Rapid, Nondestructive Denim Fiber Bundle Characterization Using Luminescence Hyperspectral Image Analysis Randi E. Deuro,a Kristen M. Leiker,b Yanhui Wang,a Nicholas J. Deuro,a Tammy M. Milillo,a Frank V. Brighta,* a Department of Chemistry, Natural Sciences Complex, University at Buffalo, State University of New York, Buffalo, NY 14260-3000 USA b PS 307 East High School, Buffalo Public Schools, 820 Northampton Street, Buffalo, NY 14211-1307 USA

An investigation into the performance of luminescence-based hyperspectral imaging (LHSI) for denim fiber bundle discrimination has been conducted. We also explore the potential of nitromethane (CH3NO2) -based quenching to improve discrimination, and we determine the quenching mechanism. The luminescence spectra (450–850 nm) and images from the denim fiber bundles were obtained with excitation at 325 or 405 nm. LHSI data were recorded in less than 5 s and subsequently assessed by principal component analysis or rendered as red, green, blue (RGB) component histograms. The results show that LHSI data can be used to rapidly and uniquely discriminate between all the fiber bundle types studied in this research. These non-destructive techniques eliminate extensive sample preparation and allow for rapid hyperspectral image collection, analysis, and assessment. The quenching data also revealed that the dye molecules within the individual fiber bundles exhibit dramatically different accessibilities to CH3NO2. Index Headings: Textile; Fiber bundle; Luminescence hyperspectral imaging; LHSI; Principal component analysis; PCA; Red, green, blue color space; RGB color space; Histograms; Nitromethane (CH3NO2) quenching; Time-resolved fluorescence.

INTRODUCTION Textiles are useful pieces of evidence in crime scene reconstruction.1,2 As such, analytical techniques that can discriminate between similar yarns and fiber bundles are valuable in forensic science.1–8 When the yarns and fiber bundles are derived from different materials (e.g., cotton, nylon, polyester, acrylic) one can exploit spectroscopic methods like Fourier transform infrared (FT-IR) spectroscopy,1,2 polarized light microscopy,3,4 and Raman spectroscopy.5,6 These techniques are generally non-destructive, maintain sample integrity, minimize sample handling, and allow for material discrimination among fiber bundle samples. However, many textile types are treated with one or more dyes/pigments to create unique coloration.7,8 As such, the base textile might remain the same, but the materials are dyed differently. Each dye, of course, exhibits unique electronic absorbance and (potentially) luminescence spectra,9–11 and these spectra may be used to provide discriminatory power between samples. Received 1 May 2014; accepted 27 June 2014. * Author to whom correspondence should be sent. E-mail: chefvb@ buffalo.edu. DOI: 10.1366/14-07580

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However, electronic spectra are normally broad,9,11 and they can be strongly overlapping for mixtures. To address this issue dye analysis is often performed following their extraction from the textile material.12–17 As examples, researchers have previously used thin layer chromatography,12,13 high performance liquid chromatography,14,15 and capillary electrophoresis16,17 to separate extracted dye mixtures. The extracted dyes are subsequently analyzed by mass spectrometry18 or ultraviolet–visible (UV–Vis) spectroscopy.19 While powerful, these extraction methods require sample preparation, they are time consuming, and they ultimately compromise specimen integrity. There are also non-destructive spectroscopic methods for discriminating dye-treated materials.4,6,10,19 For example, UV–Vis spectroscopy can be used in a nondestructive manner to characterize dyed fibers.6,10,19 However, two dyes having similar chemical structures could exhibit indistinguishable UV–Vis absorption spectra. Single-point microspectrophotometry19,20 is another technique that can be used for fiber bundle dye discrimination. While this method may yield immediate information about spectral differences between two samples, the inherent issue lies with the actual sample being analyzed. As an example, opaque and pale samples often yield broad and indistinct electronic absorbance spectra.20 Multi- and hyperspectral imaging (M/HSI) techniques provide three-dimensional (3D) (x,y,k) image data (called an image cube) for every pixel within an imaged field of view.21,22 MSI imaging methods have been used previously for fiber/yarn analysis.23,24 For example, researchers have used UV–Vis absorbance-based MSI to discriminate yarn types21,24 without affecting sample integrity. Luminescence (L) -based LM/HSI has been used extensively in the biological sciences25–27 and also in forensic science to discriminate forensic evidence from its background24,28,29 (e.g., footprint and fingerprint analysis); however, it does not appear as if LM/HSI has been used for fiber bundle discrimination. A number of methods exist for assessing multi- and hyperspectral image data.25,30–32 Principal component analysis (PCA)30,31 and red, green, blue (RGB) histogram renderings32,33 are among the more well-established image analysis methods. PCA, a multivariate statistical analysis method, uses sample spectra and principal component (PC) identification and contributions to discriminate among observed patterns and differentiate

