Fruit- and vegetable-derived matrices provide nutrients, bioactive compounds, color, and flavor, but their heterogeneous composition often leads to unstable extrusion and poor shape retention. Previous reviews have generally treated these materials as a single group or summarized formulation strategies without relating matrix form to the mechanisms of print failure. Here, printable materials are organized into pulp-, juice-, and powder-based systems, and the main limitations of each system are traced from tissue disruption, particle and fiber organization, water distribution, acidity, and rehydration to rheological response, network formation, syneresis, and shape fidelity. The roles of xanthan gum, guar gum, pectin, alginate, κ-carrageenan, and carboxymethyl cellulose are then compared in terms of shear-thinning behavior, water immobilization, ionic or thermal gelation, and structural reinforcement. Emphasis is placed on matching hydrocolloid function and composite structuring strategies to the dominant defects of each matrix. By linking composition and multiscale structure with rheology and printing performance, this review offers a mechanistic basis for formulation design and the development of personalized, dysphagia-adapted, and more sustainable fruit- and vegetable-based printed foods.
Hao-Ming Tan, Saisai Guo, Bowen Li et al.· Food Research International· 0 citations
Tables are ubiquitous across diverse domains, yet reasoning over them remains a significant challenge for modern large language models (LLMs). Current approaches typically linearize tables into sequences, inherently overlooking their intrinsic two-dimensional and hierarchical structure. To address this, we propose H2Table (Hierarchical Hypergraph-Enhanced Table Reasoning), a novel framework that represents complex tables as hierarchical nested hypergraphs. To process this representation, we design a tailored hypergraph encoder to facilitate message passing between hyperedges (headers) and nodes (cells), thereby perceiving the semantic entailment relationships between them within complex tables. Furthermore, we introduce a set of learnable query vectors acting as a lightweight bridge to extract representative structural embeddings from the encoder into the LLM. Experimental results demonstrate that our approach effectively handles complex table question answering tasks with hierarchical nested headers. Notably, on the HiTab dataset, H2Table achieves an average improvement of 22.88% over state-of-the-art baselines on highly complex tables with a nesting depth of four. Our code is available at: https://github.com/lila120/h2table.
Jia Ling, Yang-Fan Wang, Chen Tang et al.· 0 citations
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