**2.6 Some important indices**

The microscopic hair examination also made use of certain indices that have a significant value in species characterization. The indices find valuable use in statistical analysis. Three indices are commonly used and they are defined as follows:

#### **2.7 Methods to study hair characteristics**

### *2.7.1 Preliminary examination*

Hair samples need to be initially examined for their color, texture, thickness, etc. before microscopic examination. The thickness can be measured in microns using a oculometer on a light microscope.

### *2.7.2 Examination of cuticle*

## *<IP_ADDRESS> Light microscopy–based method*

The almost transparent and very thin layer of the cuticle cannot be appreciated under a transmitted light microscope by using a whole mount of hair. Only the cortex and medulla are visible in the whole mount. Therefore, to view the cuticular structure of hair, a "cast" of hair has to be made and viewed under a microscope. The suitable method is to prepare a cast of the hair. In the case of the hair "scale cast" method, a hair is placed on the surface of a suitable material such that the surface structure of the hair gets reproduced as a three-dimensional cast. This cast can be viewed under a light microscope to observe the cuticular structure of the hair. About 10–20% solution of gelatin in distilled water or 50% solution of polyvinyl acetate in distilled water is used for the preparation of scale casts [28]. A fine and uniform film of the casting media is made on a clean microscopic glass slide with the help of a glass rod or a flat brush in a single stroke along the length of the slide surface. The slide is then placed on a horizontal surface and hair samples are placed one by one on the slide with the help of tweezers. The casting media is allowed to dry for about 20–30 minutes and the hair are removed by plucking gently, leaving behind the three-dimensional cast of the hair surface, which can be used to study the scale patterns, margins and shapes. The cast is viewed under the microscope at a magnification of 100× to 400× depending upon the thickness of the hair. **Figure 1** depicts the scale pattern of the Indian Bison (*Bos gaurus*) under microscope 400× magnification.

#### *<IP_ADDRESS> SEM-based method*

Scanning electron microscopy (SEM) is also a recommended method to study the cuticle of hair as it offers resolution much higher than light microscopy. In this method, the hair samples are initially coated with a thin film of gold or palladium under a very low pressure (10–6 Torr) to make the surface conducting. These hair samples coated with a very fine film of gold or palladium are then viewed under

**37**

**Figure 2.**

*represent the area where xylene has not infiltrated).*

*Forensic Analysis in Wildlife Crime Cases: Microscopy, DNA Profiling and Isotope Analysis*

an electron microscope, with the help of an electron beam. The method also has added advantage of studying the elemental profile of the hair if the SEM is coupled with energy dispersive X-ray analysis (SEM-EDXA) or wavelength dispersive X-ray

*The cuticle of Indian bison (Bos gaurus) under microscope 400× magnification.*

Medulla can be visualized in the whole mount of hair. However, it is not usually possible to observe the fine structural details of the medulla because of the air filled in vacuoles of the medulla. Hence, it appears as a dark central region when viewed under a microscope. For a proper appreciation of the fine structure of the medulla, the air vacuoles need to be infiltrated with a solvent like xylene. To achieve this, the hair samples are cut into small pieces (0.5 cm to 1.0 cm in length) with a razor blade and immersed in xylene (preferably overnight). These hairpieces after overnight treatment with xylene can be mounted directly on a glass slide in DPX or Canada balsam and viewed under a light microscope. **Figure 2** depicts the Medulla of Serow (*Capricornis sumatraensis*) under microscope 400× magnification. From **Figure 2**, the extent of infiltration by xylene can be clearly appreciated, as medulla

*Medulla of Serow (Capricornis sumatraensis) under microscope 400× magnification (note black globules*

*DOI: http://dx.doi.org/10.5772/intechopen.98252*

analysis (SEM-WDXA).

**Figure 1.**

*2.7.3 Examination of medulla*

*Forensic Analysis in Wildlife Crime Cases: Microscopy, DNA Profiling and Isotope Analysis DOI: http://dx.doi.org/10.5772/intechopen.98252*

#### **Figure 1.**

*Forensic Analysis - Scientific and Medical Techniques and Evidence under the Microscope*

The microscopic hair examination also made use of certain indices that have a significant value in species characterization. The indices find valuable use in statistical analysis. Three indices are commonly used and they are defined as follows:

a.Scale Index: Ratio of the free proximo-distal length of the scale to the diameter

b.Scale Count Index: Number of scales per unit (1 mm) length of the hair shaft

Hair samples need to be initially examined for their color, texture, thickness, etc. before microscopic examination. The thickness can be measured in microns using a

