Food Supply Chains as Cyber-Physical Systems: a Path for More Sustainable Personalized Nutrition

Current food system evolved in a great degree because of the development of processing and food engineering technologies: people learned to bake bread long before the advent of agriculture; salting and smoking supported nomad lifestyles; canning allowed for longer military marches; etc. Food processing technologies went through evolution and significant optimization and currently rely on minor fraction of energy comparing with initial prototypes. Emerging processing technologies (high-pressure, pulsed electric fields, ohmic heating, ultrasound) and novel food systems (cultured biomass, 3-D bioprinting, cyber-physical chains) try to challenge the existing chains by developing potentially more nutritious and sustainable food solutions. However, new food systems rely on low technology readiness levels and estimation of their potential future benefits or drawbacks is a complex task mostly due to the lack of integrated data. The research is aimed for the development of conceptual guidelines of food production system structuring as cyber-physical systems. The study indicates that cyber-physical nature of modern food is a key for the engineering of more nutritious and sustainable paths for novel food systems. Implementation of machine learning methods for the collection, integration, and analysis of data associated with biomass production and processing on different levels from molecular to global, leads to the precise analysis of food systems and estimation of upscaling benefits, as well as possible negative rebound effects associated with societal attitude. Moreover, such data-integrated assessment systems allow transparency of chains, integration of nutritional and environmental properties, and construction of personalized nutrition technologies.

[1]  Jay Lee,et al.  A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems , 2015 .

[2]  Arvind Kumar,et al.  Real Time Monitoring of Valeriana Jatamansi Plant for Growth Analysis , 2018 .

[3]  Benjamin C. M. Fung,et al.  Security and privacy challenges in smart cities , 2018 .

[4]  Aidong Yang,et al.  Designing integrated local production systems: A study on the food-energy-water nexus , 2016 .

[5]  Rajkumar Buyya,et al.  IoT Based Agriculture as a Cloud and Big Data Service: The Beginning of Digital India , 2017, J. Organ. End User Comput..

[6]  By Radha Poovendran Cyber – Physical Systems : Close Encounters Between Two Parallel Worlds , 2010 .

[7]  J. Wolfert,et al.  Internet of Food and Farm 2020 , 2016 .

[8]  Alireza Sahami Shirazi,et al.  Augmenting food with information , 2015, MUM.

[9]  P. M. Ortigosa,et al.  High-performance computing for the optimization of high-pressure thermal treatments in food industry , 2018, The Journal of Supercomputing.

[10]  Markus Schmid,et al.  Intelligent Packaging in the Food Sector: A Brief Overview , 2019, Foods.

[11]  Juana López Redondo,et al.  Preference-based multi-objectivization applied to decision support for High-Pressure Thermal processes in food treatment , 2019, Appl. Soft Comput..

[12]  S. Fawcett,et al.  Data Science, Predictive Analytics, and Big Data: A Revolution that Will Transform Supply Chain Design and Management , 2013 .

[13]  J. Wolfert,et al.  Virtualization of food supply chains with the internet of things , 2016 .

[14]  I. Arvanitoyannis Novel Food Processing Technologies , 2006 .

[15]  Chandan Kumar,et al.  Food structure: Its formation and relationships with other properties , 2017, Critical reviews in food science and nutrition.

[16]  Ratnesh Kumar,et al.  Agricultural Cyber-Physical System: In-Situ Soil Moisture and Salinity Estimation by Dielectric Mixing , 2018, IEEE Access.

[17]  Barr and Feigenbaum Edward A. Avron,et al.  The Handbook of Artificial Intelligence , 1981 .

[18]  Jesse S. Jin,et al.  A Smart Fridge with an Ability to Enhance Health and Enable Better Nutrition , 2009 .

[19]  G. Lanzani Materials for bioelectronics: organic electronics meets biology. , 2014, Nature materials.

[20]  Andrew Y. C. Nee,et al.  Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison , 2019, Engineering.

[21]  Insup Lee,et al.  Cyber-physical systems: The next computing revolution , 2010, Design Automation Conference.

