Use of neural networks to predict roasting time and weight loss for beef joints

Abstract

A neural networks (NN) model was trained and validated using experimental data on roasting times and weight losses from beef joints. Mathematical and response surface (RS) models were also developed. Predicted results from NN and RS models were almost identical and better than the mathematical model. Using the trained NN and RS models, the effects of air temperature, dimension, weight of beef joint, its initial temperature on roasting time, and weight loss were investigated. An increase in air or initial beef temperature decreased roasting time but increased weight loss. For larger beef joints, both roasting time and weight loss increased significantly. Critical ratios of beef radius to length where roasting time and weight loss reached maximum values were found to be 0.45 using both NN and RS models for roasting time and 0.55 (NN model) or 0.6 (RS model) for weight loss. To improve productivity and reduce weight loss, small beef joints are recommended and beef joints with the critical ratios should be avoided.

Applied research and educational needs in food service management

“…Computer models (Martens and Nicolai, 1998) can be used to derive the best configuration of a unit in terms of heat flow. Programs such as ChefCad (Schellekens et al, 1994), Computational Fluid Dynamics (Verboven et al, 1999), neural networks (Xie, 2002) and a finite difference method (Lee et al, 2002) can minimise the trial and error approach in new equipment design.…”

Innovation in food service technology and its strategic role

“…In the future, the application of sophisticated modelling techniques capable of predicting the heat flow, such as Computational Fluid Dynamics (Verboven et al, 1999) and neural networks (Xie, 2002), may result in units of unusual shapes, such as a spherical cooking chamber, for example. Currently, the Steam Vector Baffling Systems developed by ACCU Temp Products Inc. (2005) accelerates and directs the steam flow using the wall geometry without fans or other moving parts.…”

Technological developments and the need for technical competencies in food services

“…Units designed for freezing with liquid nitrogen deliver frozen items with less damage to the texture. Sophisticated engineering techniques such as heat flux measurements, 43 computer modelling, 44 the computational fluid dynamics, 45 neural networks, 46 the finite difference method 47 and others are used to design superior temperature distribution systems. Future developments in this field can be directed at the incorporation of natural variability of raw materials.…”