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Related Concept Videos

Sensory Modalities01:15

Sensory Modalities

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Sensation typically is the process by which the sensory receptors and sense organs detect stimuli from the internal and external environment and transmit this information to the central nervous system for processing.
General senses refer to the broad category of sensory information detected by receptors in the body and can be further grouped into somatic and visceral senses. Somatic sensations include touch, pressure, temperature, and pain and are essential for navigating our environment and...
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Long-Tea-CLIP: An Expert-Level Multimodal AI Framework for Fine-Grained Green Tea Grading Across Five Sensory

Yanqun Xu1,2, Zhengfang Xue3,4,5, Qing Luo6

  • 1Department of Food Science & Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|March 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces Long-Tea-CLIP, an AI system for evaluating tea quality and consumer experience. This artificial intelligence (AI) model achieves 92% accuracy in grading Longjing tea, improving quality control and market transparency.

Keywords:
Contrastive Language‐Image Pre‐trainingcomputer visiongreen tea gradingmetabolomicsmultimodal AIsensory evaluation

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Food Science

Background:

  • Traditional tea quality assessment relies on human evaluators, leading to scalability and consistency issues.
  • Developing an objective and scalable method for tea quality evaluation is crucial for the industry.

Purpose of the Study:

  • To develop an artificial intelligence (AI)-assisted framework for comprehensive tea quality evaluation.
  • To create a multimodal tea grading system, Long-Tea-CLIP, integrating computer vision and chemoinformatics for assessing the tea consumption experience.

Main Methods:

  • Developed Long-Tea-CLIP, a multimodal system using computer vision and chemoinformatics for tea quality assessment.
  • Integrated five sensory dimensions (appearance, soup color, aroma, infused leaf, taste) using various AI submodels (ResNet-18, XGBoost, MLP).
  • Employed Tip-CLIP supervised MLP for feature extraction from infused leaf and chemical data, weighting submodel outputs for a unified score.

Main Results:

  • The Long-Tea-CLIP system achieved 92% accuracy in grading 38 Longjing tea varieties using 7763 image-text pairs.
  • Demonstrated the system's capability in detailed assessment of tea appearance, soup color, aroma, infused leaf, and taste.
  • Successfully integrated diverse data types, including dry tea images, sensory comments, and chemical data.

Conclusions:

  • Long-Tea-CLIP offers a scalable and consistent AI-driven solution for tea quality evaluation.
  • The system has significant potential to enhance tea quality control and promote market transparency.
  • This multimodal approach provides a comprehensive assessment of the tea consumption experience.