Robotics Autonomous Systems (RAS) in Food Laboratories are the Future of Food Processing Industries: A Review

Mohan A J *

Division of Dairy Microbiology, ICAR-National Dairy Research Institute, Karnal-132001 (Haryana), India.

Mounica V *

Division of Animal Biochemistry, ICAR-National Dairy Research Institute, SRS, Banglore-560030 (Karnataka), India.

Shanthilal J

Division of Dairy Technology, ICAR-National Dairy Research Institute, Karnal-132001 (Haryana), India.

Madhavi R

College of Dairy Technology, Sri Venkateswara Veterinary University (SVVU), Tirupati-517504 (Andhra Pradesh), India.

*Author to whom correspondence should be addressed.


Recent advancements in robotics have witnessed significant progress in both industrial and mobile robotics, paving the way for a new era of automation. However, a paradigm shift is underway in robotics research, focusing on enhancing the interaction between humans and robots, termed as service robotics. This emerging field aims to cater to a wide array of human social needs by bridging the gap between man and machine. Traditionally, laboratory automation has been constrained by the rigid control mechanisms of computer-driven robots. Despite their utility, particularly in liquid handling tasks, many laboratory procedures remain only partially automated. Nonetheless, by breaking down laboratory processes into discrete unit operations and integrating them, overarching analysis schemes can be accomplished. The future of laboratory automation necessitates interdisciplinary skills, requiring scientists to blend biological knowledge with engineering expertise to fully exploit its potential. Simultaneously, a new wave of robotic innovations is permeating various sectors, from robot lawn mowers to autonomous vehicles, alongside smarter robots in manufacturing environments. This progression underscores the increasing reliance on automation, with future research endeavours expected to pivot towards leveraging laboratory automation to tackle novel scientific challenges. This abstract underscore the necessity for future scientists to acquire a comprehensive skill set that integrates both biological knowledge and engineering expertise. It highlights the trend towards greater automation in laboratory environments, representing the merging of scientific and engineering fields to tackle new research challenges.

Keywords: Robotics, automation, pipetting, sample handling, productivity

How to Cite

A J, M., V, M., J, S., & R, M. (2024). Robotics Autonomous Systems (RAS) in Food Laboratories are the Future of Food Processing Industries: A Review. Journal of Advances in Food Science & Technology, 11(3), 17–26.


Download data is not yet available.


World Economic Forum W. The global risks report. Geneva, Switzerland: World Economic Forum; 2019.

Elferink M, Schierhorn F. Global demand for food is rising. Can we meet it. Harvard Business Review. 2016;7:1-6.

Rehman TU, Mahmud MS, Chang YK, Jin J, Shin J. Current and future applications of statistical machine learning algorithms for agricultural machine vision systems. Computers and Electronics in Agriculture. 2019;156:585-605.

Cai S, Ma Z, Skibniewski MJ, Bao S. Construction automation and robotics for high-rise buildings over the past decades: A comprehensive review. Advanced Engineering Informatics. 2019;42:1-18.

Sanders NR, Boone T, Ganeshan R,Wood JD. Sustainable supply chains in the age of AI and digitization: Research challenges and opportunities. Journal of Business Logistics. 2019;40(3):229-240.

Bouzembrak Y, Kluche M, Gavai A, Marvin HJ. Internet of things in food safety: Literature review and a bibliometric analysis. Trends in Food Science and Technology. 2019;94:54-64.

Zamalloa I, Kojcev R, Hernández A, Muguruza I, Usategui L, Bilbao A, Mayoral V. Dissecting robotics-historical overview and future perspectives. Acutronic Robotics. 2017;1-9.

Fuseini A, Hadley P, Knowles T. Halal food marketing: An evaluation of UK halal standards. Journal of Islamic Marketing. 2021;12(5):977-991.

Akkerman R, Farahani P, Grunow M. Quality, safety and sustainability in food distribution: A review of quantitative operations management approaches and challenges. OR Spectrum. 2010;32:863-904.

Soon JM, Brazier AK, Wallace CA. Determining common contributory factors in food safety incidents-A review of global outbreaks and recalls 2008-2018. Trends in Food Science and Technology. 2020; 97:76-87.

FDA. Hazard analysis critical control point (HACCP); 2018.

Kshetri N. Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management. 2018;39:80-89.

Alfian G, Rhee J, Ahn H, Lee J, Farooq U, Ijaz MF, Syaekhoni MA. Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system. Journal of Food Engineering. 2017;212:65-75.

Balocco R, Miragliotta G, Perego A, Tumino A. RFId adoption in the FMCG supply chain: An interpretative framework. Supply Chain Management: An International Journal. 2011;16(5):299- 315.

Kelepouris T, Pramatari K, Doukidis G. RFID‐enabled traceability in the food supply chain. Industrial Management and Data Systems. 2007;107(2):183-200.

Bodai Z, Cameron S, Bolt F, Simon D, Schaffer R, Karancsi T, Takats Z. Effect of electrode geometry on the classification performance of rapid evaporative ionization mass spectrometric (REIMS) bacterial identification. Journal of the American Society for Mass Spectrometry. 2017;29(1):26-33.

Truswell A, Abraham R, O Dea M, Lee ZZ, Lee T, Laird T, Abraham S. Robotic antimicrobial susceptibility platform (RASP): A next-generation approach to One Health surveillance of antimicrobial resistance. Journal of Antimicrobial Chemotherapy. 2021;76(7): 1800-1807.

Ochs J, Biermann F, Piotrowski T, Erkens F, Niebing B, Herbst L, Schmitt RH. Fully automated cultivation of adipose-derived stem cells in the stem cell discovery-A robotic laboratory for small-scale, high-throughput cell production including deep learning-based confluence estimation. Processes. 2021;9(4):575.

Moshayedi AJ, Khan AS, Shuxin Y, Kuan G, Jiandong H, Soleimani M, Razi A. E-Nose design and structures from statistical analysis to application in robotic: A compressive review. EAI Endorsed Transactions on AI and Robotics. 2023; 2(1).

Tan J, Xu J. Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: A review. Artificial Intelligence in Agriculture. 2020;4:104-115.

Panda S, Chen J, Benjamin O. Development of model mouth for food oral processing studies: Present challenges and scopes. Innovative Food science and Emerging Technologies. 2020;66: 102524.

Sasamata M, Shimojo D, Fuse H, Nishi Y, Sakurai H, Nakahata T, Sasaki-Iwaoka H. Establishment of a robust platform for induced pluripotent stem cell research using Maholo LabDroid. Slas Technology: Translating Life Sciences Innovation. 2021;26(5):441-453.

Scheel PD. Robotics in industry: A safety and health perspective. Professional Safety. 1993;38(3):28.