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FIG. 1. Graphic demonstrating the effects of a quenching species (at 3 M concentration) on several hypothetical mixture emission spectra. Solid lines denote the luminescence emission spectra before quenching. Dashed lines represent the luminescence emission spectra during quenching. (a) Luminescent species S1 and S1 exhibit equal contributions in the absence of quencher, but they have different Ksv values (Ksv,1 = 0.1 M1, Ksv,2 = 0.4 M1). (b) Same as panel (a), but the Ksv,1 and Ksv,2 values are interchanged. (c) Same as panel (a), but the contributions of the unquenched species (S1:S2) is 1:3. (d) Recovered I0/I (kem) profiles for the systems shown in panels (a)–(c). These profiles are clearly unique and also intensity independent.

samples. RGB rendering discriminates between samples by creating histograms of the individual unmixed red, green, and blue components that make up the sample image. In this paper, we report the use of a standard epiluminescence microscope coupled to multispectral imaging system to rapidly record LM/HSI (450–850 nm) data from fiber bundles. We then implement PCA, RGB histogram analysis, and a one-way analysis of variance (ANOVA)34 to assess the ability of each approach to quickly discriminate between the fiber bundle types. We also explore the use of nitromethane (CH3NO2) quenching to improve fiber bundle discrimination, and we determine the observed quenching mechanism.35

THEORY Consider an integer number (n) of luminescence dye types (which could be the same environmentally susceptible dye in multiple microenvironments or chemically different dye molecules) distributed within a microheterogeneous sample like a fiber bundle. Consider further that each dye type (i) behaves independently and exhibits unique (a) emission spectra, (b) excited-state luminescence lifetimes (si), and (c) susceptibilities to quencher molecules as reflected by the Stern–Volmer quenching constants (KSV,i). One can describe the luminescence quenching under these

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conditions by a Stern–Volmer model34 of the form

I0 =Iðkem Þ ¼

n X

i ¼ 1fi ðkem Þð1 þ KSV ;i ½QÞ

ð1Þ

i¼1

where I0 and I are the intensities at a particular emission wavelength (kem) in the absence and presence of quencher at concentration [Q], respectively, and fi(kem) denotes the fraction of species i that contributes at each emission wavelength. KSV,i is given by KSV ;i ¼ si;0 kq;i

ð2Þ

In this expression, si,0 represents the emission wavelength-independent, excited-state lifetime for species i in the absence of quencher, and kq,i is the bimolecular quenching constant of species i. kq,i depends on the quencher transport properties within the microheterogeneous system (fiber bundle) to species i and the physical accessibility of species i to the quencher molecules. Inspection of Eqs. 1 and 2 show that I0/I (kem) will be a function of the emission wavelength, the emitting species emission spectra, the quencher concentration, and the emitting species excited-state lifetimes. In Fig. 1, we illustrate the effect of emission spectra and Stern–Volmer quenching constant on a hypothetical

