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This study introduces an electronic nose with AI to detect plant-based milk types and potential allergens in real-time. This technology enhances food safety and beverage preparation by enabling on-device quality control.

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Area of Science:

  • Food Science
  • Sensory Science
  • Analytical Chemistry

Background:

  • Plant-based milk alternatives present food safety challenges like allergen cross-contamination.
  • Current quality control methods are insufficient for real-time, point-of-consumption risks.
  • Variability in beverage performance affects consumer experience.

Purpose of the Study:

  • To develop an embedded volatilomic sensing approach for real-time analysis of plant-based milk alternatives.
  • To enable on-device decision-making for allergen detection and beverage performance.
  • To integrate risk-aware screening with sensory-oriented process control.

Main Methods:

  • Characterization of plant-based milk volatilomes using GC-MS/SPME.
  • Deployment of metal oxide semiconductor (MOX) sensor arrays for rapid volatile fingerprinting.
  • Application of lightweight artificial intelligence algorithms for data analysis and decision-making.

Main Results:

  • Clear discrimination among oat, almond, soy, and coconut beverages based on volatile signatures.
  • MOX sensor arrays effectively function as descriptors of beverage identity and variability.
  • Demonstrated potential for screening cow's milk carryover and adaptive preparation parameter tuning.

Conclusions:

  • Embedded volatilomic intelligence offers a unified approach for personalized risk assessment and process control.
  • The proposed framework supports intelligent food-processing appliances with autonomous, real-time adaptation.
  • This technology enhances both food safety and consumer experience in plant-based beverage consumption.