[22]  Soheyl Khalilpourazari,et al.  Optimization of time, cost and surface roughness in grinding process using a robust multi-objective dragonfly algorithm , 2018, Neural Computing and Applications.

[23]  Sylvio Barbon Junior,et al.  Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble , 2019, Sensors.

[24]  I. Djekić,et al.  Impact of Novel Nonthermal Processing on Food Quality: Sustainability, Modelling, and Negative Aspects , 2019, Journal of Food Quality.

[25]  J. Tour,et al.  Laser-Induced Graphene by Multiple Lasing: Toward Electronics on Cloth, Paper, and Food. , 2018, ACS nano.

[26]  L. Manning,et al.  Use of intelligent applications to reduce household food waste , 2020, Critical reviews in food science and nutrition.

[27]  Xinyue Deng,et al.  Multi-Objective Location of Fresh Food E-Commerce Distribution Network Based on Improved NSGA-II Algorithm , 2020 .

[28]  Fumiya Iida,et al.  Improving Robotic Cooking Using Batch Bayesian Optimization , 2020, IEEE Robotics and Automation Letters.

[29]  E. Lutton,et al.  Multi-Criteria Reverse Engineering for Food: Genesis and Ongoing Advances , 2019, Food Engineering Reviews.

[30]  Jing Wang,et al.  Food safety pre-warning system based on data mining for a sustainable food supply chain , 2017 .

[31]  Ming-Ju Chen,et al.  Sequential Quadratic Programming for Development of a New Probiotic Dairy Tofu with Glucono-δ-Lactone , 2006 .

[32]  Ramón Martínez-Máñez,et al.  Recent advances on intelligent packaging as tools to reduce FOOD waste , 2018 .

[33]  Jamshed Iqbal,et al.  Prospects of robotics in food industry , 2017 .

[34]  Monika Arora,et al.  Computer vision based method for identification of freshness in mushrooms , 2019, 2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT).

[35]  Eva Balsa-Canto,et al.  Quality and Safety Models and Optimization as Part of Computer-Integrated Manufacturing , 2008 .

[36]  Rui-Yang Chen Intelligent Predictive Food Traceability Cyber Physical System in Agriculture Food Supply Chain , 2018 .

[37]  Stuart K. Johnson,et al.  Active and intelligent packaging in meat industry , 2017 .

[38]  Mohammad Ali Sahari,et al.  Practical modeling and optimization of ultrasound-assisted bleaching of olive oil using hybrid artificial neural network-genetic algorithm technique , 2017, Comput. Electron. Agric..

[39]  Jamshed Iqbal,et al.  Towards realizing robotic potential in future intelligent food manufacturing systems , 2018, Innovative Food Science & Emerging Technologies.

[40]  Rolf Steinhilper,et al.  The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0☆ , 2017 .

[41]  Honghui Fan,et al.  The Design of the Internet of Things Solution for Food Supply Chain , 2015 .

[42]  Wayne H. Wolf,et al.  Cyber-physical Systems , 2009, Computer.

[43]  Ilango Paramasivam,et al.  The Impact of Wireless Sensor Network in the Field of Precision Agriculture: A Review , 2017, Wireless Personal Communications.

[44]  Tom Fawcett,et al.  Data Science and its Relationship to Big Data and Data-Driven Decision Making , 2013, Big Data.

[45]  Claudio Soriente,et al.  On the difficulty of software-based attestation of embedded devices , 2009, CCS.

[46]  Weishan Zhang,et al.  Multi-source data fusion using deep learning for smart refrigerators , 2018, Comput. Ind..

[47]  D. Knorr,et al.  Emerging technologies in food processing. , 2011, Annual review of food science and technology.

[48]  Naresh Kumar,et al.  Wireless sensor networks for greenhouses: An end-to-end review , 2019, Comput. Electron. Agric..

[49]  R. Manzini,et al.  An application of collaborative robots in a food production facility , 2019, Procedia Manufacturing.

[50]  Panganamala Ramana Kumar,et al.  Cyber–Physical Systems: A Perspective at the Centennial , 2012, Proceedings of the IEEE.

