Noncontact Detection Method for Food Texture Assessment Based on Air Puff Combined With Structured L

Given the shortcomings of subjectivity and destructiveness associated with traditional methods of measuring food texture parameters (such as fruit firmness and meat tenderness) when using a texture meter, a noncontact measurement method utilizing an air puff combined with structured light imaging (ASLI) was proposed in this study. First, the finite-element simulation method was used to simulate the airflow impact model and optimize the ranges of the angle and distance parameters. The detection device was then developed, and binocular 3-D phenotype acquisition and 3-D feature extraction algorithms were investigated. Denoising, point cloud segmentation, greedy projection triangulation, Delaunay triangulation, and surface fitting algorithms were used to process the point cloud, and concave parameters, such as depth, mapping area, surface area, and volume of the concave area on the beef surface, were obtained. Finally, four typical samples with different meat (beef and chicken) and fruit (kiwifruit and peach) parameters were tested. Five machine-learning methods were used to model and analyze the correlation among texture parameters. The results revealed that the model established by the ensemble learning modeling method with extracted concave parameters possessed the highest accuracy. The prediction accuracy of the shear force values of the beef and chicken samples was 0.92 and 0.91, respectively. The accuracy of meat and fruit grading was 0.942 and 1.0, respectively. Therefore, the noncontact detection method proposed in this study to determine food texture characteristics based on airflow can be used to replace the traditional texture analyzer in food engineering and exhibits good application potential.