; Jabrayili et al, 2016; Moghimi, Farzaneh, & Bakhshabadi, 2018; Rostami, Farzaneh, Boujmehrani, Mohammadi, & Hamid, 2014).…”