[51]  Sergiy Smetana,et al.  Neural network, blockchain, and modular complex system: The evolution of cyber-physical systems for material flow analysis and life cycle assessment , 2018, Resources, Conservation and Recycling.

[52]  Erwin Rauch,et al.  Industry 4.0 as an enabler of proximity for construction supply chains: A systematic literature review , 2018, Comput. Ind..

[53]  Lav R. Khot,et al.  A Real-Time Weed Mapping and Precision Herbicide Spraying System for Row Crops , 2018, Sensors.

[54]  Melnned M. Kantardzic Big Data Analytics , 2013, Lecture Notes in Computer Science.

[55]  Pieter J. Mosterman,et al.  Industry 4.0 as a Cyber-Physical System study , 2016, Software & Systems Modeling.

[56]  Shimon Y. Nof,et al.  Agricultural cyber physical system collaboration for greenhouse stress management , 2018, Comput. Electron. Agric..

[57]  Rahul Singh Chowhan,et al.  Sustainable Smart Farming for Masses Using Modern Ways of Internet of Things (IoT) Into Agriculture , 2019, Smart Devices, Applications, and Protocols for the IoT.

[58]  Rui-Yang Chen,et al.  Intelligent Predictive Food Traceability Cyber Physical System in Agriculture Food Supply Chain , 2017, 2017 5th International Conference on Mechanical, Automotive and Materials Engineering (CMAME).

[59]  C. Anandharamakrishnan,et al.  Intelligent packaging: Trends and applications in food systems , 2019, Trends in Food Science & Technology.

[60]  Feng Tian,et al.  A supply chain traceability system for food safety based on HACCP, blockchain & Internet of things , 2017, 2017 International Conference on Service Systems and Service Management.

[61]  Radha Poovendran,et al.  Cyber-Physical Systems: Close Encounters Between Two Parallel Worlds [Point of View] , 2010, Proc. IEEE.

[62]  Marija Bogataj,et al.  Mitigating risks of perishable products in the cyber-physical systems based on the extended MRP model , 2017 .

[63]  Fei Tao,et al.  Digital twin-driven product design, manufacturing and service with big data , 2017, The International Journal of Advanced Manufacturing Technology.

[64]  Christopher Brewster,et al.  IoT in Agriculture: Designing a Europe-Wide Large-Scale Pilot , 2017, IEEE Communications Magazine.

[65]  Anushree Priyadarshini,et al.  Emerging food processing technologies and factors impacting their industrial adoption , 2018, Critical reviews in food science and nutrition.

[66]  Rasim M. Alguliyev,et al.  Cyber-physical systems and their security issues , 2018, Comput. Ind..

[67]  A. Selmani,et al.  Agricultural cyber-physical system enabled for remote management of solar-powered precision irrigation , 2019, Biosystems Engineering.

[68]  Lida Xu,et al.  Big data for cyber physical systems in industry 4.0: a survey , 2019, Enterp. Inf. Syst..

[69]  Berend Denkena,et al.  Mechatronic Systems for Machine Tools , 2007 .

[70]  Thijs Defraeye,et al.  Advanced computational modelling for drying processes – A review , 2014 .

[71]  Gangyan Xu,et al.  IoT-based tracking and tracing platform for prepackaged food supply chain , 2017, Ind. Manag. Data Syst..

[72]  Mehmet C. Vuran,et al.  Internet of underground things in precision agriculture: Architecture and technology aspects , 2018, Ad Hoc Networks.

[73]  Yong He,et al.  A feature-selection algorithm based on Support Vector Machine-Multiclass for hyperspectral visible spectral analysis , 2013 .

[74]  Mehmet Can Vuran,et al.  (CPS)^2: integration of center pivot systems with wireless underground sensor networks for autonomous precision agriculture , 2010, ICCPS '10.

[75]  Sophie Martin,et al.  Some remarks on computational approaches towards sustainable complex agri-food systems , 2016 .

[76]  Bernard De Baets,et al.  The digitization of a food packages life cycle , 2017 .

[77]  Borja Bordel,et al.  Cyber-physical systems: Extending pervasive sensing from control theory to the Internet of Things , 2017, Pervasive Mob. Comput..