The almost transparent and very thin layer of the cuticle cannot be appreciated under a transmitted light microscope by using a whole mount of hair. Only the cortex and medulla are visible in the whole mount. Therefore, to view the cuticular structure of hair, a "cast" of hair has to be made and viewed under a microscope. The suitable method is to prepare a cast of the hair. In the case of the hair "scale cast" method, a hair is placed on the surface of a suitable material such that the surface structure of the hair gets reproduced as a three-dimensional cast. This cast can be viewed under a light microscope to observe the cuticular structure of the hair. About 10–20% solution of gelatin in distilled water or 50% solution of polyvinyl acetate in distilled water is used for the preparation of scale casts [28]. A fine and uniform film of the casting media is made on a clean microscopic glass slide with the help of a glass rod or a flat brush in a single stroke along the length of the slide surface. The slide is then placed on a horizontal surface and hair samples are placed one by one on the slide with the help of tweezers. The casting media is allowed to dry for about 20–30 minutes and the hair are removed by plucking gently, leaving behind the three-dimensional cast of the hair surface, which can be used to study the scale patterns, margins and shapes. The cast is viewed under the microscope at a magnification of 100× to 400× depending upon the thickness of the hair. **Figure 1** depicts the scale pattern of the Indian Bison (*Bos gaurus*) under microscope 400× magnification.

Scanning electron microscopy (SEM) is also a recommended method to study the cuticle of hair as it offers resolution much higher than light microscopy. In this method, the hair samples are initially coated with a thin film of gold or palladium under a very low pressure (10–6 Torr) to make the surface conducting. These hair samples coated with a very fine film of gold or palladium are then viewed under

c.Medullary Index: Ratio of the medullary thickness to the hair thickness

**2.6 Some important indices**

of the hair shaft

(diameter)

*2.7.1 Preliminary examination*

*2.7.2 Examination of cuticle*

*<IP_ADDRESS> SEM-based method*

oculometer on a light microscope.

*<IP_ADDRESS> Light microscopy–based method*

**2.7 Methods to study hair characteristics**

**36**

*The cuticle of Indian bison (Bos gaurus) under microscope 400× magnification.*

an electron microscope, with the help of an electron beam. The method also has added advantage of studying the elemental profile of the hair if the SEM is coupled with energy dispersive X-ray analysis (SEM-EDXA) or wavelength dispersive X-ray analysis (SEM-WDXA).

#### *2.7.3 Examination of medulla*

Medulla can be visualized in the whole mount of hair. However, it is not usually possible to observe the fine structural details of the medulla because of the air filled in vacuoles of the medulla. Hence, it appears as a dark central region when viewed under a microscope. For a proper appreciation of the fine structure of the medulla, the air vacuoles need to be infiltrated with a solvent like xylene. To achieve this, the hair samples are cut into small pieces (0.5 cm to 1.0 cm in length) with a razor blade and immersed in xylene (preferably overnight). These hairpieces after overnight treatment with xylene can be mounted directly on a glass slide in DPX or Canada balsam and viewed under a light microscope. **Figure 2** depicts the Medulla of Serow (*Capricornis sumatraensis*) under microscope 400× magnification. From **Figure 2**, the extent of infiltration by xylene can be clearly appreciated, as medulla

#### **Figure 2.**

*Medulla of Serow (Capricornis sumatraensis) under microscope 400× magnification (note black globules represent the area where xylene has not infiltrated).*

in that part is clearly visible, whereas in the part where xylene has not infiltrated, the medulla shows dark globules. The dark globules can be cleared by increasing the time of keeping the hair cuttings under xylene.

The following four observations can be made:


*3.3. The TRMM, CMORPH, and CHIRPS Precipitation Datasets*

TRMM, CMORPH, and CHIRPS are three quasi-global gridded precipitation datasets. The TRMM TMPA products provide precipitation for the spatial coverage of 50◦ N–S from 1998 to the present with a spatial resolution of 0.25◦ × 0.25◦ [18]. In this study, the TRMM 3B42 product was used. The original temporal resolution of this dataset is 3 h, and the daily aggregated TRMM 3B42 product can be obtained from the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) (https://disc.gsfc.nasa.gov, accessed on 20 May 2020). The CMORPH products provide precipitation for the spatial coverage of 60◦ N–S from 1998 to the present [40]. The lasted version 1.0 includes three different products, including raw (satellite-only precipitation estimates), bias-corrected (CRT), and gauge-satellite blended (BLD). In this study, the CMORPH BLD product with a spatial resolution of 0.25◦ × 0.25◦ was used, which can be downloaded from https://ftp. cpc.ncep.noaa.gov/precip/CMORPH\_V1.0/BLD (accessed on 20 May 2020). The CHIRPS product provides precipitation for the spatial coverage of 50◦ N–S with a high spatial resolution (i.e., 0.05◦ × 0.05◦ ) from 1981 to the present [19]. The CHIRPS version 2.0 data were downloaded from https://data.chc.ucsb.edu/products/CHIRPS-2.0/global\_daily (accessed on 20 May 2020) and used in this study.

