Abstract
Food safety requires repeatability and precision in food processing and optimal signal-to-noise ratio, that is, robustness to environmental variables and interfering parameters in food processing, and processes must be traceable. A thermal process is controlled according to required temperature curves by methods from various areas of process control theory. Using the case study of industrially produced soft-boiled eggs with simultaneous pasteurization (disabling of Salmonella in the egg yolk), we demonstrate technological progress of the precise temperature process control in foods industry. The simplest thermal process control is implemented with on/off regulation of heating and cooling. Accuracy is improved with the introduction of proportional, integral, and derivative (PID) control. Fuzzy control is now used in many thermal process controls. The current state of the art is the use of artificial intelligence (AI) where we train a neural network in several iterations under different conditions. The trained neural network controls the thermal process according to the required sequence. Such a control is most insensitive to environment variables by its design. We present the drawbacks and complexity of individual approaches to precise thermal control in the food industry. One must note that the matter of the case study—egg’s pasteurization and preservation of yolk softness—have opposing temperature requirements, and coexistence of the two is not granted.