[78]  A. Thomsen,et al.  Algorithms for sensor-based redistribution of nitrogen fertilizer in winter wheat , 2006, Precision Agriculture.

[79]  Vandung Nguyen,et al.  Smart Agriculture Using IoT Multi-Sensors: A Novel Watering Management System , 2019, J. Sens. Actuator Networks.

[80]  Tao Yu,et al.  Reliable multi-objective optimization of high-speed WEDM process based on Gaussian process regression , 2008 .

[81]  J. Wolfert,et al.  Organizing information integration in agri-food: A method based on a service-oriented architecture and living lab approach , 2010 .

[82]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[83]  Jihong Yan,et al.  An intralogistics-oriented Cyber-Physical System for workshop in the context of Industry 4.0 , 2019, Procedia Manufacturing.

[84]  Jay Lee,et al.  Recent advances and trends in predictive manufacturing systems in big data environment , 2013 .

[85]  R. P. Hutabarat,et al.  Identification of Anthocyanins and Optimization of Their Extraction from Rabbiteye Blueberry Fruits in Nanjing , 2019, Journal of Food Quality.

[86]  Xia Sun,et al.  State-of-the-Art Internet of Things in Protected Agriculture , 2019, Sensors.

[87]  Paul K. Wright,et al.  Cyber-physical product manufacturing , 2014 .

[88]  Rui-Yang Chen,et al.  An intelligent value stream-based approach to collaboration of food traceability cyber physical system by fog computing , 2017 .

[89]  Bo Yan,et al.  Information sharing in supply chain of agricultural products based on the Internet of Things , 2016, Ind. Manag. Data Syst..

[90]  H. Duan,et al.  Food‐Based Edible and Nutritive Electronics , 2017 .

[91]  Ashraf Darwish,et al.  Cyber physical systems design, methodology, and integration: the current status and future outlook , 2017, Journal of Ambient Intelligence and Humanized Computing.

[92]  Hajo A. Reijers,et al.  A control model for object virtualization in supply chain management , 2015, Comput. Ind..

[93]  M. Fischer,et al.  Blockchain and more - Algorithm driven food traceability , 2019, Food Control.

[94]  H. Penny Nii,et al.  The Handbook of Artificial Intelligence , 1982 .

[95]  Yongqi Ge,et al.  Precision Regulation Model of Water and Fertilizer for Alfalfa Based on Agriculture Cyber-Physical System , 2020, IEEE Access.

[96]  Henk Corporaal,et al.  Embedded System Design , 2006 .

[97]  Manoj Kumar Tiwari,et al.  Next generation smart manufacturing and service systems using big data analytics , 2019, Comput. Ind. Eng..

[98]  L. Aiello,et al.  The Expensive-Tissue Hypothesis: The Brain and the Digestive System in Human and Primate Evolution , 1995, Current Anthropology.

[99]  Hasan Smajic,et al.  Remote Control of Large Manufacturing Plants Using Core Elements of Industry 4.0 , 2017, REV.

[100]  M. Ghasemi-Varnamkhasti,et al.  Aging discrimination of French cheese types based on the optimization of an electronic nose using multivariate computational approaches combined with response surface method (RSM) , 2019, LWT.

[101]  Fei Tao,et al.  Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison , 2018, IEEE Access.

[102]  Stefan Boschert,et al.  Digital Twin—The Simulation Aspect , 2016 .

[103]  Wenwen Xu,et al.  Food‐Materials‐Based Edible Supercapacitors , 2016 .

[104]  Bogdan-Constantin Pirvu,et al.  Engineering insights from an anthropocentric cyber-physical system: A case study for an assembly station , 2016 .

[105]  Giyoung Kim,et al.  Detection of produce residues on processing equipment surfaces using fluorescence imaging , 2019, Defense + Commercial Sensing.

[106]  Bernard De Baets,et al.  The digitization of a food package's life cycle: Existing and emerging computer systems in the pre-logistics phase , 2017, Comput. Ind..

[107]  Soundar R. T. Kumara,et al.  Cyber-physical systems in manufacturing , 2016 .

