Sterilization Of Canned Foods Research Articles

Intelligent Control Based on Reinforcement Learning for Batch Thermal Sterilization of Canned Foods

A control technique based on Reinforcement Learning is proposed for controlling thermal sterilization of canned food. Without using an a-priori mathematical model of the process, the proposed Model-Free Learning Controller (MFLC) aims to follow a temperature profile during two stages of the process: first heating by manipulating the saturated steam valve and then cooling by opening the water valve) by learning. From the defined state-action space, the MFLC agent learns the environment interacting with the process batch to batch and then using a tabular state-action mapping. The results show the advantages of the proposed technique for this kind of processes.

Computer Simulation of On-Line Control of Thermal Sterilization of Canned Foods Subjected to Arbitrary Retort Temperature Deviations

A computer control logic algorithm has been developed to compensate for the effects of arbitrary retort temperature deviations on lethality. The algorithm is based on calculating the can center temperature for any retort temperature profiles using an alternating direction implicit finite difference method. Lethal rate and corresponding accumulated lethality were calculated during the simulation process. Five types of process deviations were investigated including one with multiple deviations. All the deviations investigated resulted in serious under processing with relative errors in sterilization value ranges from 16.9 % to 43.2 %. A correction measure for process with retort temperature deviations based on targeting the lethal rate of the standard process at the end of the heating phase was proposed. The implementation of this measure resulted in successful correction for all the deviations investigated with a maximum relative error in sterilization value of 9.5 % on the over processing side. This work indicates that the current control algorithm could be incorporated in HACCP guideline, as an online corrective measure for retort temperature deviations during thermal sterilization process.

A Computer Simulation of Thermal Sterilization of Canned Foods With Sub-Freezing Initial Temperatures

Certain types of canned foods are best handled as a frozen slurry and retorting such products from an initially frozen state would be advantageous from the standpoint of materials handling. However, there is currently little information on this process. Accordingly, a computer simulation using finite difference techniques was developed to determine the added process time required to achieve commercial sterilization. Total required processing time could not be approximated as being the sum of the thawing time and the sterilization time as calculated by conventional methods. Total concentration of heat labile nutrient was found to be unaffected by the process and was independent of initial product temperature, despite the longer exposure of the product to high retort temperatures.

Real‐Time lethality Evaluation During the Flame Sterilization of Canned Foods

A pilot‐scale flame sterilizer was interfaced to a microcomputer for real‐time thermal process evaluation of individual cans. The computerized system used a photodetector system to synchronize data acquisition at predetermined monitoring sites. Infrared sensors were used to measure can temperatures. Also, a method to measure can surface temperature was developed for infrared sensor calibrations. Foil thermocouples were found to be the most accurate and reliable for surface temperature measurements. However, a thin gauge wire thermocouple attached to the inside of the can end surface was a more practical and economical method for sensor calibration.