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Author Spotlight: An Alternative Approach to Protein Quantification by Bradford Assay Using a Smartphone
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Smartphone Video Imaging Combined with Machine Learning: A Cost-Effective Method for Authenticating Whey Protein

Xuan Tang1, Wenjiao Du1, Weiran Song2,3

  • 1School of Physical Education, Yunnan University, Kunming 650091, China.

Foods (Basel, Switzerland)
|April 16, 2025
PubMed
Summary

A new method uses smartphone video imaging and machine learning for low-cost whey protein supplement authentication. This approach offers a viable preliminary screening tool for verifying product quality and authenticity.

Keywords:
authenticationchemometricsmachine learningsmartphone video imagingwhey protein concentrate

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Area of Science:

  • Analytical Chemistry
  • Food Science
  • Machine Learning

Background:

  • Rising demand for whey protein supplements necessitates cost-effective quality control methods.
  • Current authentication techniques can be expensive and time-consuming.
  • Ensuring the authenticity of sports nutrition products is crucial for consumer trust.

Purpose of the Study:

  • To develop a rapid, low-cost method for authenticating sports whey protein supplements.
  • To utilize smartphone video imaging (SVI) combined with machine learning for quality characterization.
  • To assess the performance of SVI against established techniques like hyperspectral imaging (HSI).

Main Methods:

  • Smartphone video imaging (SVI) was employed, using a smartphone screen to illuminate samples and its camera to capture color changes.
  • Video data was processed into spectral data, and machine learning models established relationships between video data and sample characteristics.
  • The method was validated across tasks including brand identification, fat and energy content quantification, and adulterant detection.

Main Results:

  • SVI achieved high accuracy in whey protein concentrate (WPC) brand identification (0.933) and adulterant detection (0.96).
  • Excellent coefficients of determination were obtained for quantifying fat content (0.897), energy levels (0.906), and milk powder adulteration (0.963).
  • Performance was comparable to hyperspectral imaging (HSI), despite SVI's significantly lower equipment cost.

Conclusions:

  • The combination of smartphones and machine learning provides a cost-effective and practical preliminary screening tool for whey protein supplement authenticity.
  • SVI offers a viable alternative for rapid quality assessment in the food supply chain.
  • This technology can empower consumers and manufacturers with accessible product verification.