Industria 4.0 y su relación con la automatización en la industria Alimentaria: Una revisión sistemática y bibliométrica

Autores/as

DOI:

https://doi.org/10.57188/manglar.2025.025

Palabras clave:

Industria 4.0, industria alimentaria, automatización, inteligencia artificial

Resumen

Este artículo de revisión examina las aplicaciones actuales de la automatización en la industria alimentaria y su vínculo con el paradigma de la Industria 4.0, analizando las principales tendencias, beneficios y desafíos asociados con la adopción de tecnologías emergentes. Se realizó una revisión sistemática de la literatura utilizando la base de datos Scopus, aplicando el enfoque PICOC y el protocolo PRISMA, lo que permitió seleccionar 120 estudios relevantes. El análisis bibliométrico, efectuado con VOSviewer y Bibliometrix, respaldó la búsqueda y facilitó la identificación de temáticas consolidadas. Los hallazgos evidencian que la Industria 4.0 ofrece múltiples oportunidades para mejorar la eficiencia, trazabilidad y capacidad de respuesta del sector alimentario, mediante la incorporación de inteligencia artificial, aprendizaje automático, aprendizaje profundo y blockchain. No obstante, su implementación enfrenta barreras como la escasez de personal calificado y la resistencia al cambio organizacional. Se sugiere que futuras investigaciones analicen el impacto de estas transformaciones en el desarrollo de competencias laborales, así como en el diseño de estrategias que permitan superar los desafíos identificados. Además, resulta crucial evaluar cómo estas tecnologías pueden contribuir a una producción más sostenible, optimizando el uso de recursos y reduciendo el impacto ambiental, en concordancia con los Objetivos de Desarrollo Sostenible.

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Referencias

Alqudhaibi, A., Albarrak, M., Jagtap, S., Williams, N., & Salonitis, K. (2025). Securing industry 4.0: Assessing cybersecurity challenges and proposing strategies for manufacturing management. Cyber Security and Applications,3,100067. https://doi.org/10.1016/j.csa.2024.100067

Ahamed, N. N., & Vignesh, R. (2022). Smart agriculture and food industry with blockchain and artificial intelligence. Journal of Computer Science, 18(1), 1-17. https://doi.org/10.3844/jcssp.2022.1.17

Qazi, A. M., Mahmood, S. H., Haleem, A., Bahl, S., Javaid, M., & Gopal, K. (2022). The impact of smart materials, digital twins (DTs) and Internet of things (IoT) in an industry 4.0 integrated automation industry. Materials Today: Proceedings, 62, 18-25. https://doi.org/10.1016/j.matpr.2022.01.387

Akyazi, T., Goti, A., Oyarbide, A., Alberdi, & E., Bayon, F. (2020). A Guide for the Food Industry to Meet the Future Skills Requirements Emerging with Industry4.0.Foods,9(4),492. https://doi.org/10.3390/foods9040492

Alsaedi, A.W.M., Al-Hilphy, A.R., Al-Mousawi, A.J., & Gavahian, M. (2024). Artificial intelligence-based modeling of novel non-thermal milk pasteurization to achieve desirable color and predict quality parameters during storage. Journal of Food Process Engineering, 47(7), e14658. https://doi.org/10.1111/JFPE.14658

Ananias, E., Gaspar, P. D., Soares, V. N., & Caldeira, J. M. (2021). Artificial intelligence decision support system based on artificial neural networks to predict the commercialization time by the evolution of peach quality. Electronics, 10(19), 2394. https://doi.org/10.3390/electronics10192394

Arshad, S., Kazmi, H., Javed, M., & Mohammed, S. (2023). Applicability of machine learning techniques in predicting wheat yield based on remote sensing and climate data in Pakistan, South Asia. European Journal of Agronomy, 1161-0301. https://doi.org/10.1016/j.eja.2023.126837

Attaran, M. (2023). The impact of 5G on the evolution of intelligent automation and industry digitization. Journal of Ambient Intelligence and Humanized Computing, 14, 5977–5993. https://doi.org/10.1007/s12652-020-02521-x

