Ultrasonic mode conversion for in-line foam structure measurement in highly aerated batters using machine learning

An ultrasonic-based method was developed to enable in-line measurements of foam structure parameters for highly aerated batters by mode conversion. Biscuit batters were foamed to different degrees (density: 364–922 g/L) by varying the mixing head speed and pressure. Density and foam structure changes were detected by efficient offline analytics (nref measurement = 96). Ultrasonic signal data were recorded using two ultrasonic sensors attached to an industry-standard tube. Mode conversion effects in the ultrasonic signals were obtained to predict the rheological parameters of the batters. The frequency range in which surface waves are expected was particularly suitable for detecting rheological changes in highly aerated batters. An ultrasonic-based, online-capable method for process monitoring was implemented and established regarding feature selection in combination with machine learning and 5-fold cross-validation. The developed ultrasonic sensor system shows high accuracy for online density measurement (R2 = 0.98) and offers decent accuracy for measurements of foam structure parameters (Bubble count: R2 = 0.95, Relative span: R2 = 0.93, Sauter diameter: R2 = 0.83). The main benefit of this novel technique is that integrating ultrasonic signal features based on mode conversion leads to a robust foam structure analysis, which has the advantage of being retrofitable into existing processes.