Document Type
Article
Publication Date
11-22-2025
Abstract
Accurate fish species identification is critical to prevent mislabeling and fraud in the seafood industry. We present a handheld multi-mode point spectroscopy system that combines fluorescence (365 and 395 nm excitation) and reflectance measurements in the visible to near-infrared (∼350–900 nm) and short-wave infrared (∼900–1700 nm) regions for rapid, non-destructive classification of fish fillets. Tissue spectra were acquired at 25 positions on 68 fillets from 11 species, in both frozen and thawed states. Feature-level fusion across all four modes enabled higher classification accuracy than any single mode alone. A global machine-learning model classified all species with 85 ± 2.8 %, while specialized dispute models for commonly misclassified species improved performance to 90 % ± 6.1 %. Individual models for thawed and frozen fillets achieved 90 ± 6.0 % and 90 ± 5.4 %, respectively, with dispute models in the thawed dataset increasing accuracy to 93 ± 4.3 %. These results demonstrate that portable multi-mode spectroscopy, combined with machine learning, provides a fast and reliable tool for on-site fish species identification.
Recommended Citation
Sueker, M., MacKinnon, N., Bearman, G., Tabb, A., Kim, D., Hellberg, R. S., Akhbardeh, A., Marateb, H. R., Qin, J., Kim, M., Vasefi, F., & Zadeh, H. K. (2025). FISH-SPEC: Fast identification system for handheld spectroscopy and species classification. Applied Food Research, 5(2), 101536. https://doi.org/10.1016/j.afres.2025.101536
Peer Reviewed
1
Copyright
The authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Included in
Aquaculture and Fisheries Commons, Atomic, Molecular and Optical Physics Commons, Food Biotechnology Commons, Food Processing Commons, Other Food Science Commons, Other Physics Commons
Comments
This article was originally published in Applied Food Research, volume 5, issue 2, in 2025. https://doi.org/10.1016/j.afres.2025.101536