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The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods when using NIR Spectroscopy to

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|January 8, 2020
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Summary

Low-cost near-infrared (NIR) sensors combined with machine learning can accurately detect food allergens in powdered foods. Optimizing sensor height, light intensity, and spectral pre-processing achieved 100% accuracy for allergen identification.

Keywords:
NIR spectroscopyallergen detectiondigital manufacturingindustry 4.0machine learningpowdered food

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Area of Science:

  • Food Science and Technology
  • Analytical Chemistry
  • Industrial Engineering

Background:

  • Food allergens pose significant health risks, necessitating robust monitoring in food production, especially for powdered products.
  • Industry 4.0 technologies offer advanced solutions for enhancing manufacturing safety and efficiency.
  • Powdered foods are high-risk matrices for allergen cross-contamination due to their composition.

Purpose of the Study:

  • To investigate the efficacy of low-cost sensors and machine learning for identifying food allergens in powdered materials.
  • To determine optimal measurement and data processing parameters for near-infrared (NIR) spectroscopy in allergen detection.
  • To assess the feasibility of implementing this technology in industrial food production environments.

Main Methods:

  • Utilized a near-infrared (NIR) sensor to analyze over 50 different powdered food materials.
  • Investigated the impact of sensor light intensity, sensor-to-sample height, and spectral pre-processing techniques.
  • Employed machine learning algorithms, including K-nearest neighbour and linear discriminant analysis, for classification.

Main Results:

  • Sensor-to-sample height significantly influenced accuracy, with closer proximity yielding better results.
  • Standard Normal Variate (SNV) and Multiplicative Scattering Correction (MSC) were the most effective spectral pre-processing methods.
  • K-nearest neighbour and linear discriminant analysis demonstrated the highest classification prediction accuracy.

Conclusions:

  • Optimized NIR sensor parameters and machine learning algorithms can achieve 100% accuracy in identifying powdered food allergens.
  • This approach offers a viable, cost-effective solution for real-time allergen monitoring in food manufacturing.
  • The study highlights the potential of Industry 4.0 technologies for improving food safety and quality control.