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Related Concept Videos

Pore Size Distribution01:23

Pore Size Distribution

In concrete, the pore size distribution significantly influences the material's properties. Capillary pores, markedly larger than gel pores, form a vast network within partially hydrated cement paste, reducing the concrete's strength and increasing its permeability. This heightened permeability leads to a greater risk of damage from environmental factors like freeze-thaw cycles and chemical attacks, with the extent of vulnerability also being tied to the water-to-cement ratio.
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In precipitation gravimetry, the precipitating agent should react specifically or selectively with the analyte. While a specific reagent reacts with the analyte alone, a selective reagent can react with a limited number of chemical species.
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Related Experiment Video

Updated: Jul 13, 2026

Analysis and Specification of Starch Granule Size Distributions
08:46

Analysis and Specification of Starch Granule Size Distributions

Published on: March 4, 2021

Rapid Starch Particle Sizing by YOLOv8n.

Xuyan Zhao1, Hanwen Niu2, Wenlu Zhu2

  • 1School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou, Henan, 450001, PR China.

Current Research in Food Science
|July 12, 2026
PubMed
Summary

This study introduces an improved YOLOv8n network for rapid starch particle size analysis, achieving high accuracy and efficiency. The AI method offers a viable alternative to traditional particle size analyzers in food quality control.

Keywords:
Attention mechanismImage segmentationRapid detectionStarch particle analysisYOLOv8n

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Area of Science:

  • Food Science and Technology
  • Computer Vision
  • Image Analysis

Background:

  • Accurate starch particle size measurement is crucial for food industry quality control.
  • Traditional methods can be time-consuming or lack precision.

Purpose of the Study:

  • To develop a rapid, image-based detection and analysis method for starch particles.
  • To improve upon existing object detection networks for enhanced accuracy and efficiency.

Main Methods:

  • An improved YOLOv8n network incorporating a P2 small-object detection layer, SimAM attention module, and WIoU3 loss function was developed.
  • The model was trained and evaluated for starch particle detection and size analysis.
  • Performance was compared against baseline models, lightweight networks, and a laser particle size analyzer (LPSA).

Main Results:

  • The improved YOLOv8n model achieved an mAP@0.85 of 93.31%, outperforming the baseline by 3.50%.
  • Model parameters were reduced by 32.8%, with an inference time of 2.4 ms per image.
  • Comparison with LPSA showed a 1.85% relative error in median diameter (D50) and similar distribution trends.
  • Quantitative analysis of particle circularity was also enabled.

Conclusions:

  • The developed image-based method provides a rapid and accurate approach for starch particle characterization.
  • The improved YOLOv8n network offers a balance of segmentation accuracy and computational efficiency.
  • This method shows significant potential for particle-size and morphology analysis in food applications.