This pilot study (open access) used octopus as a proof-of-concept in the use of portable NIR, backed by a chemometric database and decision logic, to detect undeclared prior freezing of “fresh” seafood. Existing methods, such as microscope examination or using enzyme thresholds, are often inconclusive and require samples to be sent offsite for laboratory analysis.
The authors evaluated the performance of a portable MicroNIR system for the discrimination of fresh and thawed Octopus vulgaris under realistic supply chain conditions. The dataset was designed to reflect industrial variability, including different suppliers, fishing areas, fishing methods, and thawing procedures.
They report that Partial Least Squares Discriminant Analysis (PLS-DA) models achieved classification rates of approximately 85% in calibration and cross-validation, while external prediction performance decreased to approximately 75%, particularly for thawed samples. To translate the model results into an operational quality control strategy, a Process Analytical Technology (PAT)-oriented decision logic was developed based on the mean and variability of replicate predictions. An acceptance zone was defined for fresh samples, ensuring that no thawed sample was incorrectly accepted as fresh in the external test set.
They conclude that they have demonstrated the feasibility of integrating portable NIR spectroscopy into a risk-based and online PAT framework for octopus authenticity assessment, prioritizing fraud prevention and robust decision-making.
Photo by Mike Bergmann on Unsplash
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