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Cited by 61Spectroscopy and Chemometric AnalysesMeat and Animal Product QualityOptical Imaging and Spectroscopy Techniques

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Detection of expired vacuum-packed smoked salmon based on PLS-DA method using hyperspectral images

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Ivorra, Eugenio, Girón, Joel, Sánchez, Antonio J., Verdú, Samuel, Barat, Jose M., Grau, Raúl·2013·Journal of Food Engineering
DOI 10.1016/j.jfoodeng.2013.02.022
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Abstract

Consumers want fresh food with a long shelf-life, which in 2010, resulted in an important increase in packaged and processed food. This is especially important for fishery products due to their highly perishable nature. One problem is that it is not possible to measure freshness in packaged food only using the visible spectrum. Moreover, the detection of freshness is a complex problem as fish has different tissues with different biodegradation processes. Therefore, it would be especially interesting to have a non-destructive method to evaluate the shelf-life of packed processed fish. This paper proposes a method for detecting expired packaged salmon. Firstly, this method uses hyperspectral imaging spectroscopy (HIS) using visible and SW-NIR wavelengths. Secondly, a classification of different salmon tissues is carried out by image segmentation. Finally, classifications of expired or non expired salmon are performed with the PLS-DA statistical multivariate method due to the large amount of captured data. In a similar way, spectral data and the physicochemical, biochemical and microbiological properties of salmon are correlated using partial least squares (PLSs). The result obtained has a classification success rate of 82.7% in cross-validation from real commercial samples of salmon. Therefore, this is a promising technique for the non-destructive detection of expired packaged smoked salmon.

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  • The most recent ways involving classification using hyperspectral data include extreme learning machine (ELM), partial least squares discriminant analysis (PLS-DA), and various deep learning algorithms.

    “The most recent ways involving classification using hyperspectral data include extreme learning machine (ELM) [7–9], partial least squares discriminant analysis (PLS-DA) [10,11], and various deep learning algorithms [12,13].”

    From Development of online classification system for construction waste based on industrial camera and hyperspectral camera (2019) · cites this paper as [10]

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Metadata

Authors
Ivorra, E., Girón, J., Sánchez, A. J., Verdú, S., Barat, J. M., & Grau, R.
Affiliations
Universitat Politècnica de València (ES)
Corresponding author
Ivorra, Eugenio
ORCID
Venue
Journal of Food Engineering
Publisher
Elsevier BV
Published
2013-08
Volume
117
Issue
3
Pages
342-349
ISSN
0260-8774 (ISSN-L)
DOI
10.1016/j.jfoodeng.2013.02.022
OpenAlex
W2021248229
Cited by
61
References
0
Citations by year
2026: 1 · 2025: 5 · 2024: 6 · 2023: 3 · 2022: 2 · 2021: 3 · 2020: 6 · 2019: 1 · 2018: 4 · 2017: 3 · 2016: 9 · 2015: 7 · 2014: 7 · 2013: 4
FWCI
8.58
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Journal of Food Engineering (publishedVersion)
  • RiuNet (Universitat Politècnica de València) (submittedVersion, cc-by-nc-nd)
Funders
  • Universidad de Alicante
  • Universidad Nacional de Colombia
Topics
  • Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
  • Life Sciences › Agricultural and Biological Sciences › Animal Science and Zoology › Meat and Animal Product Quality
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Optical Imaging and Spectroscopy Techniques
Keywords
smoked salmon, vacuum packaging, food spoilage detection, hyperspectral imaging, PLS-DA, shelf life, non-destructive food inspection, image segmentation, fish freshness

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