sample that is composed of a binary mixture of luminescent species with Gaussian-shaped emission spectra. Figure 1a illustrates the situation where the individual species (S1 and S2) have the same contributions in the absence of quencher and different Stern– Volmer quenching constants (Ksv,1 = 0.1 M1, Ksv,2 = 0.4 M1) in the presence of 3 M quencher. The individual component emission spectra in the absence (S1 and S2) and presence of quencher (S1q and S2q) are given by the solid and dashed spectra, respectively. The corresponding mixture emission spectra (Mix) are also shown (Mix and Mixq). Figure 1b illustrates the case where the S1and S2 Ksv values have been reversed in comparison to Fig. 1a. Figure 1c presents the situation where the Ksv values are the same as in Fig. 1a, but the contribution of the two species is 1:3 vs. 1:1. Finally, Fig. 1d presents the individual Io/I (kem) profiles associated with Figs. 1a, 1b, and 1c. The uniqueness of these emission wavelengthdependent Stern–Volmer profiles is apparent. In addition, these profiles are independent of the total intensity, an important, beneficial characteristic.

MATERIALS AND METHODS Fabric Samples. Thirteen bull denim sample swatches were purchased from a local retailer (Joanne’s Fabric). We maintained the retailer’s nomenclature for each sample/bundle type. These fiber samples were selected with the sole purpose of covering the visible spectrum for congruous analysis from 450 to 850 nm. For fiber bundle analysis, unwashed individual yarns (2.5 cm in length) were teased from the denim swatches and secured with clear labeling tape (3M) at the distal and proximal bundle ends on the face of a standard quartz microscope slide. No mounting medium was used because the goal was to rapidly record relevant data from unadulterated fiber bundles. Quenching experiments were carried out by soaking individual fiber bundles for 30 s in pure CH3NO2 (.95%, Sigma) and then removing the sample prior to analysis. CH3NO2 has no optical signature over the spectral region used in this research, and it does not interfere in the measurements; it only quenches the fiber bundle luminescence. To minimize contamination, all analysts wore clean nitrile gloves, and samples were handled with clean stainless steel tweezers. Characterization Instrumentation. All data were recorded by using an epi-luminescence microscope (BX-40, Olympus) with a 43 microscope objective (Olympus, UPlanApo) coupled to a liquid crystal tunable filter (LCTF) and CCD module (Nuance FX, CRI) (Fig. 2). For 325 nm excitation, a He-Cd laser (Omnichrome, Series 74) was used. Excitation was passed through a 323 nm 6 10 nm bandpass filter (Omega Optics, 323BP10). Emission was acquired through a 410 nm dichroic filter (Omega Optics, 410DRLP) and a 410 nm long pass filter (Omega Optics, 410ALP). For 405 nm excitation, a diode laser (Crystal Laser, DL405-100) was used. Excitation was passed through a 450 nm 6 50 nm bandpass filter (Omega Optics, 450BP50). Emission was acquired through a 455 nm dichroic filter (Omega Optics, 455DRLP) and a 455 nm long pass filter (Omega Optics, 455ALP). Regardless of excitation wavelength, the LCTF

was scanned from 450 to 850 nm with 10 nm step sizes. We used 2 3 2 binning with exposure times ranging from 0.25 to 5.0 s depending on the sample brightness. The microscope system is shown in Fig. 2a along with the multi- and hyperspectral imaging concepts (Fig. 2b). All experiments were recorded in triplicate at room temperature (295–298 K) under ambient conditions and were background corrected. For simplicity, the average results are shown. Typical imprecisions were ,4% RSD. Principal Component Analysis (PCA). The emission spectra extracted from the background corrected luminescence images were analyzed using PCA.30,31 PCA reduces the dimensionality of spectra that contain a large number of interrelated variables (observed spectral components), while still retaining the variation present in the set (individual spectral components). A common pretreatment method for PCA, centering, was used to analyze the luminescence images. This process involves subtracting each individual spectral component from the mean calculated for the fiber bundle set to normalize the data. The PCs calculated during the PCA process are computed using covariance matrix eigenvector decomposition.30,31 In this research, we use the principal component analysis function in XLSTAT (Addinsoft). Red, Green, Blue (RGB) Component Histograms. To ‘‘render’’ a luminescence image into a RGB histogram, the background corrected image cube was imported into Adobe Photoshop CS6 Extended software (Adobe Software Systems Inc.) in the photography workspace.32,33 The histogram data were then extracted from a rectangular region of interest (ROI) containing 1885 pixels for luminescence images or 22 500 pixels for photographs recorded under laboratory light illumination centered at the pixel exhibiting the highest average intensity within the image. The ROI position was selected to include signal only from the fiber bundle. The 1885/22 500 pixel values were selected to ensure adequate statistics. Similar RGB histograms (within 3% RSD) resulted when other similar areas within the fiber bundle were assessed as long as they were within the fiber bundle itself. The intensity measurements were recorded using the Image Analysis menu and exported as a CSV text file for plotting in SigmaPlot (Systat Software). The resulting histogram illustrates how pixels in the image are distributed by graphing the number of pixels at each color intensity level (i.e., tonal range). Figure 3 presents an example RGB analysis. The lefthand side shows a raw image recorded under laboratory light illumination from a Fern Green swatch recorded with a digital camera (Olympus Imaging America, Inc., SZ-12). The right-side panel presents the recovered RGB histogram (frequency vs. tonal value) for the area shown within the dashed white rectangle. The frequency represents the number of pixels with a given tonal value. The frequency sum for each component (R, G or B) equals the total number of pixels in the area assessed (22 500 in this case). The tonal range characterizes the brightness of a given component (RGB) with the dimmest tone being assigned 0 and the brightest tone being assigned 255. In this example, all RGB components contribute to the observed color. The R component is the dimmest component (lowest tonal values) and it contrib-

