A FPGA based recurrent neural networks-based impedance spectroscopy system for detection of YAKE in tuna.

Journal: Scientific reports
Published Date: Jul 1, 2025

Abstract

This paper evaluates the use of impedance spectroscopy combined with artificial intelligence. Both technologies have been widely used in food classification and it is proposed a way to improve classifications using recurrent neural networks that treat the impedance data series at different frequencies as a time series, with the intention of improving the identification of alpha and beta dispersions that are fundamental for the determination of food quality. This proposal in addition to being demonstrated its validity in the detection of YAKE on frozen tuna loins, is fully implemented on a low power FPGA device that allows the classification at the edge by means of a portable equipment that allows its application in food distribution chains with high energy efficiency.

Authors

  • Rafael Gadea-Gironés
    Department Universitat Politècnica de València, Camino de Vera, s/n, 46022 València, Spain. rgadea@eln.upv.es.
  • Jose M Monzo
    Instituto de Instrumentación para Imagen Molecular I3M, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Valencia, Spain.
  • Ricardo Colom-Palero
    Department Universitat Politècnica de València, Camino de Vera, s/n, 46022 València, Spain. rcolom@eln.upv.es.
  • Jorge Fe
    Instituto de Instrumentación para Imagen Molecular I3M, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Valencia, Spain.
  • Marta Castro-Giraldez
    Instituto Universitario de Ingeniería de Alimentos FoodUPV, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Valencia, Spain.
  • Pedro J Fito
    Instituto Universitario de Ingeniería de Alimentos FoodUPV, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Valencia, Spain.

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