Development of Optical Transducer on Fabrics for The Application of Liquid Clustering Using Reflectance Spectroscopy
Abstract
Recently, usage of fabrics as wearable device, along with their applications are increasing, one example being the detection of bio-analyzes such as blood or sweat. One method used to observe the properties of the material of a fabric is to use the Refcletance Spectroscopy, in which excitation of monochromatic light with a specific wavelength is given to a fabrics. Intensity value is then processed using the PCA method in order to obtain the pattern of the difference between each substrate. The proposed transducer optic system consists of 405nm blueviolet laser as the light source, biconvex lens, Adafruit AS7262 light detector, and Arduino. This system can only detect the difference in substrate content from the occurring light scatter. This system can be applied to various kinds of fabric wearable material with differing scatter intensity values depending on the kind of fabrics. Softer kind of fabric is proposed as material for the wearable device because it gives a high scatter intensity value and constant values in every repetation which results in better data reading.
Keywords: clustering, optical, reflectance, spectroscopy, transducer, wearable.
Full Text:
PDFReferences
Woods, D. A. & Bain, C. D., 2013. Total internal
reflection spectrodcopy for studying soft matter.
Royal Society of Chemistry, pp. 1-26.
Jacob, L. J. & Deigner, H. -. P., 2018.
Nanoparticle and nanosized Structures in
Diagnostic and Therapy. Elsevier, pp. 229-252.
Cortes, V. et al., 2016. A New Internal Quality
Index for Mango and its Predictiom by External
Visible and Near-Infrared Reflection
Specftroscopy. Elsevier, Volume 118, pp. 148158.
lI, j. et al., 2016. Attenuated Total Reflection
Surface-Enhanced
Infrared Absorbtion
Spectroscopy: a Powerful Technique for
Bioanalysis. Springer, pp. 1-8.
Koukouvinos, G. et al., 2017. Development and
Bioanalytigal Applications of a White Light
reflectance Spectroscopy Label-Free Sensing
Platform. MDPI, 7(46), pp. 2-19.
Shi, T. et al., 2014. mopnitoring Arsenic
Contamination in Agricultural Soils With
Reflectance Spectroscopu of Rice Plant.
American Chemical Society, Volume 48, pp.
-6272.
Dor, E. B., Ong, C. & Lau, I. L., 2015.
Reflectance M
Laboratory: Standards and Protocol. Elsevier, pp.
-124.
Mitchell, G. et al., 2013. Assessmentof Histprical
Polymers using Attenuated Total ReflectanmceFouier
Transform Infra-red Spectroscopy with
Principal Component Analysis. Heritage Science
Journal, pp. 1-28.
Wang, C. et al., 2017. Application of Princxipal
Component Analysis to Classify Textile Fibers
Based on UV-Vis Reflectance Spectroscopy.
Springer, pp. 391-395.
Kusumo, L. A., Mujiono, T. & Kusuma, H.,
Low Cost Optical-electronic Sensor
Development Based on Raman Spectroscopy for
Liquid. JAREE, 4(2), pp. 94-99.
Walker, J., Halliday, D. & Resnick, R., 2014.
Reflection and Refraction. In: Fundamentals of
Physics, Tenth Edition. Monaco: s.n., pp. 991 -
Wang, L. et al., 2018. Weaving Sensing Fibers
into Electrochemical Fabric for Real-Time
Health Monitoring. Advance Science News, pp.
-8.
Moyer, J. et al., 2012. Correlatio Between Sweat
Glucose and Blood Glucose in Subjects with
Diabetes. Mary Ann Liebert, Inc, Volume 14, pp.
-402.
Shin, H. et al., 2018. Correlation Between
Cancerous Exocerous and Protein Markers Based
on Surface-Enhanced Raman Spectroscopy
(SERS) and Principal Component Analysis
(PCA). ACS SENSORS, pp. 1-9.
Li, X. et al., 2016. Raman spectroscopy
combined with principal component analysis and
k nearest neighbour analysis for non-invasive
detection of colon cancer. Laser Physics, Volume
, pp. 1-10.
DOI: https://doi.org/10.12962/jaree.v5i2.202
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.