Skip to main content
SpringerLink
Log in

Evaluation of 3D Laser Scanning for Estimation of Heating-Induced Volume Shrinkage and Prediction of Cooking Loss of Pork Cuboids Compared to Manual Measurements

  • Original Paper
  • Published:
Food and Bioprocess Technology Aims and scope Submit manuscript

Abstract

Meat shrinks and assumes an irregular shape during heating due to the varying distribution of connective tissue and extracellular spaces. The terrestrial 3D laser scanning technology is proposed as an alternative method to manual measurements to estimate the volume of irregularly shaped meat cuboids and to predict the cooking loss based on the heating induced volume shrinkage. Cuboids from aged pork loins (longissimus lumborum, n = 12) were heated at 50, 60, 70 or 80 °C for 30 min. Two methods of 3D reconstruction and volume estimation of the pork cuboids by laser scanning were used; without a base scan (laser-B) and with inclusion of a base scan (laser+B), as well as two methods based on manual caliper measurements of all twelve edges (caliper-12) or of three edges in each direction (caliper-3). Both laser scanning methods (Laser+B and Laser-B) resulted in greater volume estimates for the raw samples than the caliper-12 and caliper-3 measurements (38.3, 39.4 compared to 33.9, 34.8 cm3, respectively). Cooking loss across the different temperatures could be best predicted by the caliper-based perimeter shrinkage (r = 0.94, P < 0.001), followed by the caliper-based volume shrinkage estimates (r = 0.67 and 0.64 for caliper-12 and caliper-3, respectively, p < 0.001), while volume shrinkage measured by laser scanning had low (laser-B, r = 0.35, P < 0.05) or no correlation (laser+B, r = 0.17, P > 0.05) with the cooking loss. 3D laser scanning technologies can be considered by the food industry for 3D reconstruction and volume estimation, however improvements are needed in the data processing to allow for better predictability of meat quality attributes.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Aaslyng, M., Bejerholm, C., Ertbjerg, P., Bertram, H. C., & Andersen, H. J. (2003). Cooking loss and juiciness of pork in relation to raw meat quality and cooking procedure. Food Quality and Preference, 14(4), 277–288.

    Article  Google Scholar 

  • Aaslyng, M., Oksama, M., Olsen, E., Bejerholm, C., Baltzer, M., Andersen, G., Bredie, W. L. P., Byrne, D. V., & Gabrielsen, G. (2007). The impact of sensory quality of pork on consumer preference. Meat Science, 76(1), 61–73.

    Article  Google Scholar 

  • Agudelo-Laverde, L. M., Schebor, C., & del Pilar Buera, M. (2014). Evaluation of structural shrinkage on freeze-dried fruits by image analysis: Effect of relative humidity and heat treatment. Food and Bioprocess Technology, 7(9), 2618–2626.

    Article  CAS  Google Scholar 

  • Aversa, M., Curcio, S., Calabrò, V., & Iorio, G. (2012). Experimental evaluation of quality parameters during drying of carrot samples. Food and Bioprocess Technology, 5(1), 118–129.

    Article  Google Scholar 

  • Barbera, S., & Tassone, S. (2006). Meat cooking shrinkage: Measurement of a new meat quality parameter. Meat Science, 73(3), 467–474.

    Article  CAS  Google Scholar 

  • Bernstein, A., & Noreña, C. P. Z. (2014). Study of thermodynamic, structural, and quality properties of yacon (Smallanthus sonchifolius) during drying. Food and Bioprocess Technology, 7(1), 148–160.

    Article  Google Scholar 

  • Bryhni, E. A., Byrne, D. V., Rødbotten, M., Møller, S., Claudi-Magnussen, C., Karlsson, A., Agerhem, H., Johansson, M., & Martens, M. (2003). Consumer and sensory investigations in relation to physical/chemical aspects of cooked pork in Scandinavia. Meat Science, 65(2), 737–748.

    Article  CAS  Google Scholar 

  • Channon, H. A., D'Souza, D. N., & Dunshea, F. R. (2016). Developing a cuts-based system to improve consumer acceptability of pork: Impact of gender, ageing period, endpoint temperature and cooking method. Meat Science, 121, 216–227.

    Article  CAS  Google Scholar 

  • Clemente, G., Bon, J., Sanjuán, N., & Mulet, A. (2009). Determination of shrinkage function for pork meat drying. Drying Technology, 27(1), 143–148.

