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Probing Sucrose Contents in Everyday Drinks Using Miniaturized Near-Infrared Spectroscopy Scanners

Published: 14 September 2020 Publication History

Abstract

Near-Infrared Spectroscopy (NIRS) is a non-invasive sensing technique which can be used to acquire information on an object's chemical composition. Although NIRS is conventionally used in dedicated laboratories, the recent introduction of miniaturized NIRS scanners has greatly expanded the use cases of this technology. Previous work from the UbiComp community shows that miniaturized NIRS can be successfully adapted to identify medical pills and alcohol concentration. In this paper, we further extend this technology to identify sugar (sucrose) contents in everyday drinks. We developed a standalone mobile device which includes inter alia a NIRS scanner and a 3D printed clamp. The clamp can be attached to a straw-like tube to sense a liquid's sucrose content. Through a series of studies, we show that our technique can accurately measure sucrose levels in both lab-made samples and commercially available drinks, as well as classify commercial drinks. Furthermore, we show that our method is robust to variations in the ambient temperature and lighting conditions. Overall, our system can estimate the concentration of sugar with ±0.29 g/100ml error in lab-made samples and < 2.0 g/100ml error in 18 commercial drinks, and can identify everyday drinks with > 99% accuracy. Furthermore, in our analysis, we are able to discern three characteristic wavelengths in the near-infrared region (1055 nm, 1235 nm and 1545 nm) with acute responses to sugar (sucrose). Our proposed protocol contributes to the development of everyday "food scanners" consumers.

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 3, Issue 4
December 2019
873 pages
EISSN:2474-9567
DOI:10.1145/3375704
Issue’s Table of Contents
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Publication History

Published: 14 September 2020
Published in IMWUT Volume 3, Issue 4

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Author Tags

  1. Near-Infrared spectroscopy
  2. food scanner
  3. liquid sensing
  4. machine learning
  5. mobile sensing

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