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Seabream Quality Monitoring Throughout the Supply Chain Using a Portable Multispectral Imaging Device.

Anastasia Lytou1, Lemonia-Christina Fengou1, Antonis Koukourikos2

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Summary

A portable multispectral imaging sensor rapidly predicts fish fillet microbiological quality using artificial neural network (ANN) models. This technology offers efficient food quality monitoring, reducing waste and enhancing sustainability across the supply chain.

Keywords:
Artificial Neural NetworksFish freshnessMicrobial spoilageMultispectral imagingPredictive modelsSensors

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Area of Science:

  • Food Science and Technology
  • Sensory Analysis
  • Artificial Intelligence in Food Quality

Background:

  • Rapid and cost-effective food quality monitoring is crucial for timely decision-making, waste reduction, and sustainability.
  • Assessing microbiological quality in fish is essential for ensuring food safety and consumer trust.
  • Traditional microbiological methods are time-consuming, hindering rapid quality control in the supply chain.

Purpose of the Study:

  • To develop and validate artificial neural network (ANN) models for the rapid prediction of total aerobic counts (TACs) in fish fillets.
  • To evaluate the efficiency of a portable multispectral imaging sensor for assessing fish fillet microbiological quality.
  • To investigate the influence of packaging conditions (aerobic, vacuum) and fish part (skin, flesh) on prediction accuracy.

Main Methods:

  • Acquisition of multispectral images from seabream fillets (aquaculture and retail).
  • Estimation of microbiological quality (TACs) in parallel with image acquisition.
  • Development and validation of ANN models using data partitioning and external validation with retail samples.

Main Results:

  • ANN models demonstrated good performance for predicting TACs from both skin and flesh sides (RMSE 0.402-0.547).
  • Models showed similar performance for aerobic and vacuum-packaged fish, with slightly reduced accuracy when combining both conditions.
  • External validation with retail samples yielded poorer performance (RMSE 1.061-1.414) compared to internal validation.

Conclusions:

  • The portable multispectral imaging sensor is an efficient tool for rapid microbiological quality assessment of fish fillets.
  • ANN models show promise for real-time quality monitoring, benefiting industry, authorities, and consumers.
  • Further research and model refinement are needed to improve performance in external validation scenarios.