Abstract：
The surface appearance of milk powders is a crucial quality property since the roughness of the milk powder determines its functional properties, and especially the purchaser perception of the milk powder. Unfortunately, powder produced from similar spray dryers, or even the same dryer but in different seasons, produces powder with a wide variety of surface roughness. To date, professional panelists are used to quantify this subtle visual metric, which is time-consuming and subjective. Consequently, developing a fast, robust, and repeatable surface appearance classification method is essential. This study proposes a three-dimensional digital photogrammetry technique for quantifying the surface roughness of milk powders. A contour slice analysis and frequency analysis of the deviations were performed on the three-dimensional models to classify the surface roughness of milk powder samples. The result shows that the contours for smooth-surface samples are more circular than those for rough-surface samples, and the smooth-surface samples had a low standard deviation; thus, milk powder samples with the smoother surface have lower Q (the energy of the signal) values. Lastly, the performance of the nonlinear support vector machine (SVM) model demonstrated that the technique proposed in this study is a practicable alternative technique for classifying the surface roughness of milk powders.
摘要：
奶粉的表面外观是至关重要的质量特性，因为奶粉的粗糙度决定了其功能特性，尤其是购买者对奶粉的看法。不幸的是,从类似的喷雾干燥器生产的粉末，甚至是相同的烘干机，但在不同的季节，产生各种各样的表面粗糙度的粉末。迄今为止,专业小组成员被用来量化这个微妙的视觉指标，这是耗时和主观的。因此,快速发展，健壮,可重复的表面外观分类方法至关重要。本研究提出了一种用于量化奶粉表面粗糙度的三维数字摄影测量技术。对三维模型进行了轮廓切片分析和偏差频率分析，以对奶粉样品的表面粗糙度进行分类。结果表明，光滑表面样品的轮廓比粗糙表面样品的轮廓更圆形，并且光滑表面的样品具有较低的标准偏差；因此，具有较光滑表面的奶粉样品具有较低的Q(信号的能量)值。最后,非线性支持向量机（SVM）模型的性能表明，本研究中提出的技术是对奶粉表面粗糙度进行分类的可行替代技术。
