Food process modelling

modelling: fundamental approaches 1 The power and pitfalls of deductive modelling B. P. Hills, Institute of Food Research, Norwich 1.1 Introduction 1.2 Deductive modelling and process optimization 1.3 Modelling the keeping-quality and shelf-life of foods 1.4 Deductive modelling of flavour release from foods 1.5 Future trends 1.6 References 2 Problem decomposition M. Sloof, Everest B. V.,
s-Hertogenbosch 2.1 Introduction 2.2 Decomposition in information technology 2.3 Modelling food processes 2.4 Benefits for modelling food processes 2.5 Future trends 2.6 References 3 Kinetic modelling M. A. J. S. van Boekel, Wageningen University, and L. M. M. Tijskens, ATO-BV, Wageningen 3.1 Introduction 3.2 Key principles and methods Contents © 2001 Woodhead Publishing Ltd. 3.3 Areas of application 3.4 Pros and cons of kinetic modelling 3.5 Future trends 3.6 References 4 The modelling of heat and mass transfer B. M. Nicolaı¨, P. Verboven, N. Scheerlinck, Katholieke Universiteit Leuven 4.1 Introduction 4.2 The diffusion equation 4.3 The Navier-Stokes equations 4.4 Heat and mass transfer in porous media: Luikov’s equations 4.5 Numerical methods 4.6 Conclusions 4.7 Acknowledgements 4.8 References 5 Combined discrete/continuous modelling R. W. Sierenberg 5.1 Introduction: the big gap 5.2 The power of parallel processes 5.3 The ‘world view’ of system theory 5.4 Combined modelling 5.5 Pitfalls 5.6 Conclusions and trends Part II The principles of modelling: empirical approaches 6 The power and pitfalls of inductive modelling F. Verdenius, ATO BV, Wageningen and L. Hunter, University of Colorado, School of Medicine 6.1 Introduction 6.2 Key principles and methods 6.3 Pros and cons of inductive modelling 6.4 Future trends 6.5 References 7 Data mining G. Holmes and T. C. Smith, University of Waikato, Hamilton 7.1 Introduction 7.2 Input characteristics 7.3 Data engineering methods 7.4 Output representations 7.5 Data mining methods 7.6 Output evaluation 7.7 Concluding remarks 7.8 References © 2001 Woodhead Publishing Ltd. 8 Modelling and prediction in an uncertain environment J. F. Van Impe, K. Bernaerts, A. H. Geeraerd, F. Poschet and K. J. Versyck, Katholieke Universiteit Leuven 8.1 Introduction 8.2 Data (pre)processing 8.3 Model structure characterisation 8.4 Model parameter estimation 8.5 Model output uncertainty assessment 8.6 Conclusions 8.7 Appendix A 8.8 Appendix B 8.9 References Part III Applications: agricultural production 9 Yield and quality prediction of vegetables: the case of cucumber L. F. M. Marcelis, Plant Research International, Wageningen 9.1 Introduction 9.2 Key principles and methods 9.3 Areas of application 9.4 How generic are crop models? 9.5 Some future trends 9.6 Summary 9.7 References 10 Modelling and management of fruit production: the case of tomatoes C. Gary and M. Tchamitchian, INRA, Avignon 10.1 Introduction: the contexts of tomato production 10.2 Processes and methods of modelling tomato crops 10.3 Areas of application 10.4 Discussion of the methods and future trends 10.5 References 11 Dairy production C. F. E. Topp, Scottish Agricultural College, Ayr 11.1 Introduction 11.2 The model structure 11.3 Conclusions 11.4 Acknowledgements