NIR Characterization and Measurement of the Cotton Contentof Dyed Blend Fabrics

Abstract Near infrared (NIR) techniques forthe measurement of cotton-polyester (PET) blendcontent have concentrated on yarn slivers andgreige fabrics rather than the more difficult appli-cation to dyed or printed fabrics. The need for arobust and rapid measurement of the fiber contentin dyed cotton-PET fabrics has been expressed byseveral organizations. Investigations were initiatedto develop NIR techniques to measure the cottoncontent of dyed blend fabrics. NIR measurementswere made on dyed or pigmented cotton-PET fab-rics that comprised a wide range of cotton content,fabric parameters, and patterns/colors. The sam-ples were analyzed on two NIR instruments attwo locations with various reflectance NIR sam-pling systems. Significant spectral differenceswere observed for 100% cotton and 100% PETsamples, and these spectral differences carriedover to the dyed cotton-PET samples with chang-ing cotton content. The impacts of significant dif -ferences in baseline were minimized with the useof advanced chemometric normalization tech-niques. Rapid (less than 5 minutes) and accurateNIR measurements of the blend content in dyedcotton-PET fabrics were developed, with a NIR-laboratory method agreement normally within ±3.0% cotton for nearly 90% and higher of the vali-dation samples.

Key words NIR, polyester, blend, fabrics

Near infrared (NIR) spectroscopy has been used exten-sively for several years in many industries, including chemi-cal, polymer, plastics, pharmaceuticals, agriculture, andtextiles. It is equally adaptable to laboratory, at-line, andon-line analyses. NIR techniques offer several analyticaland instrumental advantages - normally fast, accurate, pre-cise, and non-destructive analyses combined with rugged,easy to maintain, and easy to operate instrumentation. Inaddition, NIR operations often require minimum operatortraining, minimal sample preparation and handling, andthe use of chemometric mathematical models.

James Rodgers'USDA, Agricultural Research Service. Southern RegionalResearch Center New Orleans, LA 70124, USA

Keith BeckNorth Carolina State University. Raleigh, NC 27595 USA

Textile applications of NIR technology have advanceddramatically, to include the determination of polymer/fibertype and identification, cotton maturity, moisture, finish,size pick-up, carpet heatset temperature and type, and cot-ton-polyester (PET) blend content in greige (not dyed norpigmented) fibers, slivers, and yarns [1-7]. The U.S. Cus-

Corresponding author: USDA-ARS-SRRC, 1100 Robert E.Lee Blvd., New Orleans, LA 70124, USA. Tel: 504-286-4407; fax:504-286-4217; e-mail: james.rodgers@ars.usda.gov

Textile Research Journal Vol 79181 675-686 001 10.1177/0040517508090884

www.trl.sagepub.com © 2009 SAGE PublicationsFigures 1-13 appear in color online http://lrj.sagepub.com

Los Angeles, London. New Delhi and Singapore

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toms, independent laboratories/testing facilities, retailers,and other interested concerns have noted the need (anddesire) for a rapid and accurate measurement of the fibercontent in dyed cotton-PET fabrics for both quality assur-ance and Product Validation/Certification. These interestswere brought to the attention of AATCC committeesRA24 (Fiber Analysis) and RA103 (Spectroscopic Tech-nologies). In this paper, results of the initial evaluations onthe feasibility of accurately measuring the cotton contentof dyed blend fabrics by NIR are presented.

NIR Spectroscopy

Located between the visible and infrared (IR) wave-lengths, the primary NIR spectral region is normally con-cerned with the region from 1100-2500 nm [1, 3, 8]. Theprimary chemical groups observed by NIR spectroscopyare CH, NH, and OH groups, with strong absorbencesalso observed for carbonyl and amide groups. NIR spectraoften contain broad rather than sharp spectral features, asa result of combination and overtone bands. These broadspectral features necessitate the use of advanced chemo-metric regression methods (Derivative Math, Multiple Lin-ear Region (MLR), Partial Least Squares (PLS), etc.) toobtain accurate and precise NIR calibrations for the prop-erty(ies) of interest.

The NIR method is not a primary analytical method -the NIR spectra must be calibrated to a reference method.NIR techniques can be used for both qualitative and quan-titative determinations. Quantitative measurements areused to determine the quantity or amount of the particularproperty of interest (e.g. the cotton content in a dyed cot-ton-PET blend fabric). An example of qualitative analysesis the identification of the fiber types present in a fabric orfiber blend. For solids, the NIR spectral response is themeasurement of the diffuse reflectance from the sample'ssurface, R, as the wavelength, ?L, is changed. The diffusereflectance is converted to absorbance, A or log (1/R),yielding an A versus X NIR spectrum. Once spectra havebeen collected, the procedure used to develop robust NIRcalibrations consists of three main procedures - spectralanalysis (spectral comparison of spectra of samples withdifferent property values/contents), calibration develop-ment, and calibration validation (prediction on samplesnot used in calibration development).

