Skip to main content

Automation in Food Manufacturing and Processing

  • Chapter
  • First Online:
Springer Handbook of Automation

Part of the book series: Springer Handbooks ((SHB))

Abstract

Factory-based food production and processing globally forms one of the largest economic and employment sectors. Within it, current automation and engineering practice is highly variable, ranging from completely manual operations to the use of the most advanced manufacturing systems. This chapter will discuss the factors that make automation of food production so essential and at the same time consider counterinfluences that on occasions have prevented this automation uptake.

The chapter will focus on the diversity of an industry covering areas such as bakery, dairy, confectionary, snacks, meat, poultry, seafood, produce, sauce/condiments, frozen, and refrigerated products, which means that generic solutions are often (considered by the industry) difficult or impossible to obtain. However, it will also be shown that there are many features in the production process that are almost completely generic, such as labeling, quality/safety automation/inspection, and palletization, and others that do in fact require an almost unique approach due to the natural and highly variable features of food products. In considering these needs, this chapter has therefore approached the specific automation requirements of food production from two perspectives. Firstly, it will be shown that in many cases there are generic automation solutions that could be valuably used across the industry ranging from small cottage facilities to large multinational manufacturers. Examples of generic types of automation well suited across the industry will be provided. In addition, for some very specific difficult handling operations, customized solutions will be shown to give opportunities to study the problems/risks/demands associated with food handling and to provide an insight into the solution, thereby demonstrating that in most instances the difficult/impossible can indeed be achieved.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 309.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 399.00
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Data Trends of the European Food and Drink Industry 2019. FoodDrinkEurope, Brussels (2019)

    Google Scholar 

  2. https://www.fda.gov/food/guidance-regulation-food-and-dietary-supplements/hazard-analysis-critical-control-point-haccp Accessed 17 June 2020

  3. https://www.food.gov.uk/topic/haccp. Accessed 17 June 2020

  4. https://www.euro.who.int/en/health-topics/disease-prevention/food-safety/areas-of-work/building-national-capacity-in-food-safety/hazard-analysis-and-critical-control-points-haccp-systems Accessed 17 June 2020

  5. https://www.fda.gov/drugs/pharmaceutical-quality-resources/facts-about-current-good-manufacturing-practices-cgmps Accessed 20 June 2020

  6. ISO: ISO 22000:2018, Food safety management systems — Requirements for any organization in the food chain, ISO (Geneva), 2018

    Google Scholar 

  7. Materials of Construction Subgroup of the EHEDG: Materials of construction for equipment in contact with food. Trends Food Sci. Technol. 18(51), 540–555 (2007). https://doi.org/10.1016/j.tifs.2006.11.025

    Article  Google Scholar 

  8. Lelieveld, H.L.M., Holah, J.T., Napper, D. (eds.): Hygiene in Food Processing Principles and Practice, 2nd edn. Woodhead Publishing (2013). ISBN: 9780857094292

    Google Scholar 

  9. Honess, C.: Importance of surface finish in the design of stainless steel. In: Stainless Steel Ind, p. 14.15. British Stainless Steel Association, Sheffield (2006)

    Google Scholar 

  10. FDA: Indirect food additives: Polymers. Code of Federal Regulations, CFR Title 21, part 177. (FDA Rockville 2007). Available online from: https://www.govinfo.gov/content/pkg/FR-2017-05-04/pdf/2017-08988.pdf

  11. Midelet, G., Carpentier, B.: Transfer of microorganisms, including listeria monocytogenes, from various materials to beef. Appl. Environ. Microbiol. 68(8), 4015–4024 (2002)

    Article  Google Scholar 

  12. FDA: Indirect food additives: Adhesives and components of coatings. CFR Title 21, part 175. (FDA Rockville 2007). Available online from: https://www.govinfo.gov/content/pkg/FR-2013-07-12/pdf/2013-16684.pdf

  13. Nuttall, A.: Design aspects of multiple driven belt conveyors. Doctoral Dissertation, Delft University of Technology, Delft (2007)

    Google Scholar 

  14. Edwards, M.C., Stringer, M.F.: Observations on patterns in foreign material investigations. Food Control. 18(7), 773–782 (2007). https://doi.org/10.1016/j.foodcont.2006.01.007

