Sensor array optimization techniques for exhaled breath analysis to discriminate diabetics using an electronic nose |
Jeon, Jin-Young
(Division of Electronics, Information and Communication Engineering, Kangwon National University)
Choi, Jang-Sik (Division of Electronics, Information and Communication Engineering, Kangwon National University) Yu, Joon-Boo (Division of Electronics, Information and Communication Engineering, Kangwon National University) Lee, Hae-Ryong (SW& Content Research Laboratory, Electronics, Information and Communication Engineering) Jang, Byoung Kuk (Department of Internal Medicine, Keimyung University) Byun, Hyung-Gi (Division of Electronics, Information and Communication Engineering, Kangwon National University) |
1 | K. Persaud and D. Dodd, Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose, Nature 299 (1982), 352-355. DOI |
2 | D. Guo et al., A novel breath analysis system based on electronic olfaction, IEEE Trans. Biomed. Eng. 57 (2010), no. 11, . DOI |
3 | Biomarkers Definitions Working Group, Biomarkers and surrogate endpoints: preferred definitions and conceptual framework, Clin. Pharmacol. Ther. 69 (2001), no. 3, 89-95. DOI |
4 | Z. Wang and C. Wang, Is breath acetone a biomarker of diabetes? A historical review on breath acetone measurements, J. Breath Res. 7 (2013), no. 3, pp. 037109:1-037109:18. |
5 | C. Deng et al., Determination of acetone in human breath by gas chromatography-mass spectrometry and solid-phase microextraction with on-fiber derivatization, J. Chromatogr. B 810 (2004), no. 2, 269-275. DOI |
6 | J.-B. Yu et al., Analysis of diabetic patient's breath with conducting polymer sensor array, Sens. Actuators B Chem. 108 (2005), no. 1-2, 305-308. DOI |
7 | K. Yan et al., Design of a breath analysis system for diabetes screening and blood glucose level prediction, IEEE Trans. Biomed. Eng. 61 (2014), no. 11, 2787-2795. DOI |
8 | P. Wang et al., A novel method for diabetes diagnosis based on electronic nose, Biosens. Bioelectron. 12 (1997), no. 9-10, 1031-1036. DOI |
9 | E. I. Mohamed et al., Predicting type 2 diabetes using an electronic nose-based artificial neural network analysis, Diabetes Nutr. Metab. 15 (2002), no. 4, 215-221. |
10 | I. T. Jolliffe, Discarding variables in a principal component analysis. I: artificial data, J.R. Stat. Soc. 21 (1972), no. 2, 160-173. |
11 | J.-S. Choi, J. Y. Jeon, and H. G. Byun, Investigation of chemical sensor array optimization method for DADSS, J. Sens. Sci. Technol. 25 (2016), no. 1, 13-19. DOI |
12 | J.-Y. Jeon et al., Chemical sensors array optimization based on Wilks lambda technique, J. Sens. Sci. Technol. 23 (2014), no. 5, 299-304. |
13 | Y. Yin et al., A sensor array optimization method of electronic nose based on elimination transform of Wilks statistic for discrimination of three kinds of vinegars, J. Food Eng. 127 (2014), 43-48. DOI |
14 | A. Chaudry, T. M. Hawkins, and P. J. Travers, A method for selecting an optimum sensor array, Sens. Actuators B Chem. 69 (2000), no. 3, 236-242. DOI |
15 | H.-J. Lim et al., A step-wise elimination method based on Euclidean distance for performance optimization regarding to chemical sensor array, J. Sens. Sci. Technol. 24 (2015), no. 4, 258-263. DOI |
16 | Y.-G. Song et al., Metal oxide nanocolumns for extremely sensitive gas sensors, J. Sens. Sci. Technol. 25 (2016), no. 3, 184-188. DOI |
17 |
Y.-S. Shim et al., Highly sensitive and selective |
18 | SUPELCO, Sigma-Aldrich Co., Solid Phase Microextraction Fiber Assemblies, 1999, accessed November 22, 2017, available at https://www.sigmaaldrich.com/content/dam/sigma-aldrich/docs/Sigma/General_Information/1/t794123.pdf. |
19 | SUPELCO, Sigma-Aldrich Co., Selection Guide for Supelco SPME Fibers, 2018, available at accessed April 12, 2018, https://www.sigmaaldrich.com/technical-documents/articles/analytical/selecting-spme-fibers.html |
20 |
Y.-S. Shim et al., Utilization of both-side metal decoration in close-packed |
21 | G. Peng et al., Diagnosing lung cancer in exhaled breath using gold nanoparticles, Nat. Nanotechnol. 4 (2009) 669-673. DOI |
22 | W. Dillon et al., Origins of breath nitric oxide in humans, Chest 110 (1996), no. 4, 930-938. DOI |
23 | A. Gibson et al., Deeplearning4j, A Beginner's Guide to Eigenvectors, PCA, Covariance and Entropy, 2016, accessed July 18, 2017, available at https://deeplearning4j.org/eigenvector. |
24 | D. Francesco et al., Breath analysis: trends in techniques and clinical applications, Microchem. J. 79 (2005), no. 1-2, 405-410. DOI |
![]() |