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FIG. 2. Simplified schematic of the hyperspectral luminescence imaging system used in this research (a) and the multi- and hyperspectral imaging concepts (b). In panel (b) four pixels are represented wherein each pixel emits a different color/spectrum. In a multispectral image (a) limited number of spectral bandpasses are used to assess the sample. In a hyperspectral image the number of spectral bands is increased so that one can recover actual spectra at each pixel. In the current research data are acquired from 450 to 850 nm at 10 nm steps. In operation, the LCTF is adjusted to a particular wavelength, a CCD image is captured, and the LCTF is incremented to the next wavelength and another image captured. The process is repeated until the full spectral range of interest is assessed. The result is an image cube.

FIG. 3. Illustration of the RGB rendering process. (left panel) Photograph of a Fern Green fabric swatch recorded under overhead laboratory light illumination. The RGB histograms are rendered from a region of interest (ROI) that is indicated by the white dashed rectangle. (right panel) The recovered RGB rendered histograms (frequency vs. tonal range) for the red, green, and blue components in the left side image are shown. The image is mostly composed of G components then blue components, and red is the least contributing component.

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utes the least, and the G component is the brightest (highest tonal value) and it contributes the most. The B component contribution is intermediate when compared to the R and G components. Statistical Comparisons. Statistical comparisons among the RGB component histograms were carried out by ANOVA using the Kruskal–Wallis one-way analysis of variance on ranks at the 95% confidence level.34 Ranks were used because the individual histograms were not generally normal distributed. The Tukey pairwise comparison with P  0.05 being significant was used to isolate the comparisons that differ significantly. Time-Correlated Single-Photon Counting (TCSPC) Luminescence. Time-resolved intensity decays were measured by using an IBH model 5000 W SAFE timecorrelated single photon counting lifetime instrument with a pair of calcite polarizers oriented at the magic angle (kex = 08, kem = 54.78).35 A 335 nm light-emitting diode (LED) (Nano LED, N-16) operating at 1 MHz served as the excitation source. Emission was monitored through a monochromator with a 16 nm spectral bandpass. The time resolution for this experiment was 0.04820 ns/channel (1024 channels). Data analysis was performed by using Globals WE (Globals Unlimited), a commercially available software package.