    Article  Google Scholar 

  15. Yamazaki, T., Sakurai, Y., Ohnishi, H., Kobayashi, M., Kurosu, S.: Continuous mass measurement in checkweighers and conveyor belt scales. In: Proceedings of the 41st SICE Annual Conference. SICE 2002, Osaka, vol. 1, pp. 470–474 (2002). https://doi.org/10.1109/SICE.2002.1195446

  16. Pietrzak, P., Meller, M., Niedzwiecki, M.: Dynamic mass measurement in checkweighers using a discrete time-variant low-pass filter. Mech. Syst. Signal Process. 48(1–2), 67–76 (2014). https://doi.org/10.1016/j.ymssp.2014.02.013

    Article  Google Scholar 

  17. Loma systems: A guide to metal detection in the food manufacturing industry, Loma systems. https://www.elmedint.com/uploads/editor/Guide_to_Metal_Detection.pdf. Accessed 2 Sept 2020

  18. Mathanker, S.K., Weckler, P.R., Bowser, T.: X-ray applications in food and agriculture: a review. Transactions of the ASABE (American Society of Agricultural and Biological Engineers) 56(3), 1227–1239 (2013). https://doi.org/10.13031/trans.56.9785

  19. Kotwaliwale, N., Singh, K., Kalne, A., et al.: X-ray imaging methods for internal quality evaluation of agricultural produce. J. Food Sci. Technol. 51(1), 1–15 (2011). https://doi.org/10.1007/s13197-011-0485-y

    Article  Google Scholar 

  20. Lind, R., Murhed, A.: Computer vision in food processing: an overview. In Sun, D.W. (ed.) Computer Vision Technology in the Food and Beverage Industries. pp. 133–149. Woodhead Publishing, London (2012). https://doi.org/10.1533/9780857095770.2.133.

    Google Scholar 

  21. Davies, E.R.: Machine vision in the food industry. In: Caldwell, D.G. (ed.) Robotics and Automation in the Food Industry: Current and Future Technologies, pp. 21–35. Woodhead Publishing, London (2013)

    Google Scholar 

  22. Blasco, J., Aleixos, N., Gómez, J., Moltó, E.: Citrus sorting by identification of the most common defects using multispectral computer vision. J. Food Eng. 83(3), 384–393 (2007)

    Article  Google Scholar 

  23. Riquelme, M.T., Barreiro, P., Ruiz-Altisent, M., Valero, C.: Olive classification according to external damage using image analysis. J. Food Eng. 87, 371–379 (2008)

    Article  Google Scholar 

  24. Okamoto, H., Lee, W.S.: Green citrus detection using hyperspectral imaging. Comput. Electron. Agric. 66(2), 201–208 (2009)

    Article  Google Scholar 

  25. Blasco, J., Aleixos, N., Cubero, S., Gómez-Sanchis, J., Moltó, E.: Automatic sorting of satsuma (Citrus unshiu) segments using computer vision and morphological features. Comput. Electron. Agric. 66, 1–8 (2009)

    Article  Google Scholar 

  26. Jeliński, T., Du, C.-J., Sun, D.-W., Fornal, J.: Inspection of the distribution and amount of ingredients in pasteurized cheese by computer vision. J. Food Eng. 83(1), 3–9 (2007). https://doi.org/10.1016/j.jfoodeng.2006.12.020

    Article  Google Scholar 

  27. Dougherty, G.: Digital Image Processing for Medical Applications, pp. 3–15. Cambridge University Press, Cambridge (2009)

    Book  Google Scholar 

  28. Mahesh, S., Manickavasagan, A., Jayas, D.S., Paliwal, J., White, N.D.G.: Feasibility of near-infrared hyperspectral imaging to differentiate Canadian wheat classes. Biosyst. Eng. 101, 50–57 (2008)

    Article  Google Scholar 

  29. Ureña, R., Rodríguez, F., Berenguel, M.: A machine vision system for seeds germination quality evaluation using fuzzy logic. Comput. Electron. Agric. 32, 1–20 (2001)

    Article  Google Scholar 

  30. Gómez, J., Blasco, J., Moltó, E., Camps-Valls, G.: Hyperspectral detection of citrus damage with a Mahalanobis kernel classifier. Electron. Lett. 43(20), 1082–1084 (2007)

    Article  Google Scholar 

  31. Choudhary, R., Paliwal, J., Jayas, D.S.: Classification of cereal grains using wavelet, morphological, colour, and textural features of non-touching kernel images. Biosyst. Eng. 99, 330–337 (2008)