ExperimentalA series of 300 woven dyed or pigmented cotton-PETblend fabrics (2" X 2" squares) was supplied by Sears-Can-ada. The swatches covered a very wide blend range (from—10% cotton/90% PET to —90% cotton/10% PET) and a

wide and diverse range of fabric parameters, patterns, andcolors. All samples had been washed to remove surface fin-ishes. Of the 300 dyed fabrics, 265 samples of wide blendcontent range were used in calibration development (Cali-bration Set), and the remaining 35 fabrics (also of wideblend content range) were used for calibration validation(Prediction Set). The reference method for NIR calibra-tion development was the acid-burnout method, in whichthe cotton in the dyed cotton-PET fabrics was dissolvedwith 70% H2SO4, and the residual PET determined gravi-metrically [9]. The reference values for cotton/PET con-tent were provided by Sears-Canada. The time-consumingacid-burnout reference method can often require 2-8hours to complete, and it involves the use of concentratedchemicals that require special care and disposal.

FOSS NlRSystems 6500 scanning spectrophotometersat two locations were used to perform all NIR analyses onthe same set of 300 cotton-PET dyed fabrics [10]. Threesampling systems were also evaluated. At Laboratory One,an interactive fiber-optic probe and the standard, "static"method were used to measure all samples. At LaboratoryTwo, the SmartProbe interactive fiber optic probe was usedto measure all samples. The interactive probe was approxi-mately 300 mm long and had a 19 mm diameter. The Smart-Probe was approximately 270 mm long and had a 7 mmdiameter. In addition, the SmartProbe was connected to apistol grip that contained a trigger to begin the sampleanalyses. Additional details for each sampling system areavailable from the vendor.

With fiber-optic probes, the NIR instrument is "brought"into intimate contact with the surface of the sample and thespectrum measured. With the static method, the sample is"brought" to the NIR instrument, placed against the dif-fuse reflectance port, and the spectrum collected. For thestatic measurements, each sample was read in duplicate;for the probe measurements, each sample was read fourtimes (different fabric location for each reading). The dyedfabric samples were measured from 1100-2500 nm. Due tofiber-optic limitations, only the spectral region 1100-2200nm could be used for NIR calibrations with both probesampling systems. Sample analysis time was less than 30seconds per NIR reading.

A key component in any analytical method develop-ment and implementation in the laboratory and/or manu-facturing environment is the development of an agreed"end-state criteria" among all parties. The end-state crite-ria are the benchmark and decision criteria that determinethe successful development of the analytical technique.The end-state criteria for the quantitative measurement ofthe cotton content in dyed commercial cotton-PET blendfabrics were 1) minimal sample preparation, 2) a totalanalysis time of < 5 minutes, and 3) a method agreementbetween the lab and NIR-determined cotton content inwhich ^! 80% of the dyed fabric samples agree within ±5.0% cotton.

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Using the FOSS VISION chemometric software pack-age, both MLR and PLS NIR calibrations were developed.MLR is a linear regression model of constituent/propertyvalue and absorbance in which the absorbance at morethan one wavelength is used. PLS is a nonlinear multi-wavelength/wavelength region regression method in whichthe spectral and constituent/property data are simultane-ously modeled in steps that account for spectral signal andconstituent values [10]. The Prediction Set was used to val-idate the robustness of the initial NIR models. Using thePrediction Set, the major statistical determinations of cali-bration robustness and "goodness" were R 2 (correlationcoefficient), Standard Deviation of Differences (SDD, aresidual analysis composed of the standard deviation of thedifferences between the NIR-determined blend contentand the lab blend content for the prediction samples), andthe number of samples outside of a ± 5.0% method agree-ment between the NIR-determined blend content and thelab blend content for each prediction sample (outliers). Arobust and "universal" NIR calibration yields a high R 2, alow SDD, and a low number of outliers.

Results and Discussion

NIR Spectral AnalysisPrior to calibration development and detailed sample anal-yses, the NIR spectra of 100% cotton and 100% PET fiberswere measured and compared. Figure 1 demonstrates thatthe differences between the cotton and PET NIR spectrawere significant and readily observed in several spectralregions. These readily observed and distinct NIR spectraldifferences between greige cotton and PET fibers werevery encouraging for calibration development. These spec-tral differences demonstrate why NIR technology has beensuccessful in the determination of the blend content ofgreige fibers, slivers, yarns, and fabrics. However, the pres-ence of differences in coloration methods (dyes, pigments,etc.), fabric parameters (construction, patterns, etc.), andsurface effects (delusterants, remaining finishes, etc.) maydramatically impact the NIR spectral response among thesamples. These matrix differences can make the successfuldevelopment of NIR calibrations very problematic fordyed cotton-PET blend fabrics.

