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The NIRMARLIN project has demonstrated the feasibility of Near-Infrared (NIR) technology for the high-precision identification of blue marlin (Makaira nigricans) in comparison with other similar species.
The solution enables the detection of blue marlin catches, improving species segregation and reducing the risk of regulatory non-compliance within the fisheries sector.
The developed model achieves 95% accuracy in external validation and lays the groundwork for a new phase of validation under real operating conditions, as well as industrial deployment aimed at strengthening traceability and quality control.
Bilbao, 4 May 2026. OR.PA.GU., a Galician company specialising in the fisheries sector, and AZTI, a leading technology centre in food and marine research, have demonstrated—within the framework of the NIRMARLIN project—the feasibility of a solution based on Near-Infrared (NIR) spectroscopy for the identification of blue marlin (Makaira nigricans), a species classified as “Vulnerable” by the IUCN.
The technology has proven capable of distinguishing blue marlin from other morphologically similar species under processing conditions, where visual identification presents limitations. This provides a robust technological basis for improving species segregation, traceability and in-plant quality control.
AZTI’s research team conducted several sampling campaigns throughout 2025 at OR.PA.GU.’s facilities in Tui (Pontevedra). Spectral measurements were taken from blue marlin specimens and other similar species, both in frozen and thawed states, generating a robust dataset for the development of classification models.
The results show consistent spectral differences between species, independent of external factors such as the presence of ice, ensuring system reliability under real operating conditions. Nevertheless, an additional validation phase in continuous production environments will be required prior to full-scale implementation.
Data processing using multivariate analysis techniques—including Principal Component Analysis (PCA) and classification models based on Partial Least Squares Discriminant Analysis (PLS-DA)—enabled system optimisation and identified the tail region as the optimal measurement point, reducing variability and improving classification performance.
The final model achieves 95% accuracy in external validation, with high sensitivity and specificity values, reinforcing its potential for industrial scaling in future project phases.
“The results confirm that NIR spectroscopy can become a key tool for objective species identification in processing plants. With 95% accuracy in external validation, it strengthens control systems and facilitates compliance with regulatory requirements,” said Sonia Nieto Ortega, an expert researcher in digitalisation at AZTI.
From OR.PA.GU., Juana Parada highlighted the value of applied technological developments: “The integration of technologies such as NIR spectroscopy enables progress towards more efficient, traceable processes aligned with market demands and regulatory requirements, particularly regarding accurate species classification.”
The outcomes of the NIRMARLIN project confirm the technical viability of NIR as a support tool in industrial processes. When integrated into production lines, it can enhance automated classification, strengthen traceability and help prevent the commercialisation of vulnerable species.
The project also establishes the foundations for a new phase focused on validation in real production environments, system optimisation under operational conditions and subsequent industrial deployment, opening the door to adoption by other companies in the fisheries and food sectors.
This initiative aligns with the growing demands of the fisheries and food industries in terms of transparency, sustainability and digitalisation, as well as with regulatory objectives related to the responsible management of marine resources.
The NIRMARLIN project has been carried out with funding received by OR.PA.GU. through the Production and Marketing Plans (PPyC) for fisheries producer organisations and their associations (OPPs), financed by the Spanish Ministry of Agriculture, Fisheries and Food (MAPA).