Application of Artificial Intelligence in Food Industry—a Guideline

Mavani, Nidhi Rajesh; Ali, Jarinah Mohd; Othman, Suhaili; Hussain, Mohd Azlan; Hashim, Habibah; Rahman, Norliza Abd

doi:10.1007/s12393-021-09290-z

Cited by 95 publications

(50 citation statements)

References 249 publications

(87 reference statements)

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“…In contrast with the mathematical models, ANNs and ANFIS models are able to identify the relationship between parameters without the need to extract the relationship between them. Thus, they can be regarded as powerful modeling tools (Mavani et al, 2021). Numerous studies have employed ANN and ANFIS to predict the thermodynamic parameters of drying agricultural crops using different dryers.…”

Section: Introductionmentioning

confidence: 99%

Comparison of two artificial intelligence methods ( ANNs and ANFIS ) for estimating the energy and exergy of drying cantaloupe in a hybrid infrared‐convective dryer

Zadhossein

Abbaspour‐Gilandeh

Kaveh³

et al. 2022

Food Processing Preservation

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In this investigation, energy and exergy analysis of a hybrid infrared-convective (IR-CV) dryer is presented for drying cantaloupe slices. The experiments were performed at three temperature levels (40, 55, and 70°C), one level of air velocity (0.5 m/s), and three IR powers (250, 500, and 750 W). The relationships between the input process parameters (IR power, input air temperature, and drying time) and the thermodynamic properties of the dried product (moisture ratio, drying rate, energy efficiency, exergy efficiency, and exergy loss) were modeled by implementing the artificial neural network (ANN) and ANFIS. Results indicated that high IR power and air temperature can shorten the drying time, meanwhile increasing the energy efficiency. Based on the obtained results, input air temperature and IR power highly affect the exergy efficiency.The highest exergy efficiency was obtained at the input air temperature of 70°C and IR power of 750 W. The exergy loss was increased by increasing both parameters of the air temperature and IR power. Models developed using ANN and ANFIS indicated that the ANFIS model predicted the moisture ratio, energy efficiency, and exergy loss better than the ANN model, as it estimated these thermodynamic parameters at a higher regression coefficient (>0.9889) than ANN (0.9850), while, the accuracy of ANN model for predicting drying rate and exergy efficiency was better than ANFIS. Novelty impact statement:In this study, the effects of both different infrared powers (250, 500, and 750 W) and air temperature (40, 55, and 70°C) on drying kinetics, energy, and exergy of cantaloupe slices during the drying process were investigated. The prediction model of cantaloupe slice moisture ratio, energy, and exergy in the infrared hot air drying process was established based on artificial neural network and adaptive neuro-fuzzy inference system. Artificial neural network and adaptive neuro-fuzzy inference system model 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%

Comparison of two artificial intelligence methods ( ANNs and ANFIS ) for estimating the energy and exergy of drying cantaloupe in a hybrid infrared‐convective dryer

Zadhossein

Abbaspour‐Gilandeh

Kaveh³

et al. 2022

Food Processing Preservation

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“…Researchers in biology and food engineering have developed several methods (fuzzy logic methods, artificial neural networks, genetic algorithms, decision trees, and supporting vector machines) to study the complex characteristics of many products while adopting cost-effective measures and satisfying production constraints and consumer expectations [93]. Research on the combination of neural networks with other methods in food processing and preservation is rapidly developed [94], [95], [96], [97], [98].…”

Section: Discussionmentioning

confidence: 99%

Current Trends in Intelligent Control Neural Networks for Thermal Processing (Foods): Systematic Literature Review

Marfuah

Ulya

Kusudaryati

et al. 2022

Journal of Robotics and Control

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Thermal processing is a technique for sterilizing foods through heating at high temperatures. Thermal processing plays a significant role in preserving foods economically, efficiently, reliably, and safely. Control in thermal processing of foods is necessary to avoid any decrease in food quality, i.e., color change, reduced content, sensory quality, and nutrition. Artificial Neural Network (ANN) has been developed as a computing method in research and developments on thermal processing methods to discover one suitable for food processing without damaging food quality. To this date, ANN has been used in food industries for modeling many processes. The paper aims to identify the latest trend in intelligent neural network control for the thermal processing of foods. The paper conducted a systematic literature review with five research questions using Preferred Reporting Items for Systematic Review (PRISMA). According to screening results and article selection, 240 potential articles have fulfilled the inclusion criteria. Then, each article was explored to identify the advantage and the advance of intelligent network control in thermal food processing. It can be concluded that the technology in information and computations of food processing has rapidly developed and advanced through the utilization of a combination of ANN with fuzzy logic and/or genetic algorithms.

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“…ML and AI have become integral parts of all these control concepts, and successful use-cases have been reported by several branches of the food industry (Tajammal Munir et al 2015;Kondakci & Zhou 2017;Jerome & Singh 2019;Khadir 2021;Mavani et al 2021;Macdonald 2021). Apart from optimizing the process and product, a similar methodology can monitor the processing equipment, leading to concepts such as predictive maintenance (Dalzochio et al 2020).…”

Section: Food Domain Challenges Solved Using Data and Aimentioning

confidence: 99%

The fourth industrial revolution in the food industry—Part I: Industry 4.0 technologies

Hassoun¹,

Aït‐Kaddour

Abu‐Mahfouz

et al. 2022

Critical Reviews in Food Science and Nutrition

116

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Climate change, the growth in world population, high levels of food waste and food loss, and the risk of new disease or pandemic outbreaks are examples of the many challenges that threaten future food sustainability and the security of the planet and urgently need to be addressed. The fourth industrial revolution, or Industry 4.0, has been gaining momentum since 2015, being a significant driver for sustainable development and a successful catalyst to tackle critical global challenges. This review paper summarizes the most relevant food Industry 4.0 technologies including, among others, digital technologies (e.g., artificial intelligence, big data analytics, Internet of Things, and blockchain) and emerging technologies (e.g., smart sensors, robotics, digital twins, and cyber-physical systems). Moreover, insights into the new food trends (such as 3D printed foods) that have emerged as a result of the Industry 4.0 technological revolution will also be discussed in Part II of this work.The Industry 4.0 technologies have significantly modified the food industry and led to substantial consequences for the environment, economics, and human health. Despite the importance of each of the technologies mentioned above, ground-breaking sustainable solutions could only emerge by combining many technologies simultaneously. The Food Industry 4.0 era has been characterized by new challenges, opportunities, and trends that have reshaped current strategies and prospects for food production and consumption patterns, paving the way for the move towards Industry 5.0.

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Application of Artificial Intelligence in Food Industry—a Guideline

Cited by 95 publications

References 249 publications

Comparison of two artificial intelligence methods ( ANNs and ANFIS ) for estimating the energy and exergy of drying cantaloupe in a hybrid infrared‐convective dryer

Comparison of two artificial intelligence methods ( ANNs and ANFIS ) for estimating the energy and exergy of drying cantaloupe in a hybrid infrared‐convective dryer

Current Trends in Intelligent Control Neural Networks for Thermal Processing (Foods): Systematic Literature Review

The fourth industrial revolution in the food industry—Part I: Industry 4.0 technologies

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