Author
Listed:
- Rifka Nakib
(Departamento de Bioloxía Vexetal e Ciencias do Solo, Facultade de Ciencias, Instituto de Agroecoloxía e Alimentacion (IAA), Campus Auga, Universidade de Vigo, 32004 Ourense, Spain)
- Asma Ghorab
(Departamento de Bioloxía Vexetal e Ciencias do Solo, Facultade de Ciencias, Instituto de Agroecoloxía e Alimentacion (IAA), Campus Auga, Universidade de Vigo, 32004 Ourense, Spain)
- María Carmen Seijo Coello
(Departamento de Bioloxía Vexetal e Ciencias do Solo, Facultade de Ciencias, Instituto de Agroecoloxía e Alimentacion (IAA), Campus Auga, Universidade de Vigo, 32004 Ourense, Spain)
Abstract
The authentication of Algerian honey represents a critical challenge for the valuation of national biological patrimony. The present review provides a comprehensive synthesis of existing literature regarding Algerian honeys, emphasizing their diverse botanical origins and complex chemical profiles across seven distinct biogeographical regions, while proposing an innovative Foodomics and AI-driven roadmap to secure geographic authenticity and sustainable rural development. Such evidence underscores the necessity of transitioning from this classical analytical framework toward the emerging ‘Foodomics’ paradigm. By integrating advanced technologies like DNA metabarcoding and molecular fingerprinting, the establishment of a proposed ‘digital passport’ is proposed as a strategic solution to secure Protected Geographical Indications (PGI). Beyond technical innovation, this evolution is presented as a vital socio-economic necessity to ensure the sustainability of rural beekeeping and the international competitiveness of the industry. Ultimately, bridging established data with a molecular roadmap ensures that the biological prestige of this natural heritage is preserved for future generations. Beyond chemical and botanical analyses, this roadmap also incorporates Chemometric Modeling as a cognitive system. By applying techniques such as self-organizing maps (SOMs) and principal component analysis (PCA). This combination ensures highly accurate classification and supports the implementation of a sustainable digital passport system for the local honey industry.
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