Previous investigations on synthetic carpet fibers haveshown that these matrix effects do not change the locationof the primary absorbance wavelengths and bands for thepolymer type, but they can dramatically impact the inten-

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sity of the absorbance in these regions [11]. These changesor deviations in expected absorbences for a given polymertype content can dramatically and adversely impact thequantitative responses and capabilities of the NIR calibra-tions, and they must be accounted for or minimized. Inorder to determine the applicability of those observationson synthetic carpet fibers to cotton and PET fibers and tocotton-PET blends, several different types of cotton andPET fibers and fabrics were measured on the NIR analyz-ers, and their spectra compared. For the cotton samples,both dyed and undyed cotton fabrics - many at differentprocessing steps (mercerized, bleached, napped, etc.) - dis-played similar key spectral features over the entire spec-trum, as shown in Figure 2. The blue dyed knit sampleexhibited dye-induced initial baseline shifts below —1400nm, but its spectrum conformed to the absorbance spectraof the other cotton samples above 1400 nm. DerivativeMath is often used to minimize spectral differences and toaccentuate small differences between samples that exhibitsmall-to-moderate baseline differences or shifts. SecondDerivative Math treatments were applied to the absorb-ance spectra for the cotton samples, and the results arepresented in Figure 3. With the exception of the blue knitsample below 1150 nm, excellent spectral agreement wasobserved among the various cotton fiber and fabric sam-ples. As observed with synthetic carpet fibers, the matrix

effects for the greige and dyed fabrics did lead to differ-ences in the spectral absorbences and derivative intensitiesin several spectral regions, but the matrix effects did notchange the location of the primary absorbance and deriva-tive wavelengths and bands for the polymer type (e.g. cot-ton and PET), regardless of the source or type of cottonand PET In Figures 4 and 5, similar absorbance and deriv-ative spectral results were observed for PET fibers and fab-rics. The differences observed between the cotton and PETfibers and fabrics in Figures 1-5 were also present in thecotton-PET blend fabrics (Figure 6 as an example). Thesereadily observed and distinct NIR spectral differencesbetween cotton and PET fibers and fabrics and the dyedcotton-PET blend fabrics indicated that the quantitativedetermination of the cotton content in the dyed cotton-PET blend fabrics may well be feasible.

The 265 dyed cotton-PET blend fabrics of the calibra-tion set were measured on the 6500 NIR analyzers from1100-2500 nm. Significant spectral differences wereobserved with increasing cotton content at several spectralpositions, especially in the 1400-1700 nm and 1900-2200nm spectral regions. A few samples exhibited much differ-ent baselines compared to the majority of the fabric sam-ples, as shown for the 48.0% cotton sample in Figure 6.These "baseline shifts" were most prevalent below 1900 nm.It is probable that these baseline shifts for the "outlier"

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fabrics were caused by differences in fabric construction,surface effects (e.g. delusterants), and/or coloration method(e.g. dyed or pigmented). As mentioned previously, Deriva-tive Math is often used to minimize spectral differencescaused by surface inhomogeneities and to accentuate smalldifferences among samples. This technique works very wellfor samples with small-to-moderate baseline shifts. Usingsecond Derivative Math, the observed NIR baseline shiftsfor the "outlier" samples were significantly minimized, but,as shown in Figure 7, the large baseline shifts could not becompletely overcome and corrected by use of a DerivativeMath. Advanced and sophisticated normalization tech-niques have been developed and optimized, and they arenow available in many chemometric NIR software pack-ages. One advantage of a normalization treatment is itsability to minimize spectral differences due to baselineshifts. Standard Normal Variate (SNV) is one of the nor-malization math treatments in VISION [lO]. SNV is a scat-ter correction method that normalizes spectra in a data setby mean-centering each spectrum and dividing it by theset's standard deviation.

Figure 8 shows that the spectral differences and mis-order for cotton content observed with the 48.0% cottonsample in Figure 6 were eliminated when SNV was appliedto the samples [121. When SNV was combined with a see-

ond derivative, the resulting normalized derivative mathtreatments for these samples yielded well-spaced and prop-erly ordered derivative spectra for increasing cotton con-tent, as shown in Figure 9.