    Article  Google Scholar 

  • Diamant, R., Watts, B. M., & Cliplef, R. L. (1976). Consumer criteria for pork related to sensory, physical and descriptive attributes. Canadian Institute of Food Science and Technology Journal, 9(3), 151–154.

    Article  Google Scholar 

  • Du, C., & Sun, D. -W. (2005). Correlating shrinkage with yield, water content and texture of pork ham by computer vision. Journal of Food Process Engineering, 28(3), 219–232.

  • Du, C., Iqbal, A., & Sun, D. -W. (2016). Chapter 8 - quality measurement of cooked meats. In D.-W. Sun (Ed.), Computer vision Technology for Food Quality Evaluation (Second ed., pp. 195–212). San Diego: Academic Press.

  • Faro Technologies (2013). User manual for the Focus3D 20/120 and S 20/120. https://faro.app.box.com/s/kfpwjofogeegocr7mf2s866s2qalnaqw Accessed 13 Dec 2018.

  • Förstner, W., & Khoshelham, K. (2017). Efficient and accurate registration of point clouds with plane to plane correspondences. Presentation at the Computer Vision Workshop (ICCVW), 2017 IEEE international conference on computer vision and pattern recognition, Honolulu, Hawaii, United States.

  • Friedli, M., Kirchgessner, N., Grieder, C., Liebisch, F., Mannale, M., & Walter, A. (2016). Terrestrial 3D laser scanning to track the increase in canopy height of both monocot and dicot crop species under field conditions. Plant Methods, 12(1), 9.

    Article  Google Scholar 

  • Giavarina, D. (2015). Understanding bland Altman analysis. Biochemia Medica, 25(2), 141–151.

    Article  Google Scholar 

  • Igathinathane, C., Davis, J., Purswell, J., & Columbus, E. (2010). Application of 3D scanned imaging methodology for volume, surface area, and envelope density evaluation of densified biomass. Bioresource Technology, 101(11), 4220–4227.

    Article  CAS  Google Scholar 

  • Kirtil, E., & Oztop, M. H. (2016). 1 H nuclear magnetic resonance relaxometry and magnetic resonance imaging and applications in food science and processing. Food Engineering Reviews, 8(1), 1–22.

    Article  Google Scholar 

  • Kondjoyan, A., Oillic, S., Portanguen, S., & Gros, J.-B. (2013). Combined heat transfer and kinetic models to predict cooking loss during heat treatment of beef meat. Meat Science, 95(2), 336–344.

    Article  Google Scholar 

  • Lepetit, J. (2008). Collagen contribution to meat toughness: Theoretical aspects. Meat Science, 80(4), 960–967.

    Article  CAS  Google Scholar 

  • Li, C., Zhou, G., & Xu, X. (2010). Dynamical changes of beef intramuscular connective tissue and muscle fiber during heating and their effects on beef shear force. Food and Bioprocess Technology, 3(4), 521–527.

    Article  Google Scholar 

  • Li, B., Wei, J., Wang, L., Ma, B., & Xu, M. (2019). A comparative analysis of two point cloud volume calculation methods. International Journal of Remote Sensing, 40(8), 3227–3246.

    Article  Google Scholar 

  • Lumme, J., Karjalainen, M., Kaartinen, H., Kukko, A., Hyyppä, J., Hyyppä, H., Jaakkola, A., & Kreemola, J. (2008). Terrestrial laser scanning of agricultural crops. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B5), 563–566.

    Google Scholar 

  • Márquez, C., & De Michelis, A. (2009). Comparison of drying kinetics for small fruits with and without particle shrinkage considerations. Food and Bioprocess Technology, 4(7), 1212–1218.

    Article  Google Scholar 

  • Moscetti, R., Saeys, W., Keresztes, J. C., Goodarzi, M., Cecchini, M., Danilo, M., & Massantini, R. (2015). Hazelnut quality sorting using high dynamic range short-wave infrared hyperspectral imaging. Food and Bioprocess Technology, 8(7), 1593–1604.

    Article  Google Scholar 

  • Ngapo, T., Dransfield, E., Martin, J.-F., Magnusson, M., Bredahl, L., & Nute, G. (2004). Consumer perceptions: Pork and pig production. Insights from France, England, Sweden and Denmark. Meat Science, 66(1), 125–134.

    Article  CAS  Google Scholar 

  • Offer, G., & Trinick, J. (1983). On the mechanism of water holding in meat: The swelling and shrinking of myofibrils. Meat Science, 8(4), 245–281.

