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Main Title Chemometrics applied to the discrimination of synthetic fibers by microspectrophotometry /
Author Reichard, Eric Jonathan,
Publisher Purdue University,
Year Published 2013
OCLC Number 879575764
Subjects Microspectrophotometry--Research ; Chemometrics--Research ; Dyes and dyeing--Chemistry ; Acrylic fibers--Analysis--Methodology ; Polyester fibers--Analysis--Methodology ; Polymers--Structure ; Chemistry, Analytic--Quantitative--Statistical methods ; Calibration--Research--Analysis--Methodology ; Multivariate analysis--Research--Analysis--Methodology
Internet Access
Description Access URL
Library Call Number Additional Info Location Last
ELBM  QD75.4.C45R45 2013 AWBERC Library/Cincinnati,OH 05/19/2014
Collation xii, 99 leaves : color illustrations ; 28 cm
Cover title. May 2013. Thesis (M.S.)--Purdue University, 2013. Includes bibliographical references (leaves 61-68).
Contents Notes
Microspectrophotometry is a quick, accurate, and reproducible method to compare colored fibers for forensic purposes. The use of chemometric techniques applied to spectroscopic data can provide valuable discriminatory information especially when looking at a complex dataset. Differentiating a group of samples by employing chemometric analysis increases the evidential value of fiber comparisons by decreasing the probability of false association. The aims of this research were to (1) evaluate the chemometric procedure on a data set consisting of blue acrylic fibers and (2) accurately discriminate between yellow polyester fibers with the same dye composition but different dye loadings along with introducing a multivariate calibration approach to determine the dye concentration of fibers. In the first study, background subtracted and normalized visible spectra from eleven blue acrylic exemplars dyed with varying compositions of dyes were discriminated from one another using agglomerative hierarchical clustering (AHC), principal component analysis (PCA), and discriminant analysis (DA). AHC and PCA results agreed showing similar spectra clustering close to one another. DA analysis indicated a total classification accuracy of approximately 93% with only two of the eleven exemplars confused with one another. This was expected because two exemplars consisted of the same dye compositions. An external validation of the data set was performed and showed consistent results, which validated the model produced from the training set. In the second study, background subtracted and normalized visible spectra from ten yellow polyester exemplars dyed with different concentrations of the same dye ranging from 0.1-3.5% (w/w), were analyzed by the same techniques. Three classes of fibers with a classification accuracy of approximately 96% were found representing low, medium, and high dye loadings. Exemplars with similar dye loadings were able to be readily discriminated in some cases based on a classification accuracy of 90% or higher and a receiver operating characteristic area under the curve score of 0.9 or greater. Calibration curves based upon a proximity matrix of dye loadings between 0.1-0.75% (w/w) were developed that provided better accuracy and precision to that of a traditional approach.