A Review on Recent Advances in Imaging Technologies for Quality and Safety Assessment of Fish and Fishery Products
DOI:
https://doi.org/10.66132/mr2104Keywords:
Fish freshness, Hyperspectral imaging, Deep learning, Non-destructive quality assessmentAbstract
Fish and fishery products are an important source of protein, essential omega-3 fatty acids, and micronutrients. However, they are prone to rapid spoilage via autolysis, lipid oxidation, and microbial proliferation, necessitating sophisticated quality assessment techniques. Traditional sensory and chemical tests (e.g., TVBN, K-value, TBARS) are destructive, lengthy, and unsuitable for high-throughput applications. This review examines imaging technologies like computer vision (RGB), multispectral/hyperspectral imaging (HSI), VIS/NIR spectroscopy, and UV/fluorescence imaging as non-destructive alternatives for assessing fish product freshness, composition, defects, safety, and authenticity. Key principles include image acquisition, preprocessing, feature extraction (color, texture, spectra), modeling using chemometrics (PCA, PLS, SVM), and deep learning (CNNs, transformers). Applications involve automated grading, spoilage prediction (e.g., eye/gill changes, spectral freshness indices), moisture/fat mapping, bruise detection, parasite detection, and process monitoring. These systems can be integrated with E-Nose and AI for online industrial use, helping to minimize waste and improve traceability. Challenges include cost and calibration; prospects include miniaturized hardware and multimodal fusion for sustainable seafood processing.
References
Duan, X., Liu, Y., Zhang, H., & Wang, J. (2025). Monitoring moisture migration and drying kinetics of vacuum freeze-dried grass carp fillets using near-infrared hyperspectral imaging. Drying Technology, 43(3), 512–525. https://doi.org/10.1080/07373937.2024.2318456
Hardy, J., Sun, D. W., & Pu, H. (2024). Recent developments in hyperspectral imaging for fish freshness and spoilage assessment: From laboratory studies to industrial applications. Food Control, 158, 110245. https://doi.org/10.1016/j.foodcont.2024.110245
Liyanage, M. P., Madhubhashini, H. M., & Liyanage, R. P. (2024). Current applications and future trends of artificial senses in fish freshness determination: A review. Journal of Food Science, 89(2), 785–804. https://doi.org/10.1111/1750-3841.16865
Luo, Y., Chen, X., Zhang, H., & Sun, D. W. (2023). Quantitative prediction of fat distribution in Atlantic salmon fillets using hyperspectral imaging and deep learning. LWT, 184, 114980. https://doi.org/10.1016/j.lwt.2023.114980
Ozdogan, G., Lin, X. H., & Sun, D. W. (2021). Rapid and noninvasive sensory analyses of food products by hyperspectral imaging: Recent application developments. Trends in Food Science & Technology, 111, 151–165. https://doi.org/10.1016/j.tifs.2021.02.021
Patel, D., Bhise, S., Kapdi, S. S., et al. (2024). Non-destructive hyperspectral imaging technology to assess the quality and safety of food: A review. Food Production, Processing and Nutrition, 6, 69. https://doi.org/10.1186/s43014-024-00246-4
Pu, H., Wei, Q., & Sun, D. W. (2023). Recent advances in muscle food safety evaluation: Hyperspectral imaging analyses and applications. Critical Reviews in Food Science and Nutrition, 63(10), 1297–1313. https://doi.org/10.1080/10408398.2022.2121805
Saha, J., & Manickavasagan, A. (2021). Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review. Current Research in Food Science, 4, 28–44. https://doi.org/10.1016/j.crfs.2021.01.001
Sun, D.-W., Pu, H., & Yu, J. (2024). Applications of hyperspectral imaging technology in the food industry. Nature Reviews Electrical Engineering, 1(4), 251–263. https://doi.org/10.1038/s44287-024-00033-w
Yildiz, M. B., Yasin, E. T., & Koklu, M. (2024). Fisheye freshness detection using common deep learning algorithms and machine learning methods with a developed mobile application. European Food Research and Technology, 250, 1919–1932. https://doi.org/10.1007/s00217-024-04493-0
Yu, H. D., Jiang, D. P., Peng, X. W., & Zhang, Y. Z. (2021). Hyperspectral imaging in combination with data fusion for rapid evaluation of tilapia fillet freshness. Food Chemistry, 348, 129129. https://doi.org/10.1016/j.foodchem.2021.129129
Zhang, X., Wang, Y., Chen, X., & Li, Y. (2022). Recent advances of machine vision technology in fish classification. ICES Journal of Marine Science, 79(2), 263–276. https://doi.org/10.1093/icesjms/fsab261
Zheng, Y., Yang, L., Zheng, H., Guo, Q., & Fang, H. (2026). Toward intelligent and deployable seafood quality evaluation: A review of deep learning-assisted computer vision. Comprehensive Reviews in Food Science and Food Safety, 25(4), e70551. https://doi.org/10.1111/1541-4337.70551
Zuo, Y., Li, H., Wang, X., & Sun, D. W. (2025). Advances in hyperspectral imaging for non-destructive evaluation of nutritional and compositional attributes of aquatic foods. Journal of Agricultural and Food Chemistry, 73(7), 4215–4231. https://doi.org/10.1021/acs.jafc.4c07891
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