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Hyperspectral Imaging Coupled with Multivariate Analyses for Efficient Prediction of Chemical, Biological and Physical Properties of Seafood Products

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Abstract

Quality evaluation of seafood is essential for consumer satisfaction. Hyperspectral imaging (HSI) has been introduced in the seafood industry for assessing seafood quality, safety, authenticity, and adulteration whilst maintaining sample integrity. However, there is limited information on multivariate analyses applied using the HSI for seafood quality. This review presents a comprehensive summary of the existing published research to describe the application of HSI coupled with multivariate analyses of seafood products. Applications of multivariate analyses for map distribution, spectral selection, and data extraction of the HSI system in the seafood industry are highlighted. Trends and challenges using HSI in the seafood industry are also discussed in this review. As a rapid and non-destructive tool, HSI technology shows great potential for evaluating the quality of seafood products by on-line or at-line detection. The ability to provide spatial and spectral information coupled with multivariate analyses makes the HSI system broadly in the seafood industry. Deep learning performed by artificial intelligence is a great solution recently for data classification of hyperspectral imaging with a shift-invariant feature of seafood products. HSI systems fitted with multivariate analyses software could be eased in the large-scale seafood industry to determine the chemical, biological, and physical quality traits of seafood products.

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Data Availability

The data information generated during and/or analysed during the current study are extracted from the references provided also available from the corresponding author on reasonable request.

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Acknowledgements

Financial support is received by the first author from the Ministry of Higher Education Malaysia (MOHE) during his PhD study.

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Authors and Affiliations

  1. Department of Agricultural Biotechnology, Center for Food and Bioconvergence, and Research Institute of Agriculture and Life Science, Seoul National University, Seoul, 08826, Korea

    Azfar Ismail, Dong-Gyun Yim & Cheorun Jo

  2. Department of Aquaculture, Faculty of Agriculture, Universiti Putra Malaysia, 43400, Selangor, Malaysia

    Azfar Ismail

  3. Department of Biosystems and Biomaterials Science and Engineering, Seoul National University, Seoul, 08826, Korea

    Ghiseok Kim

  4. Global Smart Farm Convergence Major, College of Agriculture and Life Sciences, Seoul National University, Seoul, 08826, Korea

    Ghiseok Kim

  5. Institute of Green Bio Science and Technology, Seoul National University, Pyeongchang, 25354, Korea

    Cheorun Jo

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Contributions

Azfar Ismail: investigation, resources, writing-original draft. Dong-Gyun Yim: methodology, validation, writing-review and editing. Ghiseok Kim: data curation, software, writing-review and editing. Cheorun Jo: conceptualization, supervision, writing-review and editing.

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Correspondence to Cheorun Jo.

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Ismail, A., Yim, DG., Kim, G. et al. Hyperspectral Imaging Coupled with Multivariate Analyses for Efficient Prediction of Chemical, Biological and Physical Properties of Seafood Products. Food Eng Rev 15, 41–55 (2023). https://doi.org/10.1007/s12393-022-09327-x

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