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Cited by 12Spectroscopy and Chemometric AnalysesMeat and Animal Product QualityOptical Imaging and Spectroscopy Techniques

Artificial Intelligence Empowered Multispectral Vision Based System for Non-Contact Monitoring of Large Yellow Croaker (Larimichthys crocea) Fillets

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Wang, Shengnan, Das, Avik Kumar, Pang, Jie, Liang, Peng·2021·Foods
DOI 10.3390/foods10061161PMC PMC8224386
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Abstract

A non-contact method was proposed to monitor the freshness (based on TVB-N and TBA values) of large yellow croaker fillets (Larimichthys crocea) by using a visible and near-infrared hyperspectral imaging system (400–1000 nm). In this work, the quantitative calibration models were built by using feed-forward neural networks (FNN) and partial least squares regression (PLSR). In addition, it was established that using a regression coefficient on the data can be further compressed by selecting optimal wavelengths (35 for TVB-N and 18 for TBA). The results validated that FNN has higher prediction accuracies than PLSR for both cases using full and selected reflectance spectra. Moreover, our FNN based model has showcased excellent performance even with selected reflectance spectra with rp = 0.978, R2p = 0.981, and RMSEP = 2.292 for TVB-N, and rp = 0.957, R2p = 0.916, and RMSEP = 0.341 for TBA, respectively. This optimal FNN model was then utilized for pixel-wise visualization maps of TVB-N and TBA contents in fillets.

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  • For fish, SVM and CNN models predict quality using total viable counts from images, achieving over 86% accuracy.

    “For fish, AI models like Support Vector Machine (SVM) and CNN predict quality using Total Viable Counts (TVC) from images, achieving over 86 % accuracy [156].”

    From Towards the development of foods 3D printer: Trends and technologies for foods printing (2024) · cites this paper as [156]

    Sections 3.2–3.3 support the broad point that models use hyperspectral images to predict freshness indicators in fish fillets. But the paper uses FNN and PLSR to predict TVB-N and TBA, not SVM and CNN to predict total viable counts. Its reported prediction metrics include R² and error values, not an accuracy exceeding 86%. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Wang, S., Das, A. K., Pang, J., & Liang, P.
Affiliations
  • Fujian Agriculture and Forestry University (CN)
  • Hong Kong University of Science and Technology (HK)
Corresponding author
Liang, Peng
ORCID
Venue
Foods
Publisher
MDPI AG
Published
2021-05-21
Volume
10
Issue
6
Pages
1161
ISSN
2304-8158 (ISSN-L)
DOI
10.3390/foods10061161
PMC
PMC8224386
OpenAlex
W3160153890
Cited by
12
References
0
Citations by year
2026: 2 · 2024: 4 · 2023: 2 · 2022: 3 · 2021: 1
FWCI
1.25
Type
journal article
Language
en
Open access
Licence
Locations
  • Foods (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Key R&D Program of China (2018YFD0901001-04)
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
large yellow croaker, non-contact monitoring, hyperspectral imaging, near-infrared reflectance spectroscopy, total volatile basic nitrogen, TBA, feedforward neural networks, partial least squares regression, wavelength selection, fish spoilage

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