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Fluorescence in situ hybridization, or FISH, was developed in the early 1980s and has quickly become one of the most widely used techniques in cytogenetics. Labeled probes are used to bind complementary DNA or RNA sequences on a chromosome or in a region within a cell. Earlier, the probes could only be obtained by cloning or reverse transcription of a DNA template. Currently, the probe oligonucleotides can be synthesized synthetically. Additionally, with the advancement of optical techniques,...
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NMR Spectroscopy as a Robust Tool for the Rapid Evaluation of the Lipid Profile of Fish Oil Supplements
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Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based

Hossein Kashani Zadeh1, Mike Hardy2, Mitchell Sueker3

  • 1SafetySpect Inc., Grand Forks, ND 58202, USA.

Sensors (Basel, Switzerland)
|June 10, 2023
PubMed
Summary

A new handheld multimode spectroscopy system accurately assesses fish freshness by fusing visible near-infrared (VIS-NIR), short-wave infrared (SWIR), and fluorescence (FL) data. This non-destructive method achieved 95% accuracy, significantly improving upon single-mode techniques for predicting fish quality.

Keywords:
artificial intelligencefish freshnessfood qualitymachine learningmulti-mode spectroscopyshelf-life assessment

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Area of Science:

  • Food science and technology
  • Analytical chemistry
  • Spectroscopy

Background:

  • Assessing fish freshness is crucial for food safety and quality control.
  • Current methods for fish quality assessment can be time-consuming, destructive, or subjective.
  • Developing rapid, non-destructive techniques is essential for the seafood industry.

Purpose of the Study:

  • To develop a fast, non-destructive, handheld multimode spectroscopic system for fish quality assessment.
  • To evaluate the effectiveness of data fusion from multiple spectroscopic techniques for predicting fish freshness.
  • To compare the performance of multimode spectroscopy against single-mode techniques.

Main Methods:

  • Utilized visible near-infrared (VIS-NIR) and short-wave infrared (SWIR) reflectance, and fluorescence (FL) spectroscopy.
  • Applied data fusion techniques to combine spectral data from multiple modes.
  • Employed various machine learning algorithms, including principal component analysis, random forest, and support vector machines, for classification.
  • Collected extensive spectral data from salmon and sablefish fillets over a 14-day period.

Main Results:

  • The multimode spectroscopic system achieved 95% accuracy in classifying fish freshness.
  • Data fusion significantly improved accuracy compared to single-mode spectroscopies: FL (+26%), VIS-NIR (+10%), and SWIR (+9%).
  • The developed system demonstrated high potential for accurate and rapid fish quality assessment.

Conclusions:

  • Multimode spectroscopy combined with data fusion offers a powerful tool for non-destructive fish freshness assessment.
  • This technology has the potential to accurately predict shelf life for fish fillets.
  • Future research should expand the application to a broader range of fish species.