Bader, F., & Rahimifard, S. (2020). A methodology for the selection of industrial robots in food handling. Innovative Food Science & Emerging Technologies, 64,102379. https://doi.org/10.1016/j.ifset.2020.102379

Bali, K.C., Kaya Yıldırım, F., & Ulusoy, B.H. (2024). Artificial intelligence-based model for evaluating the inhibition of Listeria monocytogenes, Staphylococcus aureus, and Escherichia coli in kefir matrix. Quality Assurance and Safety of Crops & Foods, 16(4), 80–98. https://doi.org/10.15586/QAS.V16I4.1459

Banús, N., Boada, I., Xiberta, P., Toldrà, P., & Bustins, N. (2021). Deep learning for the quality control of thermoforming food packages. Scientific Reports, 11(1), 21887. https://doi.org/10.1038/s41598-021-01254-x

Barbut, S. (2020). Meat Industry 4.0: A Distant Future?. Animal Frontiers, 10(4), 38–47. https://doi.org/10.1093/af/vfaa038

Barge, P., Biglia, A., Comba, L., Ricauda Aimonino, D., Tortia, C., & Gay, P. (2020). Radio Frequency IDentification for Meat Supply-Chain Digitalisation. Sensors, 20(17), 4957. https://doi.org/10.3390/s20174957

Bhat, W., Manzoor, A., Ahmad, Z., & Qureshi, D. (2023). How to Conduct Bibliometric Analysis Using R-Studio: A Practical Guide. European Economics Letters, 13, 681–700. https://doi.org/10.52783/eel.v13i3.350

Bhatia, S., & Albarrak, A.S. (2023). A Blockchain-Driven Food Supply Chain Management Using QR Code and XAI-Faster RCNN Architecture. Sustainability, 15(3), 2579. https://doi.org/10.3390/su15032579

Borras, E., Wang, Y., Shah, P., Bellido, K., Hamera, K. L., Arlen, R. A., ... & Turpen, T. H. (2023). Active sampling of volatile chemicals for non-invasive classification of chicken eggs by sex early in incubation. Plos one, 18(5), e0285726. https://doi.org/10.1371/journal.pone.0285726

Bui, T.D., Tseng, J.W., Tran, T.P.T., Ha, H.M., Lim, M.K., & Tseng, M.L. (2023). Circular supply chain strategy in Industry 4.0: The canned food industry in Vietnam. Business Strategy and the Environment, 32(8), 6047–6073. https://doi.org/10.1002/BSE.3472

Burgdorf, S., Roddelkopf, T., & Thurow, K. (2024). Automated Crystallization Monitoring in Material Development using Computer Vision and Neuronal Networks. Chemie Ingenieur Technik, 96(3). https://doi.org/10.1002/cite.202300049

Casablanca, P.M., & Arroyo-Barrigüete, J.L. (2023). Productividad en la Industria 4.0. Evidencias empíricas en el sector de embotellado. Revista de Ingeniería de Organización, 79. https://doi.org/10.37610/dyo.v0i79.636

Chard, L. (2021). Lab Techniques for a More Sustainable World. BioTechniques, 71(4), 501–504. https://doi.org/10.2144/btn-2021-0081

Chen, T.-C., & Yu, S.-Y. (2022). The review of food safety inspection system based on artificial intelligence, image processing, and robotic. Food Science and Technology, 42, e35421. https://doi.org/10.1590/fst.35421

Chiras, D., Stamatopoulou, M., Paraskevis, N., Moustakidis, S., Tzimitra-Kalogianni, I., & Kokkotis, C. (2023). Explainable Machine Learning Models for Identification of Food-Related Lifestyle Factors in Chicken Meat Consumption Case in Northern Greece. BioMedInformatics, 3(3), 817–828. https://doi.org/10.3390/biomedinformatics3030051

D’Amore, G., Di Vaio, A., Balsalobre-Lorente, D., & Boccia, F. (2022). Artificial intelligence in the water–energy–food model: a holistic approach towards sustainable development goals. Sustainability, 14(2), 867. https://doi.org/10.3390/su14020867