[108]  Ciprian-Radu Rad,et al.  Smart Monitoring of Potato Crop: A Cyber-Physical System Architecture Model in the Field of Precision Agriculture , 2015 .

[109]  J. Simal-Gandara,et al.  Future challenges on the use of blockchain for food traceability analysis , 2018, TrAC Trends in Analytical Chemistry.

[110]  N. Short,et al.  Big Data Analysis for Sustainable Agriculture on a Geospatial Cloud Framework , 2019, Front. Sustain. Food Syst..

[111]  Syed Mahfuzul Aziz,et al.  Review of Cyber-Physical System in Healthcare , 2014, Int. J. Distributed Sens. Networks.

[112]  Kwok-Yan Lam,et al.  Wireless Communication and Security Issues for Cyber–Physical Systems and the Internet-of-Things , 2018, Proceedings of the IEEE.

[113]  Michael C. McAlpine,et al.  Silk‐Based Conformal, Adhesive, Edible Food Sensors , 2012, Advanced materials.

[114]  Gursel Alici,et al.  3D printing Vegemite and Marmite: Redefining “breadboards” , 2018 .

[115]  Kaiser Younis,et al.  Comparison of Gaussian process regression, artificial neural network, and response surface methodology modeling approaches for predicting drying time of mosambi ( Citrus limetta ) peel , 2018, Journal of Food Process Engineering.

[116]  Sachchidanand Singh,et al.  Big Data analytics , 2012 .

[117]  BuyyaRajkumar,et al.  IoT Based Agriculture as a Cloud and Big Data Service , 2017 .

[118]  Federico Pallottino,et al.  A Review on blockchain applications in the agri-food sector. , 2019, Journal of the science of food and agriculture.

[119]  P. Verboven,et al.  Digital twins of food process operations: the next step for food process models? , 2020 .

[120]  Frank Vahid,et al.  A Survey on Concepts, Applications, and Challenges in Cyber-Physical Systems , 2014, KSII Trans. Internet Inf. Syst..

[121]  Viacheslav I. Adamchuk,et al.  Agriculture Cyber-Physical Systems , 2017 .

[122]  Catherine Bonazzi,et al.  Computer-aided process engineering for environmental efficiency: Industrial drying of biomass , 2016 .

[123]  De BaetsBernard,et al.  The digitization of a food packages life cycle , 2017 .

[124]  David Garlan,et al.  DARTSim: An Exemplar for Evaluation and Comparison of Self-Adaptation Approaches for Smart Cyber-Physical Systems , 2019, 2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS).

[125]  Alessandro Fanti,et al.  Study and Design of a Wireless Sensors Network for the Optimization of Bread Manufacturing Process , 2018, 2018 26th Telecommunications Forum (TELFOR).

[126]  Patrice Buche,et al.  Expertise-based decision support for managing food quality in agri-food companies , 2019, Comput. Electron. Agric..

[127]  Martin C. Hartel,et al.  Edible Electrochemistry: Food Materials Based Electrochemical Sensors , 2017, Advanced healthcare materials.

[128]  Mehran Abolhasan,et al.  Frost Monitoring Cyber–Physical System: A Survey on Prediction and Active Protection Methods , 2020, IEEE Internet of Things Journal.

[129]  S. Smetana,et al.  A Path From Sustainable Nutrition to Nutritional Sustainability of Complex Food Systems , 2019, Front. Nutr..

[130]  S. Smetana,et al.  Nutritional Sustainability Inside–Marketing Sustainability as an Inherent Ingredient , 2019, Front. Nutr..

[131]  Henry Jaeger,et al.  DRYING TECHNOLOGIES IN FOOD PROCESSING , 2014 .

[132]  Marcus Hardie,et al.  Underground Wireless Data Transmission Using 433-MHz LoRa for Agriculture , 2019, Sensors.

[133]  Bernard De Baets,et al.  The digitization of a food package's life cycle: Existing and emerging computer systems in the logistics and post-logistics phase , 2017, Comput. Ind..

[134]  Fengjun Li,et al.  Cyber-Physical Systems Security—A Survey , 2017, IEEE Internet of Things Journal.