502
T. Scherzer
the redesign of hotels, office buildings etc. [9, 37, 38]. Therefore, several special
NIR-based analyzers for carpet analysis including both stationary and hand-held
fiber-optic instruments are commercially available on the market.
However, identification of fibers and quantitative analysis of the composition
of textiles is also beneficial in process control during production processes or for
inspection purposes, e.g. in import and export of textile materials. A numbers of
studies is dealing with material analysis for such applications [39–42]. A more recent
application of textile classification is authentication of materials. In particular, in case
of high-value materials the type and the percentage of fibers used in textiles and their
blends becomes more and more important in order to prevent fraudulent supply of
low-grade materials. This does not only apply to certain natural fibers (e.g. wool
versus cashmere), but also to expensive synthetic materials such as aramid fibers
used for example for bulletproof vests [41].
Based on the experiences with the sorting of polymer waste, NIR spectroscopy has
been proven to be an extremely powerful tool for the fast and reliable identification
of fibers and textiles, when it is combined with powerful chemometric algorithms
such as principal component analysis (PCA) for the reduction of the dimensions of
the data set and linear discrimination analysis (LDA) for classification. The majority
of studies have been carried out by conventional NIR spectroscopy, but recently
hyperspectral imaging has been used for data acquisition as well [41]. Most studies
deal with the identification of combined sets of natural and synthetic fibers. Polymers,
which are usually included in such studies, comprise typical fiber materials such as
polyethylene terephthalate (PET), polyamide (PA), polypropylene (PP), polyethylene
(PE), acrylic fibers such as polyacrylonitrile (PAN) [6, 9] and regenerated fibers such
as viscose or tencel, but also more exotic materials such as polylactic acid (PLA) [42]
and poly(p-phenylene terephthalamide) (para-aramid) [41]. Due to their different
molecular structures, the differentiation between these fibers is rather easy. However,
fibers with different, but very similar molecular structures of the same polymer such
as PA 6 and PA 6.6 can be classified as well despite the marginal differences in their
spectra [38]. More sophisticated methods such as neural networks are required to
consider further properties of the fiber materials like color/dyeing or pretreatments
such as heat setting.
21.3.2 Quality Control in Fiber and Textile Production
Blending is a common practice in textile manufacturing in order to combine technical
and economic advantages of two or more fibers in a textile fabric. Therefore, blends
make up a significant part of the textile market. In most cases, natural fibers (e.g.
cotton, wool) are combined with synthetic fibers (usually polyester), but blends of
synthetic fibers (e.g. PET/PA) are common as well. Typically, quality and price of
textile fabrics are closely correlated with the composition of the blend. Monitoring of
the actual composition is a critical issue for the control of conformity with the initial
specifications, but also for process control, since certain properties that are relevant
T. Scherzer
the redesign of hotels, office buildings etc. [9, 37, 38]. Therefore, several special
NIR-based analyzers for carpet analysis including both stationary and hand-held
fiber-optic instruments are commercially available on the market.
However, identification of fibers and quantitative analysis of the composition
of textiles is also beneficial in process control during production processes or for
inspection purposes, e.g. in import and export of textile materials. A numbers of
studies is dealing with material analysis for such applications [39–42]. A more recent
application of textile classification is authentication of materials. In particular, in case
of high-value materials the type and the percentage of fibers used in textiles and their
blends becomes more and more important in order to prevent fraudulent supply of
low-grade materials. This does not only apply to certain natural fibers (e.g. wool
versus cashmere), but also to expensive synthetic materials such as aramid fibers
used for example for bulletproof vests [41].
Based on the experiences with the sorting of polymer waste, NIR spectroscopy has
been proven to be an extremely powerful tool for the fast and reliable identification
of fibers and textiles, when it is combined with powerful chemometric algorithms
such as principal component analysis (PCA) for the reduction of the dimensions of
the data set and linear discrimination analysis (LDA) for classification. The majority
of studies have been carried out by conventional NIR spectroscopy, but recently
hyperspectral imaging has been used for data acquisition as well [41]. Most studies
deal with the identification of combined sets of natural and synthetic fibers. Polymers,
which are usually included in such studies, comprise typical fiber materials such as
polyethylene terephthalate (PET), polyamide (PA), polypropylene (PP), polyethylene
(PE), acrylic fibers such as polyacrylonitrile (PAN) [6, 9] and regenerated fibers such
as viscose or tencel, but also more exotic materials such as polylactic acid (PLA) [42]
and poly(p-phenylene terephthalamide) (para-aramid) [41]. Due to their different
molecular structures, the differentiation between these fibers is rather easy. However,
fibers with different, but very similar molecular structures of the same polymer such
as PA 6 and PA 6.6 can be classified as well despite the marginal differences in their
spectra [38]. More sophisticated methods such as neural networks are required to
consider further properties of the fiber materials like color/dyeing or pretreatments
such as heat setting.
21.3.2 Quality Control in Fiber and Textile Production
Blending is a common practice in textile manufacturing in order to combine technical
and economic advantages of two or more fibers in a textile fabric. Therefore, blends
make up a significant part of the textile market. In most cases, natural fibers (e.g.
cotton, wool) are combined with synthetic fibers (usually polyester), but blends of
synthetic fibers (e.g. PET/PA) are common as well. Typically, quality and price of
textile fabrics are closely correlated with the composition of the blend. Monitoring of
the actual composition is a critical issue for the control of conformity with the initial
specifications, but also for process control, since certain properties that are relevant