Integrating EEM fluorescence spectroscopy with machine and deep learning models for multi-output prediction of fish freshness

Authors

• Yanwei Fan, Beijing Academy of Agricultural and Forestry Sciences
• Zengtao Ji, Beijing Academy of Agricultural and Forestry Sciences
• Chuanheng Sun, Beijing Academy of Agricultural and Forestry Sciences
• Daming Xu, Beijing Academy of Agricultural and Forestry Sciences
• Xinting Yang, Beijing Academy of Agricultural and Forestry Sciences
• Ce Shi, Beijing Academy of Agricultural and Forestry SciencesFollow
• Hongbing Fan, University of KentuckyFollow

Abstract

A precise and real-time assessment of the freshness of aquatic products during storage can effectively reduce economic losses and food safety problems. This study developed a novel multi-output freshness monitoring model integrating excitation-emission matrix (EEM) fluorescence spectroscopy with two machine learning (ML) and one deep learning (DL) approach to simultaneously predict freshness indices and classify freshness stages. We Utilized bighead carp (Hypophthalmichthys nobilis) andrainbow trout (Oncorhynchus mykiss), to predict TVB-N, K value, and TVC freshness indices via PCA-RF regression, achieving R² = 0.85-0.98 and RMSE = 0.23-3.63 significantly outperforming PARAFAC-SVR/RF and PCA-SVR; classify freshness into fresh, sub_fresh, and rot stages using a custom convolutional neural network (CNN), attaining 93.84% for bighead carp and 90.47% for rainbow trout; validate model generalizability through transfer learning, demonstrating that fine-tuned species-specific CNNs achieve cross-species classification accuracy exceeding 88.40%. This approach circumvents manual feature engineering via end-to-end EEM-CNN processing. Furthermore, we established the feasibility of transferring models trained on one species to another through fine-tuning. This study provides a model for precise quality monitoring in the fish supply chain and further applications of real-time freshness monitoring and risk assessment.

Document Type

Article

Publication Date

12-1-2025

Notes/Citation Information

Publisher Copyright: © 2025 The Author(s)

Digital Object Identifier (DOI)

10.1016/j.afres.2025.101374

Archival?

Archival

Repository Citation

Fan, Yanwei; Ji, Zengtao; Sun, Chuanheng; Xu, Daming; Yang, Xinting; Shi, Ce; and Fan, Hongbing, "Integrating EEM fluorescence spectroscopy with machine and deep learning models for multi-output prediction of fish freshness" (2025). Animal and Food Sciences Faculty Publications. 104.
https://uknowledge.uky.edu/animalsci_facpub/104