Multi-attribute prediction of protein composition and hardness using semantic segmentation for 3D-Printed plant-based meat analogues.

Sol Kim, Ji-Hoe Kim, Jaehwi Seol, Jeonghyeon Pak, Hyoung Il Son, Soo-Jung Kim

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Article in Current research in food science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Sol KimDepartment of Integrative Food, Bioscience and Biotechnology, Chonnam National University, Gwangju, 61186, Republic of Korea.

Ji-Hoe KimDepartment of Food Science and Technology, College of Agriculture and Life Sciences, Chonnam National University, Gwangju, 61186, Republic of Korea.

Jaehwi SeolDepartment of Convergence Biosystems Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju, 61186, Republic of Korea.

Jeonghyeon PakDepartment of Convergence Biosystems Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju, 61186, Republic of Korea.

Hyoung Il SonDepartment of Convergence Biosystems Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju, 61186, Republic of Korea.

Soo-Jung KimDepartment of Integrative Food, Bioscience and Biotechnology, Chonnam National University, Gwangju, 61186, Republic of Korea.

Funding

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6 · The paper itself

Abstract

Three-dimensional (3D) food printing enables layer-by-layer fabrication of plant-based meat analogues (PMAs), but reliable in-process quality evaluation remains challenging. This study developed a multi-attribute deep learning framework for the non-destructive, in-process prediction of formulation-derived protein composition and hardness from RGB surface images captured during printing. The target attributes were wheat gluten (WG), soy protein isolate (SPI), rice protein (RP), and hardness. A DeepLabv3+ model with a ResNet-50 backbone was used to segment printability-related structural features, including pores, aggregation, and spreading, achieving a mean intersection over union (mIoU) of 0.558 and mean accuracy (mAcc) of 0.620. RGB images and segmentation masks were then used as inputs to an EfficientNet-Bidirectional Long Short-Term Memory model for multi-output prediction. Fivefold cross-validation showed R

Indexed as

3D food printingHardnessMulti-attribute predictionNon-destructive methodPlant-based meat analoguesProtein compositionSemantic segmentation

Identifiers

PMID42253341

DOI10.1016/j.crfs.2026.101458

PMCPMC13235374

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