Scientific Report Analysis
Prioritizing neglected food species in nutritional studies using expert-knowledge and explainable AI
Authored by: Michelle Cristine Medeiros Jacob, Aline Martins Carvalho, Ângela Giovana Batista, Aníbal Freitas Santos Júnior, Antonio Augusto Ferreira Carioca, Celia Márcia Medeiros Morais, Cinthia Baú Betim Cazarin, Daniel Tregidgo, Danilo Vicente Batista Oliveira, Dirce Maria Lobo Marchioni, Eliana Bistriche Giuntini, Elias Jacob Menezes-Neto, Fillipe Oliveira Pereira, Gabriela Farias Moura, Hani R. El Bizri, Ingrid Wilza Leal Bezerra, Jailane Souza Aquino, João Victor Mendes Silva, Josiane Steluti, Juliana Kelly Silva-Maia, Juliana Araujo Teixeira, Lara Juliane Guedes Silva, Letícia Zenóbia Oliveira Campos, Marcela Alvares Oliveira, Maria Elieidy Gomes Oliveira, Mariana Paula Drewinski, Marina Maintinguer Norde, Nelson Menolli, Priscila F. M. Lopes, Rafael Ricardo Vasconcelos Silva, Rômulo Romeu Nóbrega Alves, Samara Camile Gomes Silva, Sávio Marcelino Gomes, Severina Carla Vieira Cunha Lima, Thais Q. Morcatty & Ulysses Paulino Albuquerque
Received: 25 May 2025 | Accepted: 5 February 2026 | Published online: 02 March 2026
This study pioneers an innovative, AI-driven framework for prioritizing neglected food species for nutritional research in Brazil. By combining expert knowledge with explainable AI, we identify critical factors for research prioritization, addressing significant data gaps and promoting sustainable food systems.
Executive Impact Summary
Leveraging a sophisticated blend of expert knowledge and LightGBM with SHAP analysis, this research provides a data-driven blueprint for prioritizing food biodiversity studies. Our findings accelerate the integration of neglected species into sustainable food systems, offering significant opportunities for public health and ecological conservation.
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Neglected Food Species Identified
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Recipes Documented
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Nutritional Data Gap
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AI Models Leveraged
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Enterprise Process Flow
Start
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Assemble Core Experts panel
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First draft
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Create neglected food list
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Select features
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Collect data
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Form Ad Hoc Panel
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Conduct prioritization
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Rate confidence
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Group discussion
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Final writing
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End
369
Neglected Food Species Identified
This study cataloged 369 neglected food species across diverse categories such as plants, wild terrestrial vertebrates, and mushrooms, forming a crucial foundation for future research in Brazil. Plants and wild vertebrates predominated the inventory.
36,058
Recipes Documented
Over 36,000 recipes using neglected species were identified, highlighting their cultural relevance and potential for broader dietary integration. This extensive culinary documentation provides unique insights into local food uses.
66.9%
Nutritional Data Gap
A significant data gap was found, with only 33.1% of identified species having available nutritional information. This gap is particularly pronounced for algae, insects, and wild vertebrates, underscoring urgent research needs.
AI-Driven Prioritization Unveiled
Our explainable AI model (LightGBM with SHAP analysis) revealed that the number of recipes and species occurrence across different states were the most influential factors in prioritizing neglected food species for nutritional studies.
This highlights the practical utility and accessibility as primary drivers for research attention. Interestingly, ecological and conservation status played a less significant role, indicating a potential disconnect between immediate research priorities and long-term biodiversity goals. Distinct perspectives were observed, with environmental scientists favoring wider geographic distribution and nutritionists emphasizing documented culinary uses.
This AI insight is critical for stakeholders to understand how current priorities are formed and to guide future strategies towards a more holistic approach.
Addressing Gaps & Fostering Sustainable Diets
This study underscores several critical challenges, including significant nutritional data gaps, cultural barriers to wider adoption of certain food groups (like insects and algae), and a disconnect between research prioritization and conservation needs.
We recommend a multi-pronged approach:
- Invest in Interdisciplinary Studies: Bridge knowledge gaps in nutritional composition and ecological impact for neglected species.
- Develop National Databases: Create integrated platforms for culinary uses and nutritional data to enhance accessibility for researchers and policymakers.
- Promote Diverse Food Integration: Incorporate biodiverse foods into public health programs and training for nutrition professionals to align dietary diversity with environmental sustainability.
- Consider Conservation Status: Implement measures like promoting certified agroecological production and educating consumers on sustainable sourcing.
These strategic actions can help integrate neglected food species into sustainable food systems, improving human health and ecological resilience.
Calculate Your Potential AI-Driven ROI
See how leveraging AI for data analysis and prioritization can translate into significant operational efficiencies and strategic advantages for your organization.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A structured approach to integrate AI-driven prioritization, tailored to your organization's unique needs and objectives.
Phase 01: Data & Expertise Integration
Collaborate to identify your specific research goals and existing datasets. Integrate expert knowledge from your domain specialists, mirroring the multi-disciplinary approach of this study.
Phase 02: Custom Model Development
Tailor a LightGBM and SHAP-based model to your data, identifying the most influential features for your prioritization challenges. This ensures relevance and accuracy for your specific context.
Phase 03: Explainable Insights & Strategy
Leverage SHAP analysis to provide transparent, actionable insights into prioritization drivers. Develop clear strategies for resource allocation based on model predictions and expert validation.
Phase 04: Continuous Optimization
Implement monitoring and feedback loops to continuously refine the AI model, adapting to new data and evolving research priorities, ensuring long-term strategic advantage.
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