    Article  Google Scholar 

  32. Chen, K., Qin, C.: Segmentation of beef marbling based on vision threshold. Comput. Electron. Agric. 62, 223–230 (2008)

    Article  Google Scholar 

  33. Zhou, L., Zhang, C., Liu, F., Qiu, Z., He, Y.: Application of deep learning in food: a review. Compr. Rev. Food Sci. Food Saf. 18(6), 1793–1811 (2019)

    Article  Google Scholar 

  34. Zhu, L., Spachos, P., Pensini, E., Plataniotis, K.N.: Deep learning and machine vision for food processing: a survey. Curr. Res. Food Sci. 4, 233–249 (2021)

    Article  Google Scholar 

  35. Saravacos, G., Kostaropoulos, A.: Handbook of Food Processing Equipment. Springer (2016). https://doi.org/10.1007/978-3-319-25020-5

    Book  Google Scholar 

  36. Brody, A.L., Marsh, K.S.: The Wiley Encyclopedia of Packing Technology. Wiley–Blackwell (1997). ISBN 10: 0471063975 ISBN 13: 9780471063971

    Google Scholar 

  37. Popa, M., Belc, N.: Packaging. In: McElhatton, A., Marshall, R.J. (eds.) Food Safety. Springer, Boston (2007). https://doi.org/10.1007/978-0-387-33957-3_4

    Chapter  Google Scholar 

  38. Chua, P.Y., Illner, T., Caldwell, D.G.: Robotic manipulation of food products – a review. Ind. Robot. Int. J. 30(4), 345–354 (2003)

    Article  Google Scholar 

  39. Taylor, P.M.: Presentation and gripping of flexible materials. Assem. Autom. 15(3), 33–35 (1995)

    Article  Google Scholar 

  40. Moreno Masey, R.J., Caldwell, D.G.: Design of an automated handling system for limp, flexible sheet lasagna pasta. In: IEEE Conference on Robotics and Automation, pp. 1226–1231, Rome (2007)

    Google Scholar 

  41. Jørgensen, T.B., Krüger, N., Pedersen, M.M., Hansen, N.W., Hansen, B.R.: Designing a flexible grasp tool and associated grasping strategies for handling multiple meat products in an industrial setting. Int. J. Mech. Eng. Robot. Res. 8(2), 220–227 (2019)

    Article  Google Scholar 

  42. Reed, J.N., Miles, S.J., Butler, J., Baldwin, M., Noble, R.: Automatic mushroom harvester development. J. Agric. Eng. Res. 78(1), 15–23 (2001)

    Article  Google Scholar 

  43. Brown, E., Rodenberg, N., Amend, J., Mozeika, A., Steltz, E., Zakin, M.R., Lipson, H., Jaeger, H.M.: Universal robotic gripper based on the jamming of granular material. In: Proceedings of the National Academy of Sciences, vol. 107, pp. 18809–18814 (2010)

    Google Scholar 

  44. http://www.aewdelford.com/

  45. Gjerstad, T.B., Lien, T.K.: New gripper technology for flexible and efficient fish processing, Food Factory of the Future 3. Sweden, 2006

    Google Scholar 

  46. Salvietti, G., Iqbal, Z., Malvezzi, M., Eslami, T., Prattichizzo, D.: Soft hands with embodied constraints: the Soft ScoopGripper. In: 2019 International Conference on Robotics and Automation (ICRA), Montreal, 2019, pp. 2758–2764. https://doi.org/10.1109/ICRA.2019.8793563

  47. http://soma-project.eu. Accessed June 2020

  48. Davis, S., Gray, J.O., Caldwell, D.G.: A non-contact end effector based on the Bernoulli principle for the handling of sliced tomatoes. Int. J. Robot. Comput. Integr. Manuf. 24(2) (2008)

    Google Scholar 

  49. Stephan, F., Seliger, G.: Handling with ice – the cryo-gripper, a new approach. Assem. Autom. 19(4), 332–337 (1999)

    Article  Google Scholar 

  50. http://www.frperc.bris.ac.uk/

  51. Connolly, C.: Gripping developments at Silsoe. Ind. Robot Int. J. 30(4) (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Darwin G. Caldwell .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Caldwell, D.G. (2023). Automation in Food Manufacturing and Processing. In: Nof, S.Y. (eds) Springer Handbook of Automation. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-030-96729-1_44

Download citation

Publish with us

Policies and ethics