Comprehensive study on applications of artificial neural network in food process modeling

Raj, G. V. S. Bhagya; Dash, Kshirod Kumar

doi:10.1080/10408398.2020.1858398

Cited by 65 publications

(29 citation statements)

References 146 publications

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“…ANN can be easily used to establish a nonlinear relationship between independent and dependent variables. It was found that ANN has better prediction compared to RSM and possessed a greater ability to model non‐linear problems (Bhagya Raj & Dash, 2020). The frequently used supervised learning algorithm: Multilayer perception (MLP) was used along with a back‐propagation algorithm to train the neural network to develop a predictive model for mapping layers of input and output variables.…”

Section: Predictive Modeling and Optimization Methodsmentioning

confidence: 99%

Ultrasonic modulated osmotic dehydration of Aonla ( Phyllanthus emblica L.) slices: An integrated modeling through ANN, GPR, and RSM

Singh

Mehta

Singh

et al. 2021

Food Processing Preservation

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This study was aimed to explore the effect of ultrasonic treatment on osmotic dehydration of aonla slices. The Box–Behnken design (BBD) was utilized to design the experiment to study the effect of three process variables: ultrasonication time of 10–30 min, ultrasonication temperature of 30–50°C, and sugar concentration of 40–60°Bx. Artificial neural network (ANN), Gaussian process regression (GPR), and response surface methodology (RSM) (in terms of a second‐order polynomial) were used to predict the mass transfer parameters (water loss, solid gain, and weight reduction), shrinkage ratio and color changes during osmotic dehydration of aonla slices. The predictive capabilities of the ANN, GPR, and RSM were compared by means of mean square error (RMSE), mean absolute error (MAE), average absolute deviation (AAD), standard error of prediction (SEP), chi‐square statistic and coefficient of determination (R2). Results showed that the ANN model was found to be a more accurate predictor than the other models for all responses except shrinkage ratio where the GPR model was found to be better in predicting model. RSM also suggested the optimum processing conditions leading to maximizing water loss and weight reduction and minimizing solid gain to be at ultrasonication time of 10 min, ultrasonication temperature of 30°C, and sugar concentration of 60°Bx. Novelty impact statement Ultrasonic pretreated Aonla slices were found to have improved mass transfer dynamics during osmotic dehydration. Integrated Process modeling was explored and ANN was found to be a better process model compared to GPR and RSM. RSM predicted the optimum process parameters such as pretreatment time: 10 min, temperature: 30°C and sugar concentration: 60°Bx.

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Section: Predictive Modeling and Optimization Methodsmentioning

confidence: 99%

Ultrasonic modulated osmotic dehydration of Aonla ( Phyllanthus emblica L.) slices: An integrated modeling through ANN, GPR, and RSM

Singh

Mehta

Singh

et al. 2021

Food Processing Preservation

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show abstract

“…The main challenge to produce extrusionbased food products is to control the quality parameters. The quality of extruded products depends on the feed material properties like moisture content, protein concentration, pH value of the feed material, and the extruder parameters like feed rate, extruder length, screw geome-try, screw speed, and the temperature of the barrel (Bhagya Raj & Dash, 2020). The development of a physics-based method for addressing the effects of all these influencing parameters on quality attributes of extruded products has not been attempted yet due to the extreme difficulty of developing such a theoretical model.…”

Section: In Food Extrusion Processesmentioning

confidence: 99%

“…Encapsulation can be defined as the process of incorporation of food ingredients, enzymes, cells, or other materials in a small capsule. Modeling the encapsulation process involves modeling encapsulation efficiency (EE) and functional properties such as total phenolic content, and the antioxidant activity of the active agent in the encapsulated food (Bhagya Raj & Dash, 2020). An ultrasoundassisted liposome encapsulation process was modeled and optimized using the ANN and RSM methods for optimizing the EE (E.E%), particle size, and absolute loading (AL) (S. -M. Huang et al, 2017).…”

Section: In Other Food Processing Applicationsmentioning

confidence: 99%

Machine learning‐based modeling in food processing applications: State of the art

Khan

Sablani

Nayak

et al. 2022

Comp Rev Food Sci Food Safe

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Food processing is a complex, multifaceted problem that requires substantial human interaction to optimize the various process parameters to minimize energy consumption and ensure better‐quality products. The development of a machine learning (ML)‐based approach to food processing applications is an exciting and innovative idea for optimizing process parameters and process kinetics to reduce energy consumption, processing time, and ensure better‐quality products; however, developing such a novel approach requires significant scientific effort. This paper presents and evaluates ML‐based approaches to various food processing operations such as drying, frying, baking, canning, extrusion, encapsulation, and fermentation to predict process kinetics. A step‐by‐step procedure to develop an ML‐based model and its practical implementation is presented. The key challenges of neural network training and testing algorithms and their limitations are discussed to assist readers in selecting algorithms for solving problems specific to food processing. In addition, this paper presents the potential and challenges of applying ML‐based techniques to hybrid food processing operations. The potential of physics‐informed ML modeling techniques for food processing applications and their strategies is also discussed. It is expected that the potential information of this paper will be valuable in advancing the ML‐based technology for food processing applications.

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“…1 The production of huge volumes of multidisciplinary and heterogeneous food data (e.g., nutrition composition table, health databases, food images, food ordering data, and recipes) from the food system provides a basis for the development of artificial intelligence (AI), making digital technology an indispensable part of food science and industry. Each stage from food processing to consuming in this system can be replaced with data-driven computational methods to prompt the development of food science and industry, such as the use of neural networks in modeling the food process, 2 , 3 food quality assessment, 4 food object recognition and analysis, 5 , 6 , 7 food authentication and traceability, 8 and dietary assessment. 9 , 10 …”

Section: Introductionmentioning

confidence: 99%

Applications of knowledge graphs for food science and industry

Min

et al. 2022

Patterns

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Comprehensive study on applications of artificial neural network in food process modeling

Cited by 65 publications

References 146 publications

Ultrasonic modulated osmotic dehydration of Aonla ( Phyllanthus emblica L.) slices: An integrated modeling through ANN, GPR, and RSM

Ultrasonic modulated osmotic dehydration of Aonla ( Phyllanthus emblica L.) slices: An integrated modeling through ANN, GPR, and RSM

Machine learning‐based modeling in food processing applications: State of the art

Applications of knowledge graphs for food science and industry

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