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Updated: Jun 27, 2026

Source and Route of Pyrrolizidine Alkaloid Contamination in Tea Samples
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Nondestructive Detection of Foreign Matter in Pu-erh Ripe Tea Based on Deep Learning.

Baijuan Wang1,2, Xiaoxue Guo1,3, Xin Fang1

  • 1College of Tea Science, Yunnan Agricultural University, Kunming 650201, China.

Foods (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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This summary is machine-generated.

This study introduces AE-YOLOv13-S, an AI model inspired by primate vision, to accurately detect foreign matter in Pu-erh tea. The enhanced network improves precision and recall for better food safety.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Food Science

Background:

  • Detecting small, occluded foreign matter in Pu-erh ripe tea is challenging due to complex backgrounds.
  • Existing methods struggle with fine details, feature discrimination, and accurate localization, leading to missed or false detections.

Purpose of the Study:

  • To develop an improved YOLOv13-based network, AE-YOLOv13-S, for efficient and accurate foreign matter detection in Pu-erh ripe tea processing.
  • To enhance detection capabilities by integrating primate visual mechanism-inspired modules.

Main Methods:

  • Implemented an Adaptive Sparse Self-Attention Network to optimize the backbone, inspired by primate cognitive patterns for target search and verification.
  • Integrated Emulating Self-Attention with Convolution to refine Conv modules, mimicking primate hierarchical information processing.
Keywords:
convolutionfood safetyforeign matter detectionteavisual mechanism

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  • Introduced a Scale-based Dynamic Loss function, guided by primate visual perception, to improve bounding box accuracy and model convergence.

    • Main Results:

    • AE-YOLOv13-S demonstrated reduced training losses (Box Loss: 6.76%, Cls Loss: 6.52%, DFL Loss: 8.65%) compared to the baseline.
    • Achieved significant reductions in validation metrics (6.58% to 16.39%) and increases in precision (1.43 pp), recall (4.85 pp), and mAP@50 (2.69 pp).
    • The model shows efficient and accurate classification and detection with minimal increase in computational cost (0.3 G FLOPs).

    Conclusions:

    • The AE-YOLOv13-S model offers a novel technical approach for foreign matter detection in tea processing.
    • Provides a practical solution for intelligent quality control and food safety assurance in the tea industry.
    • The primate vision-inspired enhancements significantly improve detection accuracy and efficiency.