    Article  CAS  Google Scholar 

  • O'Mahony, R., Cowan, C., & Keane, M. (1991). Consumer preferences for pork chops with different levels of intramuscular fat. Food Quality and Preference, 3(4), 229–234.

    Article  Google Scholar 

  • Paulus, S., Schumann, H., Kuhlmann, H., & Léon, J. (2014). High-precision laser scanning system for capturing 3D plant architecture and analysing growth of cereal plants. Biosystems Engineering, 121, 1–11.

    Article  Google Scholar 

  • Purslow, P., Oiseth, S., Hughes, J., & Warner, R. D. (2016). The structural basis of cooking loss in beef: Variations with temperature and ageing. Food Research International, 89(part 1), 739–748.

    Article  CAS  Google Scholar 

  • Sanders, D. R., Wanki, M., & Kuethe, T. H. (2007). Consumer willingness-to-pay for fresh pork attribute. Journal of Agribusiness, 25(2), 163–179.

    Google Scholar 

  • Schoeman, L., Williams, P., du Plessis, A., & Manley, M. (2016). X-ray micro-computed tomography (μCT) for non-destructive characterisation of food microstructure. Trends in Food Science & Technology, 47, 10–24.

    Article  CAS  Google Scholar 

  • Siripon, K., Tansakul, A., & Mittal, G. S. (2007). Heat transfer modeling of chicken cooking in hot water. Food Research International, 40(7), 923–930.

    Article  Google Scholar 

  • Tello, J., Cubero, S., Blasco, J., Tardaguila, J., Aleixos, N., & Ibáñez, J. (2016). Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters. Journal of the Science of Food and Agriculture, 96(13), 4575–4583.

    Article  CAS  Google Scholar 

  • Tilly, N., Hoffmeister, D., Liang, H., Cao, Q., Liu, Y., Lenz-Wiedemann, V., Miao, Y., & Bareth, G. (2012). Evaluation of terrestrial laser scanning for rice growth monitoring. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39, B7.

    Google Scholar 

  • Tornberg, E. (2005). Effects of heat on meat proteins–implications on structure and quality of meat products. Meat Science, 70(3), 493–508.

    Article  CAS  Google Scholar 

  • Uyar, R., & Erdoğdu, F. (2009). Potential use of 3-dimensional scanners for food process modeling. Journal of Food Engineering, 93(3), 337–343.

    Article  Google Scholar 

  • Verbeke, W., Demey, V., Bosmans, W., & Viaene, J. (2005). Consumer versus producer expectations and motivations related to “superior” quality meat: Qualitative research findings. Journal of Food Products Marketing, 11(3), 27–41.

    Article  Google Scholar 

  • Warner, R., McDonnell, C. K., Bekhit, A., Claus, J., Vaskoska, R., Sikes, A., Dunshea, F. R., & Ha, M. (2017). Systematic review of emerging and innovative technologies for meat tenderisation. Meat Science, 132, 72–89.

    Article  CAS  Google Scholar 

  • Zheng, C., Sun, D.-W., & Zheng, L. (2007). Predicting shrinkage of ellipsoid beef joints as affected by water immersion cooking using image analysis and neural network. Journal of Food Engineering, 79(4), 1243–1249.

    Article  Google Scholar 

Download references

Acknowledgments

R. Vaskoska acknowledges an Australian Government Research Training Program (RTP) Scholarship.

Funding

No funding received for the research. 

Author information

Authors and Affiliations

  1. School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Building 184, Royal Parade, Parkville, VIC, 3010, Australia

    Rozita Vaskoska, Minh Ha, Jason D. White & Robyn D. Warner

  2. Department of Infrastructure Engineering, University of Melbourne, Parkville, VIC, 3010, Australia

    Ha Thi Thu Tran & Kourosh Khoshelham

  3. Charles Sturt University, Wagga Wagga, NSW, 2650, Australia

    Jason D. White

Authors
  1. Rozita Vaskoska
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Minh Ha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ha Thi Thu Tran
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kourosh Khoshelham
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jason D. White
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Robyn D. Warner
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rozita Vaskoska.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vaskoska, R., Ha, M., Tran, H.T.T. et al. Evaluation of 3D Laser Scanning for Estimation of Heating-Induced Volume Shrinkage and Prediction of Cooking Loss of Pork Cuboids Compared to Manual Measurements. Food Bioprocess Technol 13, 938–947 (2020). https://doi.org/10.1007/s11947-020-02421-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11947-020-02421-0

Keywords

Search

Navigation