Dadi, V., Nikhil, S. R., Mor, R., Agarwal, T., & Arora, S. (2021). Agri-food 4.0 and innovations: Revamping the supply chain operations. Production Engineering Archives, 27(2), 75-89. https://doi.org/10.30657/pea.2021.27.10

Dawid, H., & Neugart, M. (2023). Effects of technological change and automation on industry structure and (wage-) inequality: insights from a dynamic task-based model. Journal of Evolutionary Economics, 33(1), 35-63. https://doi.org/10.1007/s00191-022-00803-5

De Pilli, T. (2022). Application of fuzzy logic system for the pizza production processing optimisation. Journal of Food Engineering, 319, 110906. https://doi.org/10.1016/J.JFOODENG.2021.110906

Konur, S., Lan, Y., Thakker, D., Morkyani, G., Polovina, N., & Sharp, J. (2023). Towards design and implementation of Industry 4.0 for food manufacturing. Neural Computing and Applications, 35, 23753–23765. https://doi.org/10.1007/s00521-021-05726-z

Decardi-Nelson, B., & You, F. (2024). Artificial intelligence can regulate light and climate systems to reduce energy use in plant factories and support sustainable food production. Nature Food, 5(10), 869–881. https://doi.org/10.1038/S43016-024-01045-3

Di Vaio, A., Boccia, F., Landriani, L., & Palladino, R. (2020). Artificial Intelligence in the Agri-Food System: Rethinking Sustainable Business Models in the COVID-19 Scenario. Sustainability, 12(12), 4851. https://doi.org/10.3390/su12124851

Duan, J.L., Lai, L.Q., Yang, Z., Luo, Z.J., & Yuan, H.T. (2024). Multi-feature language-image model for fruit quality image classification. Computers and Electronics in Agriculture, 227, 109462. https://doi.org/10.1016/J.COMPAG.2024.109462

Einarsdóttir, H., Guðmundsson, B., & Ómarsson, V. (2022). Automation in the fish industry. Animal Frontiers, 12(2), 32-39. https://doi.org/10.1093/af/vfac020

Farah Bader., & Shahin Rahimifard (2020). A methodology for the selection of industrial robots in food handling. Innovative Food Science & Emerging Technologies, 64, 102379. https://doi.org/10.1016/j.ifset.2020.102379

Friege, H., & Eger, Y. (2022). Best practice for bio-waste collection as a prerequisite for high-quality compost. Waste Management & Research, 40(1), 104-110. https://doi.org/10.1177/0734242X211033714

Fries, M., & Ludwig, T. (2024). ‘why are the sales forecasts so low?’socio-technical challenges of using machine learning for forecasting sales in a bakery. Computer Supported Cooperative Work (CSCW), 33(2), 253-293. https://doi.org/10.1007/s10606-022-09458-z

Ghanghas, S., Kumar, N., Singh, V. K., Kumar, S., Birania, S., & Kumar, A. (2024). Image processing technology, imaging techniques, and their application in the food processing sector. Nonthermal Food Engineering Operations, 193-223. https://doi.org/10.1002/9781119776468.ch6

van der Burg, S., Giesbers, E., Bogaardt, M. J., Ouweltjes, W., & Lokhorst, K. (2024). Ethical aspects of AI robots for agri-food; a relational approach based on four case studies. AI & SOCIETY, 39(2), 541-555. https://doi.org/10.1007/s00146-022-01429-8

Goyache, F., Bahamonde, A., Alonso, J., López, S., Del Coz, J. J., Quevedo, J. R., ... & Díez, J. (2001). The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry. Trends in Food Science & Technology, 12(10), 370-381. https://doi.org/10.1016/S0924-2244(02)00010-9

Grenier, P., Alvarez, I., Roger, J.M., Steinmetz, V., Barre, P., & Sablayrolles, J.M. (2000). Artificial intelligence in wine-making. OENO One, 34(2), 61–68. https://doi.org/10.20870/OENO-ONE.2000.34.2.1007

