Non-contact volumetric shrinkage measurement of horticultural products during thin-layer drying using 3D laser imaging and mathematical modeling

Abstract Shrinkage significantly affects the quality of dried thin-layer products, but conventional measurement methods are destructive and unsuitable for online monitoring. In this study, a non-contact imaging system was developed using laser triangulation and mathematical modeling to measure volumetric shrinkage during thin-layer drying. The system combines a linear motion module with a PID controller and a laser scanner to capture 3D surface area data. A multilayer perceptron neural network model predicted layers’ thickness as a function of time, temperature, and initial thickness, achieving high correlation coefficients (R) of 0.987 (carrot), 0.995 (banana), and 0.993 (apple) during testing. Instantaneous volume was calculated using scanned surface area and predicted thickness. Validation against pycnometer-measured volumetric shrinkage, performed on independent slices at each time point, yielded strong agreement, with R and RMSE values of 0.988/3.82 (carrot), 0.950/4.97 (banana), and 0.982/3.23 (apple). The method performed with high accuracy for carrot and apple but showed slightly lower accuracy for banana, which may be attributed to browning-induced laser photon absorption that potentially reduces point cloud accuracy in darker areas. This non-contact approach offers a promising solution for real-time drying process monitoring, particularly for thin-layer products, with potential applications in quality control and efficiency enhancement for horticultural moving-bed drying systems.