RESULTS AND DISCUSSION Thirteen different Bull denim fiber bundles were studied in this research to assess the performance of the LHSI-based discrimination strategy. Bull denim is comprised of a 3 3 1 twill weave, piece-dyed fabric containing no indigo dyes. Figure 4 presents typical images from the raw materials investigated in this study all illuminated by fluorescent overhead lighting under three conditions. Figure 4a shows the fabric samples before adding CH3NO2. Figure 4b shows the fabrics immediately after exposure to 0.5 mL of neat CH3NO2. The CH3NO2 is clearly seen as small wet spots on the fabric. Figure 4c shows the Fig. 4b samples after 9 min under ambient conditions. These results suggest that CH3NO2 wets the samples, the treatment is reversible, and it does not impact the fiber bundles. During a quenching measurement the extent of CH3NO2 evaporation is negligible. Figures 5 and 6 present typical LHSI image cubes from quenched and unquenched individual fiber bundle samples excited at 325 and 405 nm, respectively. The specimens excited at 405 nm emit poorly when excited at 325 nm. The top row in Figs. 5 and 6 are the unquenched bundle images, while the bottom row are the same fibers that have been exposed to CH3NO2. Inspection of these data show several interesting points. First, the luminescence from these samples covers a substantial portion of the visible spectrum. Second, the emission efficiency from the samples differs significantly (e.g., white is strongly emitting, 250 ms exposure time; Fern Green is, by comparison, weakly emitting, 5 s exposure time). When exposed to CH3NO2, the luminescence intensity is lower in comparison to before quenching, indicated by the increased CCD exposure times needed to acquire the images. Third, the emission from the fiber bundles is not uniform; the warp and weft regions where fiber bundles

FIG. 4. Photographs of all bull denim swatches analyzed in this research recorded under overhead laboratory light illumination. (a) Before adding CH3NO2. (b) During CH3NO2 exposure. Note wet spot where CH3NO2 was delivered. (c) Nine minutes after CH3NO2 exposure.

overlap are apparent. Finally, the fiber bundles exposed to CH3NO2 exhibit a slightly larger diameter in comparison to the unquenched images. This is because the fiber bundles have absorbed CH3NO2 and become swollen with CH3NO2.

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FIG. 5. Image cubes of individual bull denim fiber bundles excited at 325 nm. The dashed boxes denote the ROI from which luminescence emission spectra were extracted. The top row shows the fiber bundle image cubes before CH3NO2 exposure, while the bottom row shows the same fibers during CH3NO2 exposure.

Figures 7 and 8 present typical luminescence emission spectra before (solid black line) and during CH3NO2 quenching (dashed red line) and the corresponding Io/I (kem) profiles (solid blue line) for each of the fiber bundles studied when excited at 325 and 405 nm, respectively. To provide a representative sampling, all emission spectra were extracted from 400 3 600 lm ROIs denoted by the white dashed rectangles (see Figs. 5 and 6) that include the brightest region in the fiber bundle. Inspection of these results illustrates several important points. First, the emission spectra from several fiber bundles exhibit regions of both intense and weak luminescence, indicating multiple emitting species exist within the fiber bundle. Second, several of the fiber bundles (e.g., Latte and Paloma, Cornflower and Royal Blue, and Black and Medieval Blue) exhibit similar emission spectra. Third, upon CH3NO2 exposure, the luminescence intensity of all fiber bundles decreases, indicating that all fiber bundles contain luminophores/dyes susceptible to and accessible by CH3NO2 quencher molecules. Fourth, Io/I (kem) for the green-emitting fiber bundles (kex = 325 nm) (Paloma, Latte, and Papyrus) are similar, indicating these fiber bundles may be composed of similar dyes/luminophores. Finally, the emitting species in the blue/green spectral region of the majority of the fiber bundles excited at 325 nm are quenched much more in comparison to species in other spectral regions. For fiber bundles excited at 405 nm, the greatest quenching occurs around 575–600 nm in comparison to the rest of the emission regions.

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FIG. 6. Image cubes of individual bull denim fiber bundles excited at 405 nm. The dashed boxes denote the ROI from which luminescence emission spectra were extracted. The top row shows the fiber bundle image cubes before CH3NO2 exposure, while the bottom row shows the same fibers during CH3NO2 exposure.

FIG. 7. Typical fiber bundle luminescence emission spectra from the ROI in Fig. 5 before (solid black line) and during CH3NO2 quenching (dashed red line) for fiber bundles excited at 325 nm. Io/I (kem) is presented as the solid blue line denoted on the right axis.

