Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence

Authors

Hossein Kashani Zadeh, SafetySpect Inc.Follow
Mike Hardy, Queen’s University Belfast
Mitchell Sueker, University of North Dakota
Yicong Li, Queen’s University Belfast
Angela Tzouchas, SafetySpect Inc.
Nicholas MacKinnon, SafetySpect Inc.
Gregory Bearman, SafetySpect Inc.
Simon A. Haughey, Queen’s University Belfast
Alireza Akhbardeh, SafetySpect Inc.
Insuck Baek, USDA-ARS Environmental Microbial and Food Safety Laboratory
Chansong Hwang, USDA-ARS Environmental Microbial and Food Safety Laboratory
Jianwei Qin, USDA-ARS Environmental Microbial and Food Safety Laboratory
Amanda M. Tabb, Chapman UniversityFollow
Rosalee S. Hellberg, Chapman UniversityFollow
Shereen Ismail, University of North Dakota
Hassan Reza, University of North Dakota
Fartash Vasefi, SafetySpect Inc.
Moon Kim, USDA-ARS Environmental Microbial and Food Safety Laboratory
Kouhyar Tavakolian, University of North Dakota
Christopher T. Elliott, Queen’s University Belfast

Document Type

Article

Publication Date

5-28-2023

Abstract

This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and quadratic discriminant analyses, k-nearest neighbors, random forest, support vector machine, and linear regression, as well as ensemble and majority voting methods, were used to explore spectroscopy data measured on fillets and to train classification models to predict freshness. Our results show that multi-mode spectroscopy achieves 95% accuracy, improving the accuracies of the FL, VIS-NIR and SWIR single-mode spectroscopies by 26, 10 and 9%, respectively. We conclude that multi-mode spectroscopy and data fusion analysis has the potential to accurately assess freshness and predict shelf life for fish fillets and recommend this study be expanded to a larger number of species in the future.

Comments

This article was originally published in Sensors, volume 23, in 2023. https://doi.org/10.3390/s23115149

Recommended Citation

Kashani Zadeh, H.; Hardy, M.; Sueker, M.; Li, Y.; Tzouchas, A.; MacKinnon, N.; Bearman, G.; Haughey, S.A.; Akhbardeh, A.; Baek, I.; et al. Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence. Sensors 2023, 23, 5149. https://doi.org/10.3390/s23115149

Peer Reviewed

1

Copyright

The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.