Gružauskas, V., & Burinskienė, A. (2022). Managing Supply Chain Complexity and Sustainability: The Case of the Food Industry. Processes, 10(5), 852. https://doi.org/10.3390/pr10050852

Hamill, R.M., Ferragina, A., Mishra, J.P., Kavanagh, A., Hibbett, M., Gagaoua, M., Colreavy, J., & Rady, A. (2024). Toward Meat Industry 4.0: opportunities and challenges for digitalized red meat processing. Food Industry 4.0, 259–281. https://doi.org/10.1016/B978-0-443-15516-1.00013-X

Hassoun, A., Jagtap, S., Trollman, H., Garcia-Garcia, G., Abdullah, N. A., Goksen, G., ... & Lorenzo, J. M. (2023). Food processing 4.0: Current and future developments spurred by the fourth industrial revolution. Food Control, 145, 109507. https://doi.org/10.1016/j.foodcont.2022.109507

Heinzova, R., Strohmandl, J., & Janova, N. (2024). Production and logistics 4.0 in the food industry in the Czech Republic. International Scientific Journal about Logistica, 11, 421–427. https://doi.org/10.22306/al.v11i3.527

Heuson, E., Etchegaray, A., Filipe, S. L., Beretta, D., Chevalier, M., Phalip, V., & Coutte, F. (2019). Screening of lipopeptide‐producing strains of Bacillus sp. using a new automated and sensitive fluorescence detection method. Biotechnology Journal, 14(4), 1800314. https://doi.org/10.1002/biot.2018003

Einarsdóttir, H., Guðmundsson, B., & Ómarsson, V. (2022). Automation in the fish industry. Animal Frontiers, 12(2), 32-39. https://doi.org/10.1093/af/vfac020

Hobbs, J.E. (2021). The Covid-19 pandemic and meat supply chains. Meat Science, 181, 108459. https://doi.org/10.1016/j.meatsci.2021.108459

Holzinger, I., Fister, I., Fister, H.-P., Kaul, H.P., & Asseng, S. (2024). Human-Centered AI in Smart Farming: Toward Agriculture 5.0. IEEE Access, 12, 62199- 62214. https://doi.org/10.1109/ACCESS.2024.3395532

Hsieh, S.-J., & Hykin, J. (2024). Multi-Stage Corn-to-Syrup Process Monitoring and Yield Prediction Using Machine Learning and Statistical Methods. Sensors, 24(19), 6401. https://doi.org/10.3390/S24196401

Ibn-Mohammed, T., Mustapha, K. B., Godsell, J., Adamu, Z., Babatunde, K. A., Akintade, D. D., ... & Koh, S. C. L. (2021). A critical analysis of the impacts of COVID-19 on the global economy and ecosystems and opportunities for circular economy strategies. Resources, Conservation and Recycling, 164, 105169. https://doi.org/10.1016/j.resconrec.2020.105169

Ilyukhin, S.V., Haley, T.A., & Singh, R.K. (2001). A survey of automation practices in the food industry. Food Control, 12(5), 285–296. https://doi.org/10.1016/S0956-7135(01)00015-9

Jha, R., Lang, W., & Jedermann, R. (2023). Ultrasonic measurement setup for monitoring pre-thawing stages of food. Journal of Sensors and Sensor Systems, 12, 133–139. https://doi.org/10.5194/jsss-12-133-2023

Jossa-Bastidas, O., Sanchez, A. O., Bravo-Lamas, L., & Garcia-Zapirain, B. (2023). Iot system for gluten prediction in flour samples using NIRS technology, deep and machine learning techniques. Electronics, 12(8),1916. https://doi.org/10.3390/electronics12081916

Jung, D. H., Kim, N. Y., Moon, S. H., Jhin, C., Kim, H. J., Yang, J. S., ... & Park, S. H. (2021). Deep learning-based cattle vocal classification model and real-time livestock monitoring system with noise filtering. Animals, 11(2), 357. https://doi.org/10.3390/ani11020357

