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. 2018 Apr 25;8:6507. doi: 10.1038/s41598-018-24543-4

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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(A) Box plots for combinatorial detection of glucose levels in the presence of alcohol (0–2 standard drinks) as an interferent in human sweat on a glucose sensor at 100 Hz (B) Box plots for combinatorial detection of alcohol (0–2 standard drinks) in the presence of glucose as an interferent in human sweat on an alcohol sensor at 100 Hz (C) Regression plot for human sweat glucose measured by the sweat sensor at 100 Hz and blood glucose measured by Accu-Chek^® Nano SmartView glucose monitoring system in comparison to the actual glucose concentrations presented to them without alcohol (0 drink) (D) Regression plot for human sweat glucose measured by the sweat sensor at 100 Hz and blood glucose measured by Accu-Chek^® Nano SmartView glucose monitoring system in comparison to the actual glucose concentrations presented to them with and without alcohol (0–2 drinks) (E) Bland- Altman analysis for sweat sensor at 100 Hz and Accu-Chek^® for human sweat samples with varying glucose levels in the absence (0 drink) and presence of alcohol (2 drinks) at 100 Hz.