Quantitative AnalysesAs noted previously, the spectral differences between cot-ton and polyester - regardless of the source/type of cottonand polyester - by themselves indicated a good potentialfor development of NIR calibrations for the blend contentin cotton-PET fabrics. However, the noted impacts of"color"/dyed variables on the NIR spectra make the devel-opment of robust NIR calibrations significantly more diffi-cult than the method development for greige/undyedfabrics. In order to determine the impact of the "baselineoutlier" samples on calibration development and NIR pre-diction results (and to minimize these potential impacts),both standard and SNV math treatments were used inmethod development and their results compared. The twoapproaches in NIR calibration development were under-taken because 1) advanced chemometric normalizationtechniques can act to minimize baseline deviations andimprove the robustness of NIR calibrations, 2) not all NIRchemometric software contain advanced normalization

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NIR Characterization and Measurement of the Cotton Content of Dyed Blend Fabrics J. Rodgers and K. Beck 683

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math treatments, and 3) if only a few samples are baselineoutliers, the ^! 80% method agreement within ± 5.0% cot-ton can still be achieved with standard math treatments.The NIR calibrations were developed with a 265 sampleCalibration Set and validated with the 35 sample Predic-tion Set, with both sets containing samples with a wide cot-ton content range and diverse colors/patterns. Spectralanalysis of the Prediction Set exhibited several outliers. Infact, Figure 10 (Prediction Set absorbance spectra) illus-trates that three of the prediction samples were spectrallysimilar to the baseline outlier cotton sample observed inFigure 6. When the SNV-second Derivative Math treat-ment was used, the spectral deviations observed with thethree outlier samples in the Prediction Set were minimizedsignificantly (Figure II). The resulting SN V-second deriva-tive spectra were spectrally similar to the spectral resultsobserved previously in Figure 9.

The standard and SNV NIR calibrations were validatedon the 35 prediction samples, and the results are displayedin Figures 12 and 13. Development of the optimal calibra-tions involved the use of second derivative spectra and PLSmath modeling for both the standard and SNV normalizedcalibrations. Method agreement between the NIR andgravimetric measurements of cotton content in cotton-PET blend dyed fabrics was excellent, with over 90% of thevalidation samples agreeing within ± 5.0% cotton. Similarresults were obtained with both static and fiber-optic probe

sampling systems. Total analysis time for the NIR analyseswas less than five minutes per sample (duplicates). There-fore, the rapid and accurate measurement by NIR of cot-ton content in cotton-PET blend fabrics after dyeing wasshown to be feasible. Based on SDD and the number ofoutliers > ± 5.0% cotton for the dyed fabrics, the use ofSNV normalized math treatments yielded superior predic-tion results. The SDD was improved by over 40% for bothsampling systems when SNV was utilized, and the numberof outliers > ± 5.0% cotton was reduced to zero. A tightermethod agreement is ± 3.0% cotton. For both samplingsystems, the number of prediction samples agreeing towithin ± 3.0% cotton for the NIR and gravimetric meth-ods increased from —80-85% for the standard NIR cali-brations to nearly 90% or higher for the SNV normalizedcalibrations.

A comparison of the predictive capabilities of the vari-ous math modeling and normalization systems was per-formed with the fiber optic SmartProbe. The results for thevalidation samples are presented in Table 1. The distinctsimilarities between the results from both laboratories sup-ported the feasibility of a "universal" or general NIR meas-urement of cotton-PET content that may be applicable todifferent NIR units and sampling systems. Overall, lowerand more consistent Standard Error of Prediction (SEP)results were obtained with the PLS calibrations comparedto the MLR calibrations, whether or not SNV normaliza-

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Figure 12 Dyed cotton content in blends. Prediction Set, static sampling system, standard second derivative NIR calibra-tion. R2 = 0.96, SDD = 2.90. No. > ± 5 = 2. No. > ± 3 = 5.

NR Characterization and Measurement of the Cotton Content of Dyed Blend Fabrics J. Rodgers and K. Beck 685

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Table 1 Prediction results from spectra obtained with the

SmartProbe.

Model Math AdditionalSEP (%) R2

type model normalization

MLR FDC None 2.9 0.977

MLR SDC None 3.9 0.960

MLR FDC SNV 2.2 0.987

MLR SDC SNV 4.4 0.949

PLS FDC None 3.1 0.975

PLS SDC None 2.9 0.978

PLS FDC SNV 22 0.988

PLS SDC SNV 2.6 0.982

MLR = Multiple Linear Regression; PLS = Partial Least Squaresregression; FDC = First Derivative Math treatment; SDC = SecondDerivative Math treatment; SNV = Standard Normal Variate mathtreatment; SEP = Standard Error of Prediction.

tions were used. The SEP range was lower for the PLS cal-ibrations (2.2-3.1%) compared to the MLR calibrations(2.2-4.4%), and the SEP results were often lower for theSNV calibrations compared to the corresponding deriva-tive only calibrations.