Saraji, M. K., Aliasgari, E., & Streimikiene, D. (2023). Assessment of the challenges to renewable energy technologies adoption in rural areas: A Fermatean CRITIC-VIKOR approach. Technological Forecasting and Social Change, 189, 122399. https://doi.org/10.1016/j.techfore.2023.122399

Katiyar, S., Khan, R., & Kumar, S. (2021). Artificial bee colony algorithm for fresh food distribution without quality loss by delivery route optimization. Journal of Food Quality, 2021(1), 4881289. https://doi.org/10.1155/2021/4881289

Koulouris, A., Misailidis, N., & Petrides, D. (2021). Applications of process and digital twin models for production simulation and scheduling in the manufacturing of food ingredients and products. Food and Bioproducts Processing, 126, 317–333. https://doi.org/10.1016/j.fbp.2021.01.016

Krishna, S., Kumar, R., Rose, J., Patidar, V. Soni, A., Mehta, D., & Ranadive, A. (2023). Artificial intelligence and big data analytics-based optimization of crop yields in sustainable agriculture. Carpathian Journal of Food Science and Technology, Special Issue, 1–15. https://doi.org/10.34302/SI/238

Kumar, I., Rawat, J., Mohd, N., & Husain, S. (2021). Opportunities of artificial intelligence and machine learning in the food industry. Journal of Food Quality, 2021(1), 4535567. https://doi.org/10.1155/2021/4535567

Lee, J., Kim, Y., & Kim, S. (2023). The study of an adaptive bread maker using machine learning. Foods, 12(22), 4160. https://doi.org/10.3390/foods12224160

Li, X., & Sharma, A. (2024). Development of NC Program Simulation Software Based on AutoCAD Swarm Optimization Algorithm. Computer-Aided Design and Applications, 21(S6), 30–40. https://doi.org/10.14733/cadaps.2024.S6.30-40

Lievano-Martínez, F. A., Fernández-Ledesma, J. D., Burgos, D., Branch-Bedoya, J. W., & Jimenez-Builes, J. A. (2022). Intelligent process automation: An application in manufacturing industry. Sustainability, 14(14), 8804. https://doi.org/10.3390/su14148804

Lin, C.J., & Prasetyo, R. (2025). Learning performance and physiological feedback-based evaluation for human–robot collaboration. Applied Ergonomics, 124, 104425. https://doi.org/10.1016/j.apergo.2024.104425

Luiz, L.da C., Nascimento, C.A., Bell, M.J.V., Batista, R.T., Meruva, S., & Anjos, V. (2022). Use of mid infrared spectroscopy to analyze the ripening of Brazilian bananas. Food Science and Technology, 42, e74221. https://doi.org/10.1590/fst.74221

Lutoslawski, K., Hernes, M., Radomska, J., Hajdas, M., Walaszczyk, E., & Kozina, A. (2021). Food demand prediction using the nonlinear autoregressive exogenous neural network. IEEE Access, 9, 146123-146136.https://doi.org/10.1109/ACCESS.2021.3123255

Madhavan, M., Sharafuddin, M. A., & Wangtueai, S. (2024). Impact of Industry 5.0 Readiness on Sustainable Business Growth of Marine Food Processing SMEs in Thailand. Administrative Sciences, 14(6), 110.https://doi.org/10.3390/admsci14060110

Makridis, G., Mavrepis, P., & Kyriazis, D. (2023). A deep learning approach using natural language processing and time-series forecasting towards enhanced food safety. Machine Learning, 112, 1287–1313. https://doi.org/10.1007/s10994-022-06151-6

Mansourvar, M., Funk, J., Petersen, S. D., Tavakoli, S., Hoof, J. B., Corcoles, D. L., ... & Frandsen, R. J. N. (2024). Automatic classification of fungal-fungal interactions using deep leaning models. Computational and Structural Biotechnology Journal, 23, 4222-4231.https://doi.org/10.1016/J.CSBJ.2024.11.027

