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Variables selection for quantitative determination of cotton content in textile blends by near infrared spectroscopy
- Source :
- Infrared Physics & Technology. 77:65-72
- Publication Year :
- 2016
- Publisher :
- Elsevier BV, 2016.
-
Abstract
- Investigations were initiated to develop near infrared (NIR) techniques coupled with variables selection method to rapidly measure cotton content in blend fabrics of cotton and polyester. Multiplicative scatter correction (MSC), smooth, first derivative (1Der), second derivative (2Der) and their combination were employed to preprocess the spectra. Monte Carlo uninformative variables elimination (MCUVE), successive projections algorithm (SPA), and genetic algorithm (GA) were performed comparatively to choose characteristic variables associated with cotton content distributions. One hundred and thirty-five and fifty-nine samples were used to calibrate models and assess the performance of the models, respectively. Through comparing the performance of partial least squares (PLS) regression models with new samples, the optimal model of cotton content was obtained with spectral pretreatment method of 2 Der-Smooth-MSC and variables selection method of MCUVE-SPA-PLS. The correlation coefficient of prediction (rp) and root mean square errors of prediction (RMSEP) were 0.988% and 2.100%, respectively. The results suggest that NIR technique combining with variables selection method of MCUVE-SPA has significant potential to quantitatively analyze cotton content in blend fabrics of cotton and polyester; moreover, it could indicate the related spectral contributions.
- Subjects :
- Correlation coefficient
010401 analytical chemistry
Near-infrared spectroscopy
Monte Carlo method
Regression analysis
02 engineering and technology
021001 nanoscience & nanotechnology
Condensed Matter Physics
01 natural sciences
Atomic and Molecular Physics, and Optics
0104 chemical sciences
Electronic, Optical and Magnetic Materials
Root mean square
Partial least squares regression
0210 nano-technology
Biological system
Selection (genetic algorithm)
Mathematics
Second derivative
Subjects
Details
- ISSN :
- 13504495
- Volume :
- 77
- Database :
- OpenAIRE
- Journal :
- Infrared Physics & Technology
- Accession number :
- edsair.doi...........2280907b07e097b8177553b6c94836b4