Comparison between artificial neural networks and mathematical models for moisture ratio estimation in two varieties of green malt

Aghajani, Narjes; Kashaninejad, Mahdi; Dehghani, Amir Ahmad; Garmakhany, Amir Daraei

doi:10.1111/j.1757-837x.2012.00125.x

Cited by 17 publications

(5 citation statements)

References 28 publications

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“…Well‐trained algorithms could be used as a predictive model for special purposes. Artificial neural networks (ANNs) have an exceptional learning ability and capability in definition and modeling of complex non‐linear relationships between input and outputs of a system (Aghajani et al, 2012; Aghbashlo et al, 2015). Just because of modeling capability of non‐linear systems, successful use of such approaches has been reported in drying technologies (Aghbashlo et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Machine learning algorithms to estimate drying characteristics of apples slices dried with different methods

Sağlam

Çetin

2022

Food Processing Preservation

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In this study, three different apple cultivars were dried using five different drying methods and moisture ratio (MR), moisture content (MC) and drying rate values were determined. Then, different machine learning algorithms (artificial neural network, k‐nearest neighbors, random forest, gaussian processes, and support vector regression) were used to estimate MR, MC and drying rate. For MR estimation of Golden Delicious, Oregon Spur and Granny Smith cultivars, Random Forest was most successful algorithm with correlation coefficients (R) of 0.9800, 0.9873, and 0.9841, respectively. This was followed by SVR with R: 0.9323 for Golden Delicious, ANN with R: 0.9766 for Oregon Spur and 5‐NN with R: 0.9827 for Granny Smith. MC and drying rate estimation results showed that RF, SVR, and k‐NN achieved higher R for all cultivars. It was concluded that machine learning algorithms are an effective approach for the accurate estimation of the drying characteristics of apple slices. Practical applications Machine learning‐like precise modeling techniques are used to estimate the drying characteristics of agricultural commodities. Models should be assessed and compared for optimization of drying conditions and operational costs. Machine learning models predictions agreed well with testing data sets and they could be useful for understanding and controlling the factors affecting drying behaviors.

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Section: Introductionmentioning

confidence: 99%

Machine learning algorithms to estimate drying characteristics of apples slices dried with different methods

Sağlam

Çetin

2022

Food Processing Preservation

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“…Although different types of modeling techniques for plant materials are in the literature, kinetic and modeling studies of drying processes are still open to development. Modeling of sorghum soaking behavior and drying characteristics of two different types of green malt with artificial neural network (ANN) and empirical model (Page's model) were carried out by previous studies (Aghajani, Kashaninejad, Dehghani, & Garmakhany, ; Kashiri, Garmakhany, & Dehghani, ). Theoretical, empirical, and neural network models for modeling the drying kinetics of Thymus vulgaris L. were proposed by researchers (Rodriguez, Clemente, Sanjuan & Bon, ).…”

Section: Introductionmentioning

confidence: 99%

Kinetic and artificial neural network modeling techniques to predict the drying kinetics of Mentha spicata L.

Karakaplan

Göz

Tosun

et al. 2019

J Food Process Preserv

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This study presented both the empirical and artificial neural network (ANN) approaches to estimate the moisture content of Mentha spicata. Two different types of drying methods (in shade and in oven (35 and 50°C)) were used to investigate the drying kinetics of the Mentha spicata samples. The effects of drying methods on effective diffusion coefficient, moisture ratio (MR), drying rate, and activation energy were investigated. Moreover, six different thin layer drying models (Page, Diffusion approach, Newton, Modified Henderson, Henderson and Pabis and Pabis and Midilli) and an ANN with feed forward structure were used to define the drying kinetics of these samples. In order to estimate the kinetic model parameters, sequential quadratic programming (SQP) was used. Model performances were evaluated based on the coefficient of determination (R2), root mean square error (RMSE) and mean absolute percentage error (MAPE%) values. In the kinetic part of the modeling study, the Midilli model provided better results than the others. However, the ANN had the best results when a total assessment was made. The effective diffusion coefficient values were found in the range between 1.31 × 10–12 and 4.43 × 10–12 m2/s. The activation energy was obtained as 44.31 kJ/kmol. The R2, MAPE%, and RMSE values for the ANN test data were 1.00, 0.2257, and 5.9447 × 10−4, respectively. In the future, different modeling approaches will be applied to describe this drying process. Practical applications Drying is a process where heat transfer and mass transfer take place together. Modeling is an innovative approach used in evaluation of experimental data and has increasing popularity in recent years. ANNs are a powerful data‐driven method, and they have a very broad area of usage from medicine to engineering issues. Empirical models are another approach for describing experimental data. In this study, these two modeling approaches were used to obtain the MR. Humidity is a condition that needs to be checked in food safety and protection. Therefore, it is very important to ensure control with robust modeling techniques. In this study, the developed ANN model had a high R2 value (R2 = 1.00). This indicated that it may be used successfully in real applications.

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“…Artificial neural networks (ANNs) is a mathematical tool, which tries to represent low‐level intelligence in natural organisms and it is a flexible structure, capable of making a non‐linear mapping between input and output spaces (Rumelhart et al ., ). ANNs have already been applied to simulate processes such as fermentation (Latrille et al ., ), crossflow microfiltration (Dornier et al ., ), and drying behaviour of different food and agricultural materials such as carrot (Erenturk & Erenturk, ), tomato (Movagharnejad & Nikzad, ), ginseng (Martynenko & Yang, ), cassava and mango (Hernandez‐Perez et al ., ), green malt (Aghajani et al ., ), but there is no information about the application of ANNs in the simulation of soaking process (in particular for grain). This study was carried out to test and validate the efficiency of ANNs for simulating the soaking behaviour and the effect of temperature and time on the hydration of sorghum kernel.…”

Section: Introductionmentioning

confidence: 99%

Modelling of sorghum soaking using artificial neural networks (MLP)

Kashiri

Garmakhany

Dehghani

2012

Quality Assurance and Safety of Crops & Foods

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Introduction Artificial neural network is a technique with flexible mathematical structure which is capable of identifying complex non-linear relationship between input and output data. Objectives The aim of this study was evaluation of artificial neural network efficiency for simulating the soaking behaviour of sorghum kernel as a function of temperature and time. Methods In this study, soaking characteristics of sorghum kernel was studied at different temperatures (10, 20, 30, 40 and 50°C) by measuring an increase in the mass of sorghum kernels with respect to time. A multilayer perceptron neural network was used to estimate the moisture ratio of sorghum kernel during soaking at different temperatures and a comparison was also made with the results obtained from Page's model. The soaking temperature and time were used as input parameters and the moisture ratio was used as output parameter. Results Results showed that the estimated moisture ratio by multilayer perceptron neural network is more accurate than Page's model. It was also found that moisture ratio decreased with increasing of soaking time and increased with increasing of soaking temperature. Conclusion The artificial neural network model was more suitable than other models for soaking behaviour estimation in sorghum kernel.Kashiri M, Daraei Garmakhany A, Dehghani AA (2012). Modeling of sorghum soaking using artificial neural networks (MLP). Quality Assurance and Safety of Crops & Foods, 4,[179][180][181][182][183][184]

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Comparison between artificial neural networks and mathematical models for moisture ratio estimation in two varieties of green malt

Cited by 17 publications

References 28 publications

Machine learning algorithms to estimate drying characteristics of apples slices dried with different methods

Machine learning algorithms to estimate drying characteristics of apples slices dried with different methods

Kinetic and artificial neural network modeling techniques to predict the drying kinetics of Mentha spicata L.

Modelling of sorghum soaking using artificial neural networks (MLP)

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