Markovic, M., Li, A., Ayall, T. A., Watson, N. J., Bowler, A. L., Woods, M., ... & Leontidis, G. (2024). Embedding AI-Enabled Data Infrastructures for Sustainability in Agri-Food: Soft-Fruit and Brewery Use Case Perspectives. Sensors, 24(22), 7327.https://doi.org/10.3390/S24227327

McCarney, E. R., Dykstra, R., Dykstra, C. G., & FitzPatrick, A. (2023). Automated Eating Quality Measurements on Lamb Carcases in a Processing Plant Using Unilateral NMR. Applied Magnetic Resonance, 54(11), 1377-1389. https://doi.org/10.1007/s00723-023-01615-x

Wang, M., & Li, X. (2024). Application of artificial intelligence techniques in meat processing: A review. Journal of Food Process Engineering, 47(3), e14590. https://doi.org/10.1111/jfpe.14590

Mokhtar, A., He, H., Nabil, M., Kouadri, S., Salem, A., & Elbeltagi, A. (2024). Securing China’s rice harvest: Unveiling dominant factors in production using multi-source data and hybrid machine learning models. Scientific Reports, 14(1), 14699. https://doi.org/10.1038/s41598-024-64269-0

Neo, Y.T., Chia, W.Y., Lim, S.S., Ngan, C.L., Kurniawan, T.A., & Chew, K.W. (2023). Smart systems in producing algae-based protein to improve functional food ingredients industries. Food Research International, 165, 112480. https://doi.org/10.1016/j.foodres.2023.112480

Neri, I., Caponi, S., Bonacci, F., Clementi, G., Cottone, F., Gammaitoni, L., ... & Mattarelli, M. (2024). Real-Time AI-Assisted Push-Broom Hyperspectral System for Precision Agriculture. Sensors, 24(2), 344. https://doi.org/10.3390/S24020344

Nobel, S. N., Wadud, M. A. H., Rahman, A., Kundu, D., Aishi, A. A., Sazzad, S., ... & Bhuiyan, T. A. U. H. (2024). Categorization of dehydrated food through hybrid deep transfer learning techniques. Statistics, Optimization & Information Computing, 12(4), 1004-1018. https://doi.org/10.19139/soic-2310-5070-1896

Nolasco-Mamani, M. A., Vidaurre, S. M. E., & Choque-Salcedo, R. E. (2022). Innovación y Transformación Digital en la Empresa. ACVENISPROH Académico. https://doi.org/10.47606/ACVEN/ACLIB0039

Anam, K., & Al-Jumaily, A. (2020). Performance evaluation of SRELM on bio-signal pattern recognition using two electromyography channels. International Journal on Advanced Science, Engineering and Information Technology, 10(5), 1963–1969. https://doi.org/10.18517/ijaseit.10.5.9261

Suchacka, M., Pabian, A. M., & Ulewicz, R. (2023). Industry 4.0 and socio-economic evolution. Polish Journal of Management Studies, 28. https://doi.org/10.17512/pjms.2023.28.1.18

Park, J. Y., Park, K., Ok, G., Chang, H. J., Park, T. J., Choi, S. W., & Lim, M. C. (2020). Detection of Escherichia coli O157: H7 using automated immunomagnetic separation and enzyme-based colorimetric assay. Sensors, 20(5), 1395. https://doi.org/10.3390/s20051395

Ponce, J. M., Aquino, A., & Andujar, J. M. (2019). Olive-fruit variety classification by means of image processing and convolutional neural networks. IEEE Access, 7, 147629-147641. https://doi.org/10.1109/ACCESS.2019.2947160

Raamets, T., Majak, J., Karjust, K., Mahmood, K., & Hermaste, A. (2024). Autonomous mobile robots for production logistics: a process optimization model modification. Proceedings of the Estonian Academy of Sciences,73(2). https://doi.org/10.3176/proc.2024.2.06

Ramachandran, R. P., Nadimi, M., Cenkowski, S., & Paliwal, J. (2024). Advancement and Innovations in Drying of Biopharmaceuticals, Nutraceuticals, and Functional Foods. Food Engineering Reviews, 1-27. https://doi.org/10.1007/s12393-024-09381-7

