Milk powders produced from similar spray dryers have different visual appearances, while the surface appearance of the powder is a key quality attribute because the smoothness of the milk powder also affects flowability and handling properties. Traditionally quantifying this nuanced visual metric was undertaken using sensory panelists, which is both subjective and time consuming. Therefore, it is advantageous to develop an on-line quick and robust appearance assessment tool. The aim of this work is to develop a classification model which can classify the milk powder samples into different surface smoothness groups. This work proposes a strategy for quantifying the relative roughness of commercial milk powder from 3D images. Photogrammetry equipment together with the software RealityCapture were used to build 3D models of milk powder samples, and a surface normal analysis which compares the area of the triangle formed by the 3 adjacent surface normals or compares the angle between the adjacent surface normals was used to quantify the surface smoothness of the milk powder samples. It was found that the area of the triangle of the smooth-surface milk powder cone is smaller than the area of the triangle of the rough-surface milk powder cone, and the angle between the adjacent surface normals of the rough-surface milk powder cone is larger than the angle between the adjacent surface normals of the smooth-surface milk powder cone, which proved that the proposed area metrics and angle metrics can be used as tools to quantify the smoothness of milk powder samples. Finally, the result of the support vector machine (SVM) classifier proved that image processing can be used as a preliminary tool for classifying milk powder into different surface texture groups.
Keywords: 3D image analysis; milk powder; photogrammetry; surface normal analysis; surface smoothness.