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FIG. 8. Typical fiber bundle luminescence emission spectra from the ROI in Fig. 6 before (solid black line) and during CH3NO2 quenching (dashed red line) for fiber bundles excited at 405 nm. Io/I (kem) is presented as the solid blue line denoted on the right axis.

Figures 9 and 10 present rendered RGB histogram pairs for unquenched (top panels) and CH3NO2 quenched (bottom panels) fiber bundles when excited at 325 and 405 nm, respectively. Several aspects of these data merit further discussion. First, each fiber bundle emission is a combination of red, green, and blue components with an RGB profile that spans the entire tonal range. This result is consistent with the use of multiple dyes to create each textile. Second, the red, green, and blue components have higher average tonal ranges prior to CH3NO2 quenching in comparison to during quenching, indicating that all three components are sensitive to CH3NO2 exposure, but not to the same extent. Third, the majority of the G component in green-emitting fiber bundles (e.g.,

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Paloma, Latte, etc.) is quenched by CH3NO2 in comparison to the red-emitting fiber bundles (e.g., Biking Red, Royal Blue, etc.), where the R component sees the greatest CH3NO2 quenching. To quantitatively discriminate among the fiber bundle RGB histograms, ANOVA34 was used to determine statistical significance among the individual red, green, or blue component distributions. However, because the individual component histograms do not follow a normal distribution, Kruskal–Wallis34 analysis on ranks was used. This is a sensible strategy because the fiber bundles have similar components (R, G, and B) as discussed above. The Kruskal–Wallis test confirmed the difference in the median values within a majority of the histogram components were statistically different (P , 0.001), indicating that a statistical diversity between two or more of the fiber bundle components exists. The next logical step is to determine the specific red, green, or blue components that exhibit the statistical differences. The post hoc Tukey test34 defines all possible pairwise comparisons within the red, green, and blue components and identifies the exact pairs where the difference between the two means is greater in comparison to the expected error. Tukey’s test results on Figs. 9 and 10 show that most fiber bundles can be discriminated (i.e., P  0.05) using either the red, green, or blue components. However, two fiber bundle combinations were not statistically different for any component, Cornflower vs. Royal Blue and Cornflower vs. Very Berry, indicating we have achieved 75% discrimination (9 out of 13 fiber bundles) using the LHSI technique in concert with RGB component histograms and ANOVA. However, when incorporating CH3NO2 with Tukey’s test on the quenched RGB components, the Cornflower, Royal Blue, and Very Berry fiber bundles could then be discriminated (i.e., P  0.05), indicating unambiguous discrimination among the Bull Denim fiber bundle set studied. The spectral data extracted from Figs. 5 and 6 were also analyzed using PCA.30,31 PCA reduces the spectral data dimensionality by transforming the data into uncorrelated PCs.31 The PCs are then rank ordered so that the first few represent those components that contribute the most to the spectrum.30 Figures 11 and 12 summarize the PCs for the fiber bundle types tested in this research. The spectral components associated with additional PCs were unrealistically narrow (,20 nm) or biphasic (positive and negative intensities), which are inconsistent with emission from organic dyes. To estimate the uncertainty in each PC, the entire ROI (Figs. 5 and 6) was divided into 10 equal sub-regions and the spectra from each sub-region were extracted. The deviation among the PCs was then calculated and plotted to create 3D ellipses (uncertainty in PC1, PC2, and PC3) to illustrate the associated variance. The larger the ellipse, the greater the variation present in the PCs. The PC score plots allow one to assess the relationship between the fiber bundle types. Samples that cluster together have strong correlations, indicating materials that are comprised of similar luminescent species or dye molecules. In Fig. 11 before quenching, the individual fiber bundles excited at 325 nm occupy

FIG. 9. RGB histogram pairs for the fiber bundles before (top) and during CH3NO2 quenching (bottom) when excited at 325 nm. The R, G, B components before quenching are color coded. The R, G, B components during quenching are designated by Rq, Gq, Bq and are color coded slightly differently to distinguish between the quenched and unquenched sets. The scale bar represents 50 frequency units and the y-axis are equivalent.

statistically distinct positions in PCA space, indicating that they can all be discriminated using PCA. Second, the Biking Red, Fern Green, and Latte fiber bundles show the greatest variance in the ROI, while the remaining fiber bundles have comparatively smaller variances in the same region. When exposed to CH3NO2, less overall fiber bundle discrimination is realized when using PCA.

