1. Evaluation of 3D Laser Scanning for Estimation of Heating-Induced Volume Shrinkage and Prediction of Cooking Loss of Pork Cuboids Compared to Manual Measurements
- Author
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Ha Thi Thu Tran, Kourosh Khoshelham, Minh Ha, Robyn D. Warner, Jason D. White, and Rozita Vaskoska
- Subjects
0106 biological sciences ,Materials science ,Cuboid ,Laser scanning ,Process Chemistry and Technology ,3D reconstruction ,04 agricultural and veterinary sciences ,Laser ,040401 food science ,01 natural sciences ,Industrial and Manufacturing Engineering ,law.invention ,Perimeter ,0404 agricultural biotechnology ,Volume (thermodynamics) ,law ,010608 biotechnology ,Calipers ,Safety, Risk, Reliability and Quality ,Food Science ,Shrinkage ,Biomedical engineering - 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 0.05) 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.
- Published
- 2020
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