Lightweight Deep Learning Framework for Quality Assessment of Rolled Fondant

Q: What does this research mean for the field?

A CIELAB-based lightweight convolutional neural network utilizing semantic segmentation provides highly reliable food colorimetry and quality assessment for rolled fondant, outperforming traditional bounding-box models like YOLO and enabling edge-device deployment. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Esteban Rodríguez; George Pauccar; Carlos H. Inga Espinoza; José Carrizales

doi:10.1016/j.lwt.2026.119580

May 1, 2026LWTOpen Access

Lightweight Deep Learning Framework for Quality Assessment of Rolled Fondant

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Key Points

To develop a lightweight deep learning framework that assesses the quality of rolled fondant using color metrics.
Utilized CIELAB-based lightweight convolutional neural networks (CNNs) to analyze food colorimetry.
Conducted statistical validation to ensure metrological reliability with a 95% confidence interval.
Implemented semantic segmentation techniques to improve handling of continuous textures.
Parameter-efficient models with less than 800k parameters outperformed YOLO in reliability.
Achieved successful segmentation of textures, overcoming bounding-box saturation issues.
Validated edge-device capability for final-state quality control in food products.

Implication

Demonstrates effective quality assessment of rolled fondant using lightweight deep learning frameworks, suggesting potential for edge-device applications.

Abstract

• CIELAB-based lightweight CNNs decouple luminance for robust food colorimetry. • Parameter-efficient models (<800k) outclass YOLO in metrological reliability. • Semantic segmentation overcomes bounding-box saturation in continuous textures. • Statistical validation (95% CI) proves edge-device viability for final-state QC.

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Cite This Study

Rodríguez et al. (2026) studied this question.

synapsesocial.com/papers/6a1d224302fbce9130638094 https://doi.org/10.1016/j.lwt.2026.119580

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Lightweight Deep Learning Framework for Quality Assessment of Rolled Fondant

Key Points

Implication

Abstract

Discussion

Cite This Study

Lightweight Deep Learning Framework for Quality Assessment of Rolled Fondant

Key Points

Implication

Abstract

Discussion

Cite This Study

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