However, fiber bundles that appeared close together (Very Berry/Royal Blue and Papyrus/Cornflower) before quenching are better resolved from each other during CH3NO2 quenching. In Fig. 12 before quenching, the individual fiber bundles excited at 405 nm PCs are grouped close together and have similar variances. However, during quenching, each fiber bundle is affected

FIG. 10. RGB histogram pairs for the fiber bundles before (top) and during CH3NO2 quenching (bottom) when excited at 405 nm. The R, G, B components before quenching are color coded. The R, G, B components during quenching are designated by Rq, Gq, Bq and are color coded slightly differently to distinguish between the quenched and unquenched sets. The scale bar represents 50 frequency unit and the y-axis are equivalent.

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FIG. 11. 3D representation of the recovered PCs for fiber bundles excited at 325 nm before and during CH3NO2 quenching. The legend is color coded to the corresponding data points. The size of each ellipse represents the deviation among 10 principle component analyses in the ROI (denoted in Fig. 5).

differently by the CH3NO2, leading to an improvement in discrimination power under quenching. Time-correlated single photon counting experiments (results not shown) revealed several important points. First, the average excited state lifetime (hs0 i) is ,1 ns, showing that the observed luminescence arises from fluorescence. Second, although the intensity decay traces are multiexponential, hs0 i differs by less than 20% across the emission spectrum whereas the Io/I (kem) changes by at least 300% over the same spectral region. Thus, the primary reason for the observed change in Io/I

is kq, meaning that the local microenvironment surrounding the different luminescent dye molecules is very different (see below). Figure 13 presents a simplified model to describe the types of local microenvironments surrounding the fluorescent dye molecules and encountered by the CH3NO2 quencher molecule in these fiber bundle samples. The model shows multiple dye types (colored stars) located in different types of microenvironments (polar, non-polar) that are readily accessible, accessible by a tortuous route, or inaccessible. The combinations of

FIG. 12. 3D representation of the recovered PCs for fiber bundles excited at 405 nm before and during CH3NO2 quenching. The legend is color coded to the corresponding data points. The size of each ellipse represents the deviation among 10 principle component analyses in the ROI (denoted in Fig. 6).

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FIG. 13. Simplified schematic showing the behavior of luminescent dye molecules sequestered within a fiber bundle sample. (a) 3:1 twill weave and individual fiber bundles. (b) Cross section of an individual fiber bundle showing different dye molecules (colored stars) and different microenvironments (e.g., accessible, inaccessible, tortuous, polar, non-polar) surrounding each type. The combination of dye–dye type and their individual microenvironment(s) control the efficiency of CH3NO2 quenching.

these different dyes and microenvironments leads to the observed emission wavelength-dependent I0/I (kem) profiles.

CONCLUSIONS We have evaluated the performance of LHSI in conjunction with CH3NO2 quenching, PCA, RGB rendering, and ANOVA as tools to discriminate bull denim fiber bundle types. RGB rendering allows 75% discrimination without quenching. When CH3NO2 quenching is implemented, 100% discrimination is realized. PCA provides 100% discrimination among the fiber bundle types studied without any quenching. PCA coupled withCH3NO2 quenching provides poorer overall discrimination; however, the approach does improve discrimination between certain fiber bundle types that were the most similar in the unquenched experiments. Finally, the fluorescent dye molecules within the fiber bundles exhibit different accessibilities to CH3NO2. ACKNOWLEDGMENTS We acknowledge the financial support for this research by the National Science Foundation (Grants CHE-0848171 and DUE-1102998). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The authors also acknowledge Dr. KaYi Yung for help in collecting time-correlated single-photon counting data and Prof. Dongmao Zhang (Mississippi State University) for helpful discussions on PCA. 1. M.W. Tungol, A. Montaser, E.G. Bartick. ‘‘FT-IR Microscopy for Forensic Yarn Analysis: The Results of Case Studies’’. Proc. SPIE. 1989. 1145: 308-309. doi:10.1117/12.969475. 2. V. Causin, C. Marega, S. Schiavone, A. Marigo. ‘‘A Quantitative Differentiation Method for Acrylic Yarns by Infrared Spectroscopy’’. Forensic Sci. Int. 2005. 151(2-3): 125-131. doi:10.1016/j.forsciint.2005. 02.004.

