참고문헌
- K. Persaud and D. Dodd, Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose, Nature 299 (1982), 352-355. https://doi.org/10.1038/299352a0
- D. Guo et al., A novel breath analysis system based on electronic olfaction, IEEE Trans. Biomed. Eng. 57 (2010), no. 11, . https://doi.org/10.1109/TBME.2010.2055864
- Biomarkers Definitions Working Group, Biomarkers and surrogate endpoints: preferred definitions and conceptual framework, Clin. Pharmacol. Ther. 69 (2001), no. 3, 89-95. https://doi.org/10.1067/mcp.2001.113989
- 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.
- 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. https://doi.org/10.1016/S1570-0232(04)00657-9
- 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. https://doi.org/10.1016/j.snb.2005.01.040
- 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. https://doi.org/10.1109/TBME.2014.2329753
- P. Wang et al., A novel method for diabetes diagnosis based on electronic nose, Biosens. Bioelectron. 12 (1997), no. 9-10, 1031-1036. https://doi.org/10.1016/S0956-5663(97)00059-6
- 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.
- I. T. Jolliffe, Discarding variables in a principal component analysis. I: artificial data, J.R. Stat. Soc. 21 (1972), no. 2, 160-173.
- 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. https://doi.org/10.5369/JSST.2016.25.1.13
- J.-Y. Jeon et al., Chemical sensors array optimization based on Wilks lambda technique, J. Sens. Sci. Technol. 23 (2014), no. 5, 299-304.
- 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. https://doi.org/10.1016/j.jfoodeng.2013.11.017
- 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. https://doi.org/10.1016/S0925-4005(00)00498-6
- 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. https://doi.org/10.5369/JSST.2015.24.4.258
- Y.-G. Song et al., Metal oxide nanocolumns for extremely sensitive gas sensors, J. Sens. Sci. Technol. 25 (2016), no. 3, 184-188. https://doi.org/10.5369/JSST.2016.25.3.184
-
Y.-S. Shim et al., Utilization of both-side metal decoration in close-packed
$SnO_2$ nanodome arrays for ultrasensitive gas sensing, Sens. Actuators B Chem. 213 (2015), 314-321. https://doi.org/10.1016/j.snb.2015.02.103 -
Y.-S. Shim et al., Highly sensitive and selective
$H_2$ and$NO_2$ gas sensors based on surface-decorated$WO_3$ nanoigloos, Sens. Actuators B Chem. 198 (2014) 294-301. https://doi.org/10.1016/j.snb.2014.03.073 - G. Peng et al., Diagnosing lung cancer in exhaled breath using gold nanoparticles, Nat. Nanotechnol. 4 (2009) 669-673. https://doi.org/10.1038/nnano.2009.235
- W. Dillon et al., Origins of breath nitric oxide in humans, Chest 110 (1996), no. 4, 930-938. https://doi.org/10.1378/chest.110.4.930
- 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.
- D. Francesco et al., Breath analysis: trends in techniques and clinical applications, Microchem. J. 79 (2005), no. 1-2, 405-410. https://doi.org/10.1016/j.microc.2004.10.008
- 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.
- 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
피인용 문헌
- A novel fault diagnostic method for analog circuits using frequency response features vol.90, pp.10, 2018, https://doi.org/10.1063/1.5120560
- Collaborative Filtering to Predict Sensor Array Values in Large IoT Networks vol.20, pp.16, 2018, https://doi.org/10.3390/s20164628
- Structural, morphological and gas sensing properties of Zn1−xSnxO thin films by SILAR method vol.127, pp.4, 2021, https://doi.org/10.1007/s00339-021-04354-7