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Summary

This study used optical spectroscopy and machine learning to determine salmon fillet freshness, achieving nearly perfect accuracy with combined data modes for rapid fish quality assessment.

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Area of Science:

  • Food Science
  • Analytical Chemistry
  • Biotechnology

Background:

  • Assessing fish freshness is crucial for food safety and quality control.
  • Traditional methods for determining fish spoilage can be time-consuming and subjective.
  • Rapid, objective methods are needed for real-time quality assessment in the food industry.

Purpose of the Study:

  • To evaluate the effectiveness of optical spectroscopy (fluorescence and absorption) combined with machine learning for determining salmon fillet freshness.
  • To compare the accuracy of single-mode versus dual-mode spectroscopic data analysis.
  • To explore feature selection techniques for improving predictive accuracy from spectral data.

Main Methods:

  • Salmon fillets were analyzed using hand-held fluorescence and visible/near-infrared (NIR) absorption spectroscopy over 17 days.
  • Spectroscopic data were benchmarked against nucleotide assays and potentiometry.
  • Four machine learning algorithms (LDA, Gaussian Naïve Bayes, KNN, Bagged Trees) were employed for freshness classification.
  • Dual-mode (fluorescence + absorbance) and single-mode data fusion techniques were compared.

Main Results:

  • Dual-mode data fusion achieved almost perfect accuracy (mean = 99.5 ± 0.51%) in classifying salmon fillet freshness.
  • Single-mode analyses showed lower accuracies (e.g., fluorescence: 77.1 ± 10.1%).
  • Principal component analysis identified key fluorescence wavelengths for predicting freshness, indicating potential for simplified analysis.

Conclusions:

  • Combining fluorescence and absorption spectroscopy with machine learning offers a highly accurate method for rapid fish freshness determination.
  • Dual-mode data fusion significantly outperforms single-mode analyses.
  • This approach provides a foundation for developing portable, rapid tools for fish quality assessment.