3. B.M. Gatewood, D.L. Wetzel, J.A. Reffner, L. Cho. ‘‘A New Method for Fiber Comparison Using Polarized Infrared Microspectroscopy’’. J. Forensic Sci. 1999. 44(2): 275-282. doi:10.1520/JFS14452J. 4. M.M. Houck. ‘‘The Forensic Identification of Textile Yarns’’. In: M.M. Houck, editor. Identification of Textile Yarns. Boca Raton, FL: Woodhead Publishing, 2009. Pp. 259-274. 5. J. Thomas, P. Buzzini, G. Massonnet, B. Reedy, C. Roux. ‘‘Raman Spectroscopy and the Forensic Analysis of Black/Grey and Blue Cotton Yarns. Part I. Investigation of the Effects of Varying Laser Wavelength’’. Forensic Sci. Int. 2005. 152(2-3): 189-197. doi:10.1016/ j.forsciint.2004.08.009. 6. L.C. Abbott, S.N. Batchelor, J.R.L. Smith, J.N. Moore. ‘‘Resonance Raman and UV-Spectroscopy of Black Dyes on Textiles’’. Forensic Sci. Int. 2010. 202(1-3): 54-63. doi:10.1016/j.forsciint.2010.04.026. 7. J.V. Goodpaster, E.A. Liszewski. ‘‘Forensic Analysis of Dyed Textile Fibers’’. Anal. Bioanal. Chem. 2009. 394(8): 2009-2018. doi:10.1007/ s00216-009-2885-7. 8. D.W. Deedrick. ‘‘Hairs, Fibers, Crime and Evidence Part 2: Fiber Evidence’’. Forensic Sci. Comm. 2000. 2(3): http://www.fbi.gov/ about-us/lab/forensic-science-communications/fsc/july2000/ deedric3.htm [accessed Sept 30 2014]. 9. K. Wiggins, R. Palmer, W. Hutchinson, P. Drummond. ‘‘An Investigation into the Use of Calculating the First Derivative of Absorbance Spectra as a Tool for Forensic Fibre Analysis’’. Sci. Justice. 2007. 47(1): 9-18. doi:10.1016/j.scijus.2006.11.001. 10. S.L. Morgan, A.A. Nieuwland, C.R. Mubarak, J.E. Hendrix, E.M. Enlow, B.J. Vasser. ‘‘Forensic Discrimination of Dyed Textile Yarns Using UV-Visible and Fluorescence Microspectrophotometry’’. Paper presented at: Proceedings of European Fibres Group (Annual Meeting). Prague, Czechoslovakia; May 25, 2004. 11. A.D. Campiglia, M.D. Sigman. ‘‘Application of Forensic Line Narrowing Spectroscopy to Forensic Fiber Examination’’. Doc. 240640. U.S. Department of Justice. Award number 2006-DN-BXK036. December 2012. 12. R. Macrae, R.J. Dudley, K.W. Smalldon. ‘‘The Characterization of Dyestuffs on Wool Fibers with Special Reference to Microspectrophotometry’’. J. Forensic Sci. 1979. 24(1): 117-127. 13. R. Resua. ‘‘A Semi-Micro Technique for the Extraction and Comparison of Dyes in Textile Fibers’’. J. Forensic Sci. 1980. 25(1): 168-173. 14. B.B. Wheals, P.C. White, M.D. Paterson. ‘‘High-Performance Liquid Chromatographic Method Utilising Single or Multi-Wavelength

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Rapid, nondestructive denim fiber bundle characterization using luminescence hyperspectral image analysis.

An investigation into the performance of luminescence-based hyperspectral imaging (LHSI) for denim fiber bundle discrimination has been conducted. We ...
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