An image-based food quality analysis framework driven by large language models

Xiao-Yue Yin; HL Wu; Luo-Yuan Han; Ye He; X G Wang; T H Wang; Ru-Qin Yu

doi:10.1039/d6ay00214e

January 1, 2026Analytical Methods

An image-based food quality analysis framework driven by large language models

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Key Points

To develop a framework for food quality analysis leveraging large language models and visual inspection.
Created a GUI for one-click automation of food quality assessment.
Implemented two inference paths: text-only and vision-language.
Models evaluated on tasks for ripeness, freshness, and authenticity.
Framework automates the inspection process for food quality.
Demonstrated effectiveness in assessing ripeness and authenticity.

Implication

An image-based framework improves food quality assessment for ripeness and authenticity, suggesting automation benefits.

Abstract

LLM-based visual inspection framework with two paths: text-only inference and vision-language inference. A lightweight GUI enables one-click automation. Models are evaluated on ripeness, freshness, and authenticity tasks.

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Cite This Study

Yin et al. (2026) studied this question.

synapsesocial.com/papers/69f154f9879cb923c494553b https://doi.org/10.1039/d6ay00214e

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An image-based food quality analysis framework driven by large language models

Key Points

Implication

Abstract

Discussion

Cite This Study

An image-based food quality analysis framework driven by large language models

Key Points

Implication

Abstract

Discussion

Cite This Study

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