Ramalingam, B., Mohan, R. E., Pookkuttath, S., Gómez, B. F., Sairam Borusu, C. S. C., Wee Teng, T., & Tamilselvam, Y. K. (2020). Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT. Sensors, 20(18), 5280. https://doi.org/10.3390/s20185280

Ramirez-Asis, E., Vilchez-Carcamo, J., Thakar, C.M., Phasinam, K., Kassanuk, T., & Naved, M. (2022). A review on role of artificial intelligence in food processing and manufacturing industry. Materials Today: Proceedings, 51(8), 2462–2465. https://doi.org/10.1016/j.matpr.2021.11.616

Rapado-Rincón, D., Nap, H., Smolenova, K., Van-Henten, E., & Koostra, G. (2023). MOT-DETR: 3D single-shot detection and tracking with transformers to create 3D renderings for agri-food robots. Computers and Electronics in Agriculture, 225,109275. https://doi.org/10.48550/arXiv.2311.15674

Rashvand, M., Altieri, G., Abbaszadeh, R., Matera, A., Genovese, F., Feyissa, A. H., & Di Renzo, G. C. (2023). Prediction of CO2 and ethylene produced in‐packaged apricot under cold plasma treatment by machine learning approach. Journal of Food Process Engineering, 46(9), e14418. https://doi.org/10.1111/jfpe.14418

Redchuk, A., Walas Mateo, F., Pascal, G., Tornillo, J.E. (2023). Adoption Case of IIoT and Machine Learning to Improve Energy Consumption at a Process Manufacturing Firm, under Industry 5.0 Model. Big Data and Cognitive Computing, 7(1), 42. https://doi.org/10.3390/bdcc7010042

Rokhva, S., Teimourpour, B., & Soltani, A.H. (2024). Computer vision in the food industry: Accurate, real-time, and automatic food recognition with pretrained MobileNetV2. Food and Humanity, 3,100378. https://doi.org/10.1016/J.FOOHUM.2024.100378

Sawangwong, A., & Chaopaisarn, P. (2023). The impact of applying knowledge in the technological pillars of Industry 4.0 on supply chain performance. Kybernetes, 52(3), 1094–1126. https://doi.org/10.1108/K-07-2021-0555

Seaton, M. (2022). Lessons in automation of meat processing. Animal Frontiers, 12(2), 25–31. https://doi.org/10.1093/af/vfac022

Seifi, M. R., Alimardani, R., Mohtasebi, S. S., Mobli, H., & Firouz, M. S. (2023). A Supervisory Control System for Automation of Horizontal Form-Fill-Seal Packaging Plant Based on Modified Atmosphere Technology. Acta Mechanica et Automatica, 17(3), 423–434. https://doi.org/10.2478/ama-2023-0049

Shimizu, R., & Momoda, S. (2023). Does automation technology increase wage. Journal of Macroeconomics, 77, 103541. https://doi.org/10.1016/j.jmacro.2023.103541

Sindermann, D., Heidhues, J., Kirchner, S., Stadermann, N., & Kühl, A. (2021). Industrial processing technologies for insect larvae. Journal of Insects as Food and Feed, 7(5), 857–876. https://doi.org/10.3920/JIFF2020.0103

Squara, S., Caratti, A., Fina, A., Liberto, E., Koljančić, N., Špánik, I., ... & Cordero, C. (2024). Artificial intelligence decision making tools in food metabolomics: Data fusion unravels synergies within the hazelnut (Corylus avellana L.) metabolome and improves quality prediction. Food Research International, 194, 114873. https://doi.org/10.1016/J.FOODRES.2024.114873

Starynina, J., & Ustinovichius, L. (2020). A multi-criteria decision-making synthesis method to determine the most effective option for modernising a public building. Technological and Economic Development of Economy, 26(6), 1237–1262. https://doi.org/10.3846/tede.2020.13398

