2022
DOI: 10.1002/jsfa.12008
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Recent application of artificial neural network in microwave drying of foods: a mini‐review

Abstract: The microwave-assisted thermal process is a high-efficiency drying method and is promising to be applied in the food industry. However, the prediction of the thermal treatment results from such a dynamic and complicated process can be difficult. Additionally, the determination of the optimal drying parameters, such as drying temperature, microwave power, and drying time for optimized performance can also be hard. Recently, extensive research has been focusing on the use of artificial neural network (ANN) model… Show more

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Cited by 13 publications
(3 citation statements)
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“…Machine learning techniques range from simple paradigms like linear (Pearson, 1896) and logistic regression (Cox, 1958; Gu et al, 2007) or decision trees (Breiman et al, 1984) to more complex techniques such as random forests (Breiman, 2001; Jimenez‐Carvelo et al, 2019), support vector machines (Bahamonde et al, 2007; Platt, 1999) and symbolic regression (Schmidt & Lipson, 2009), and on to artificial neural networks (Hinton, 1990; Yang & Chen, 2022). New developments known as deep learning (Goodfellow et al, 2016) have made it possible to create larger, more effective models, although they usually require vastly more data to be properly trained.…”
Section: Descriptive Brief On Predictive Models and Their Applicationsmentioning
confidence: 99%
“…Machine learning techniques range from simple paradigms like linear (Pearson, 1896) and logistic regression (Cox, 1958; Gu et al, 2007) or decision trees (Breiman et al, 1984) to more complex techniques such as random forests (Breiman, 2001; Jimenez‐Carvelo et al, 2019), support vector machines (Bahamonde et al, 2007; Platt, 1999) and symbolic regression (Schmidt & Lipson, 2009), and on to artificial neural networks (Hinton, 1990; Yang & Chen, 2022). New developments known as deep learning (Goodfellow et al, 2016) have made it possible to create larger, more effective models, although they usually require vastly more data to be properly trained.…”
Section: Descriptive Brief On Predictive Models and Their Applicationsmentioning
confidence: 99%
“…A mini-review by [11] shows that ANNs, in general, are well suited for MC estimation applications utilising microwave drying, and [12] shows that ANNs are well suited in general for foodstuff drying applications. ANNs have also shown to be a good tool for other estimation applications, such as estimating the State-of-Charge of batteries for electric vehicles [13], [14], remaining useful lifetime of batteries [15], breaking pressure [16], solenoid valve remaining useful life-time [17] and nitrogen in wheat leaves [18].…”
Section: Introductionmentioning
confidence: 99%
“…A back propagation artificial neural network (BP‐ANN), comprising a technique that imitates data processing in a way similar to the biological nervous system, consists of a three‐layer structure: input, hidden, and output layers 15,16 . BP‐ANN can build predictive model from previous experimental data after a limited number of iterations 17 .…”
Section: Introductionmentioning
confidence: 99%