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Infrared spectroscopy, also known as vibrational spectroscopy, is mainly used to determine the types of bonds and functional groups in molecules. In aldehydes and ketones, the carbonyl (C=O) bond shows an absorption around 1710 cm-1. The C=O bond vibration of an aldehyde occurs at lower frequencies than that of a ketone. In addition to the C=O absorption in an aldehyde, the aldehydic C–H bond also gives two peaks in the 2700–2800 cm-1 range. This absorption, coupled with the...
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Toward a User-Accessible Spectroscopic Sensing Platform for Beverage Recognition Through K-Nearest Neighbors

Luca Montaina1, Elena Palmieri1, Ivano Lucarini1

  • 1National Research Council (CNR), Institute for Microelectronics and Microsystems (IMM), 00133 Rome, Italy.

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|July 30, 2025
PubMed
Summary

This study introduces a new spectroscopic sensor system for automatic beverage recognition, improving nutritional monitoring. The compact, non-invasive device uses machine learning for accurate classification, enhancing usability for dietary needs.

Keywords:
IoT devicesKNNbeverage recognitiondiet monitoringmachine learningpublic healthsensing platformsmart cutlery

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Area of Science:

  • Spectroscopy
  • Machine Learning
  • Internet of Things (IoT)

Background:

  • Proper nutrition is crucial for health, but monitoring intake is challenging.
  • Current smartphone apps for food tracking require significant user input and raise privacy concerns.
  • There is a need for automated, non-invasive nutritional monitoring solutions.

Purpose of the Study:

  • To develop a novel, compact spectroscopic sensing platform for automatic beverage recognition.
  • To create a user-friendly and practical alternative to manual nutritional tracking.
  • To enable real-time nutritional monitoring through integration into everyday objects.

Main Methods:

  • Utilized the AS7265x commercial sensor to capture beverage spectral signatures.
  • Employed a K-Nearest Neighbors (KNN) machine learning algorithm for classification.
  • Optimized sensor configuration and KNN parameters, identifying a reduced set of four wavelengths.

Main Results:

  • Achieved over 96% classification accuracy for a diverse range of common beverages.
  • Demonstrated the potential for integration into everyday objects like smart glasses or cups.
  • The developed system is accurate, low-power, and cost-efficient.

Conclusions:

  • The proposed spectroscopic sensing platform offers a viable solution for automated beverage recognition and nutritional monitoring.
  • This technology can be embedded in Internet of Things (IoT) devices for accessible, real-time dietary tracking.
  • The system reduces user input requirements, enhancing practicality and usability for individuals with specific dietary needs.