Takács, K., Takács, B., Garamvölgyi, T., Tarsoly, S., Alexy, M., Móga, K., Rudas, I.J., Galambos, P., & Haidegger, T. (2024). Sensor-Enhanced Smart Gripper Development for Automated Meat Processing. Sensors, 24(14), 4631. https://doi.org/10.3390/s24144631

Tell, F., López, J.M., Sanchéz, I.Y., Paredes, C.A., & Pisano, E. (2023). Evaluation of the degree of automation and digitalization using a diagnostic and analysis tool for a methodological implementation of Industry 4.0. Computers & Industrial Engineering, 177,109097. https://doi.org/10.1016/j.cie.2023.109097

Tleuvelessova, D., Medvedkov, Y., Kairbayeva, A., & Nazymbekova, A. (2023). Mechanisation of the primary processing of watermelons without destroying the rind. Food Science and Technology, 43, e86622. https://doi.org/10.1590/fst.86622

Tran, M., Troung, S., Fernandez, A., Kidd, M., & Le, N. (2024). CarcassFormer: an end-to-end transformer-based framework for simultaneous localization, segmentation and classification of poultry carcass defect. Poultry Science, 103(8), 103765. https://doi.org/10.1016/j.psj.2024.103765

Vargas, M., Mosquera, R., Fuertes, G., Alfaro, M., & Perez-Varga, G. (2024). Process optimisation in a condiment SME through Lean Six Sigma improvement with a neural network of surface tension. Processes, 12(9), 2001. https://doi.org/10.3390/pr12092001

Verma, P.K., Pathak, P., Kumar, B., Himani, H., & Preety, P. (2023). Automatic Optical Imaging System for Mango Fruit using Hyperspectral Camera and Deep Learning Algorithm. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5s), 112–117. https://doi.org/10.17762/ijritcc.v11i5s.6635

Voipio, C., Vilko, J., Calvo, E., & Korpela, J. (2023). The future of work: skills and knowledge perspective on service automation in the foodservice industry. Technology Analysis & Strategic Management, 36(10), 2846–2860. https://doi.org/10.1080/09537325.2023.2165440

Vrchota, J., Vlčková, M., & Frantíková, Z. (2020). Division of Enterprises and Their Strategies in Relation to Industry 4.0. Central European Business Review, 9(4), 27–44. https://doi.org/10.18267/j.cebr.243

Vujičić, S., Hasanspahić, N., Car, M., & Čampara, L. (2020). Distributed Ledger Technology as a Tool for Environmental Sustainability in the Shipping Industry. Journal of Marine Science and Engineering, 8(5), 366. https://doi.org/10.3390/jmse8050366

Wang, Z., Makiyama, Y., & Hirai, S. (2021). A Soft Needle Gripper Capable of Grasping and Piercing for Handling Food Materials. Journal of Robotics and Mechatronics, 33(4), 935–943. https://doi.org/10.20965/jrm.2021.p0935

Wright, R., Parekh, S., & White, R. (2024). Safely and autonomously cutting meat with a collaborative robot arm. Scientific Reports, 14, 299. https://doi.org/10.1038/s41598-023-50569-4

Xu, W., He, Y., Li, J., Zhou, J., Xu, E., Wang, W., & Liu, D. (2023). Robotization and intelligent digital systems in the meat cutting industry: From the perspectives of robotic cutting, perception, and digital development. Trends in Food Science & Technology, 135, 234–251. https://doi.org/10.1016/j.tifs.2023.03.018

Zarikhani, Z., Mostafaee, K., & Azar, A. (2024). Route mapping of artificial intelligence technologies for the food industry: a TDE approach. Journal of Industrial and Production Management, 39, 299–326. https://doi.org/10.22034/jipm.2024.711533

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06/30/2025

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ARTÍCULO ORIGINAL

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Damián Moran, G. J., Mayhua Ayuque, J., Inga Quinto, J. E., Larrea Cerna, C. O., & Callirgos Romero, D. (2025). Industria 4.0 y su relación con la automatización en la industria Alimentaria: Una revisión sistemática y bibliométrica. Manglar, 22(2), 229-243. https://doi.org/10.57188/manglar.2025.025

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