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An electronic nose (E-nose) sensor can trace seafood's fishing gear origin. This method accurately identifies fishing methods, revealing the impact of each gear on fish quality.

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Area of Science:

  • Marine Biology
  • Food Science
  • Sensor Technology

Background:

  • Seafood quality is influenced by fishing methods, handling, and stress.
  • Traditional traceability methods face challenges in accurately reflecting the impact of fishing gear.

Purpose of the Study:

  • To develop and validate an innovative methodology for quantifying fishing gear impact on seafood quality.
  • To classify the fishing gear of origin for Sparus aurata using an E-nose sensor and advanced analytical techniques.

Main Methods:

  • Utilized an E-nose sensor to capture volatile profiles of Sparus aurata.
  • Applied Parallel Factor Analysis (PARAFAC) for impact quantification.
  • Employed Machine Learning (ML) models for fishing gear classification.

Main Results:

  • Aquaculture and purse seine fishing methods showed the highest impact on fish.
  • Longline fishing exhibited the lowest deviation.
  • The Subspace KNN model achieved 97.14% accuracy in validation and 98.08% in testing for gear classification.

Conclusions:

  • The E-nose sensor combined with PARAFAC and ML offers high precision in fish traceability.
  • This methodology can effectively identify the fishing gear responsible for fish quality alterations.
  • The sensor response profile provides a unique signature for each fishing method.