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Assessing and Quantifying the Surface Texture of Milk Powder Using Image Processing.

Haohan Ding1,2, David I Wilson3, Wei Yu2

  • 1Science Center for Future Foods, Jiangnan University, Wuxi 214122, China.

Foods (Basel, Switzerland)
|May 28, 2022
PubMed
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This study developed a 3D imaging method to objectively measure milk powder surface smoothness. This technique offers a faster, more reliable alternative to subjective sensory analysis for quality control.

Area of Science:

  • Food Science
  • Image Processing
  • Quality Control

Background:

  • Milk powder's visual appearance, specifically surface smoothness, is a critical quality attribute impacting flowability and handling.
  • Traditional methods for assessing milk powder texture rely on subjective and time-consuming sensory panel evaluations.
  • There is a need for objective, rapid, and robust on-line tools for milk powder appearance assessment.

Purpose of the Study:

  • To develop a classification model for categorizing milk powder samples into distinct surface smoothness groups.
  • To propose and validate a novel strategy for quantifying milk powder surface roughness using 3D imaging techniques.

Main Methods:

  • Utilized photogrammetry equipment and RealityCapture software to generate 3D models of milk powder samples.
Keywords:
3D image analysismilk powderphotogrammetrysurface normal analysissurface smoothness

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  • Employed surface normal analysis, comparing adjacent surface normal triangle areas and angles, to quantify surface smoothness.
  • Developed a support vector machine (SVM) classifier to evaluate the effectiveness of image processing for texture classification.

    • Main Results:

    • Quantitative analysis revealed that smooth milk powder surfaces exhibit smaller triangle areas and smaller angles between adjacent surface normals compared to rough surfaces.
    • The proposed area and angle metrics effectively quantified milk powder surface smoothness.
    • The SVM classifier demonstrated the potential of image processing for classifying milk powder into different surface texture groups.

    Conclusions:

    • 3D imaging combined with surface normal analysis provides a viable method for objectively quantifying milk powder surface smoothness.
    • Image processing offers a promising preliminary tool for classifying milk powder based on surface texture, improving quality control efficiency.