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Noninvasive Glucose Monitoring of the Aqueous Humor of the Eye: Part I. Measurement of Very Small Optical Rotations

Rabinovitch, B, March, W F, Adams, Robert L·1982·Diabetes Care

Abstract

We have described the concept of using the aqueous humor glucose as a measure of the blood glucose concentration, with a view to developing a noninvasive glucose monitor for diabetic individuals. We have conceived of a scleral lens that houses a light source, polarizers, other electro-optic units, and a light detector, and which measures the optical rotation of the aqueous humor continuously. We have built an optical bench mock-up of the glucose sensor and assessed the limits of its capabilities. We have described a physical method, employing the Faraday effect, that modulates the incident light and uses a compensator to introduce a feedback mechanism giving a null-point technique capable of measuring extremely small rotations with an accuracy of 0.4 s of arc. We have used this and have measured the optical rotations of glucose solutions from 0.02 to 0.1%, and have demonstrated linearity in both cases. Miniaturization of the technique is discussed.

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Metadata

Authors
Rabinovitch, B., March, W. F., & Adams, R. L.
Venue
Diabetes Care
Published
1982-05-01
Volume
5
Issue
3
Pages
254-258
ISSN
  • 0149-5992 (ISSN-L)
  • 1935-5548
OpenAlex
W2059081738
Cited by
179
References
0
FWCI
0.55
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Noninvasive Glucose Monitoring of the Aqueous Humor of the Eye: Part I. Measurement of Very Small Optical Rotations. https://app.cochat.ai/papers/10.2337/diacare.5.3.254
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Rabinovitch, B., March, W. F., & Adams, R. L. (1982). Noninvasive Glucose Monitoring of the Aqueous Humor of the Eye: Part I. Measurement of Very Small Optical Rotations. Diabetes Care. https://doi.org/10.2337/diacare.5.3.254

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.