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Food Quality Control – Hazelnut Analysis with Spectral Imaging

Food Quality Control – Hazelnut Analysis with Spectral Imaging

hazelnut

Why Food Quality Control and Assessment is important?

Food Quality Control is crucial in all areas from production to manufacturing. Safety, quality, and authenticity are categories of food integrity.

Every year there are thousands of people getting sick from food that has not been inspected properly enough. Hence, food safety management has become increasingly more crucial within the industry.

Multispectral Imaging in the Food Industry

Videometer is able to assist in various ways when it comes to Food Quality Control, including:

  • Food safety
  • Adulteration
  • Food counterfeit
  • Spoilage
  • Contamination
  • Food quality, and more

The areas of application are meat, seafood, plants, fruits, vegetables, and baked, fried products. Read more here about how an image-based solution makes an impact on food safety management.

Hazelnut analysis by Image-based solution and Machine Learning

One of the examples is hazelnut analysis. Nuts can be classified by their roasting degree, a fraction of inner shelf, roundness, or defects. VideometerLab measures more than 12 million spectra on a food sample within a fraction of a second. Hence, this technology provides faster and more accurate specifications and results.

The combination of multispectral imaging and machine learning creates the perfect solution for accurate food quality control.

hazelnut classification model videometer spectral imaging
Hazelnut classification model

Users of VideometerLab are able to choose how they want to classify their objects. In addition, there is an option to create new classifications and, thanks to machine learning techniques, the software can distinguish and classify different varieties and features.

Multispectral imaging can aid organizations in their food integrity assessment.

Food quality control using spectral imaging
Food Quality Control using Spectral Imaging

The software allows the users to see differences in the objects with RGB, using multiple wavelengths to show the user what is not visible to the human eye.

We measure what you see – and beyond!

Conclusion

  • A number of important hazelnut parameters are measured fast and accurately with the same instrument
  • A roasting degree measured without influence from inner shells
  • Inner shell fraction measured without influence from roasting degree
  • Size, shape, mechanical damage, and insect damage may be measured
  • Potential for molds and aflatoxin measurement on nut surface

 

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