System for automated monitoring of local soil removal during cleaning in closed food processing lines with a quartz crystal sensor

Abstract

AbstractUsing quartz crystal microbalances (QCM) to monitor cleaning processes creates new opportunities towards efficient and need-based cleaning processes. A first study with starch as food model soiling showed the suitability of a QCM-based sensor concept to detect swellable substances and monitor the cleaning process [1]. The results of a follow-up study will be presented in this manuscript. Cleaning tests were conducted at 40 and 55 °C while monitoring a range of characteristics (peak height, attenuation, integral, tangent incline, turning point) in the sensor signal course. An optical sensor, monitoring the cleaning process was used as a reference. Based on this, the most promising parameters (peak height, attenuation, tangent incline) were selected to develop an evaluation algorithm counteracting the sensor cross-sensitivity towards process parameters such as temperature and influences of the cleaning fluid. The algorithm can determine whether the sensor surface is clean or soiled. Cleaning tests with tomato paste and milk proved the sensor’s ability to detect industrial food products with an average deviation of 68,0 s (tomato paste) and 43,8 s (milk) from the reference sensor.