Conclusions

Method feasibility for the use of NIR technology to rapidlyand accurately measure the blend content of cotton-PETdyed fabrics was established. NIR analyses were performeddirectly on the dyed fabric surface (minimal sample prepa-ration) at two locations with three sampling systems on adiverse set of 300 cotton-PET dyed fabrics. Sample analysistime was less than five minutes for duplicate analyses, asignificant reduction in analysis time compared to the 2hours or longer analysis time for the laboratory gravimetricmethod. The dyed blend fabrics exhibited a wide range ofcotton content, fabric parameters, and patterns/colors. Sig-nificant spectral differences were observed between cottonand PET samples, and these differences were presentregardless of the type of cotton or PET measured. Thesedifferences were also observed at key spectral positions inthe blend fabrics. Distinct baseline differences, especiallybelow 1900 nm, were observed for a small number of fab-rics, most likely due to fabric parameters, delusterants,and/or coloration differences among the fabrics. Advancedchemometric normalization and Derivative Math tech-niques were combined with PLS math modeling to minimizethe baseline differences. These spectral manipulations led toNIR calibrations with improved predictive capabilities androbustness. The NIR-gravimetric method agreement was

EM 686 Textile Research Journal 79(8)

within ± 5.0% cotton for over 95% of the validation sam-ples and was within ± 3.0% cotton for nearly 90% andhigher of the validation samples. With an end-state criteriaof a NIR-gravimetric method agreement of ^! 80% of thesamples within ± 5.0% cotton, these prediction results(especially the ± 3.0% cotton agreement results) were veryencouraging. These results at both locations support thefeasibility of a "universal" or general NIR measurement ofcotton-PET content that may he applicable to differentNIR units and sampling systems.

AcknowLedgementsThe authors wish to gratefully acknowledge the support ofMesser. Tom Hong, Sears-Canada, for the use of the 300dyed woven blend fabrics. We also wish to acknowledge theoutstanding support, work, and assistance of Messers.Rafael Barraza and Howard Ashcraft of Solutia and Mr.Chris Cazzola, a NCSU undergraduate student, for theirwork with the cotton-PET samples.

DisclaimerThe use of a company or product name is solely for thepurpose of providing specific information and does notimply approval or recommendation by the United StatesDepartment of Agriculture to the exclusion of others.

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2. Beck, K., An Overview of Textile Applications of NIR Tech-nology: Theory/Instrumentation and Qualitative Applications,AATCC Textile Applications of NIR Technology Symposium,Asheville, North Carolina, USA (1996).

3. Burns, D., Modern Near Infrared Reflectance Analysis, ACSShort Course, New Orleans, Louisiana, USA (1985).

4. Montalvo, J., and Von Hoven, T. Analysis of Cotton, in"Near-infrared Spectroscopy in Agriculture," (Eds. C. Rob-erts, J. Workman, and J. Reeves), American Society of Agron-omy, Agronomy Monograph No. 44, Madison, Wisconsin,USA, pp. 671-728 (2004).

5. Rodgers, J., An Overview of Textile Applications of NIRTechnology: Quantitative Applications, in "Book of Papers ofthe AATCC National NIR Technology Symposium," p. 29(1996).

6. Rodgers, J.. and Ghosh. S.. NIR Analysis of Textiles, in"Handbook of Near-infrared Analysis." 3 Edn., (Eds. D.Burns and E. Ciurczak), CRC Press, Boca Raton, FL, USA.(2008).

7. Tincher, W. and Luk. A., N1RS Analysis of Cotton/PolyesterYarns, Textile Chem. Color 17(10), 25-29 (1985).

8. Perkampus, H., NIR Spectroscopy, in "Encyclopedia of Spec-troscopy," VCH, New York, USA, pp. 387-388 (1993).

9. Technical Manual of the American Association of TextileChemists and Colorists, TM-20A (2005).

10. VISION Software and Operation Manual, FOSS NIRSy5-tcms, Silver Spring, Maryland. USA (2001).

11. Rodgers, J., Influences of Carpet and Instrumental Parame-ters on the identification of Carpet Face Fibers by NIR,AATCCRee 6.27-31 (2002).

12. Rodgers, J., and Beck. K., Rapid Determination by NIR of theCotton Content of Blend Fabrics after Dyeing, 2005 BeltwideCotton Conference, New Orleans, Louisiana. USA (2005).