#### *3.4. SWAT Modelling Procedures*

Developed by the Agricultural Research Service of the United States Department of Agriculture, Agricultural Research Service [41], the Soil and Water Assessment Tool (SWAT) is a semi-distributed, process-based, and time-continuous watershed model. SWAT is capable of modeling hydrological processes, soil erosion, and water quality at basin scales [42]. In SWAT, the river basin is first divided into subbasins, then further to the hydrologic response units (HRUs) that represent a unique combination of soil type, land use, and slope. More details on the description of SWAT can be found in many sources [25,42,43], and thus this description was not repeated here for conciseness. As an easy-to-use toolbar in the QGIS interface, QSWAT (Version 2017.02\_1.4, Texas A&M University, College Station, TX, USA) was used to set up the SWAT model in this study.

To set up the SWAT model (Figure 2), the following data sources were used: (1) the elevation data was obtained from the Digital Elevation Model (DEM) product of ALOS

World 3D–30 m (AW3D30) which was released by the Japan Aerospace Exploration Agency (JAXA) with a horizontal spatial resolution of approximately 30 m. These data were downloaded from http://www.eorc.jaxa.jp/ALOS/en/aw3d30/ (accessed on 6 September 2019). (2) The land use map in 2010, with a scale of 1:100,000, was obtained from the National Earth System Science Data Sharing Platform of China (http://www.geodata.cn, accessed on 6 September 2019). (3) The soil map, with a scale of 1:500,000, was obtained from China Soil Database (CSDB, http://vdb3.soil.csdb.cn/, accessed on 6 September 2019). This study used eighteen scenarios that are generated through the combination of three meteorological (excluding precipitation) datasets (Gauge, CFSR, and CMADS) and six precipitation datasets (Gauge, CFSR, CMADS, TRMM, CMORPH, and CHIRPS), to study the impact of different input data on the streamflow simulation. were downloaded from http://www.eorc.jaxa.jp/ALOS/en/aw3d30/ (accessed on 6 September 2019). (2) The land use map in 2010, with a scale of 1:100,000, was obtained from the National Earth System Science Data Sharing Platform of China (http://www.geodata.cn, accessed on 6 September 2019). (3) The soil map, with a scale of 1:500,000, was obtained from China Soil Database (CSDB, http://vdb3.soil.csdb.cn/, accessed on 6 September 2019). This study used eighteen scenarios that are generated through the combination of three meteorological (excluding precipitation) datasets (Gauge, CFSR, and CMADS) and six precipitation datasets (Gauge, CFSR, CMADS, TRMM, CMORPH, and CHIRPS), to study the impact of different input data on the streamflow simulation.

World 3D–30 m (AW3D30) which was released by the Japan Aerospace Exploration Agency (JAXA) with a horizontal spatial resolution of approximately 30 m. These data

*Water* **2022**, *14*, x FOR PEER REVIEW 5 of 23

**Figure 2.** Flowchart of streamflow simulation driven by open-access gridded meteorological and remote sensing precipitation products. **Figure 2.** Flowchart of streamflow simulation driven by open-access gridded meteorological and remote sensing precipitation products.

The SWAT model was run at the daily time scales for the period 1 January 2008–31 July 2014. The first year (2008) was used as a warm-up period to alleviate the impact of initial hydrological simulation conditions. From 1 January 2009 to 31 December 2011, as a calibration period, sensitivity parameters were identified and further calibrated to make the simulated streamflow fit the observation as close as possible. The period 1 January 2012–31 July 2014 was used as the independent validation period to test the validity of calibrated parameters. In this study, the sequential uncertainty fitting algorithm version 2 The SWAT model was run at the daily time scales for the period 1 January 2008–31 July 2014. The first year (2008) was used as a warm-up period to alleviate the impact of initial hydrological simulation conditions. From 1 January 2009 to 31 December 2011, as a calibration period, sensitivity parameters were identified and further calibrated to make the simulated streamflow fit the observation as close as possible. The period 1 January 2012–31 July 2014 was used as the independent validation period to test the validity of calibrated parameters. In this study, the sequential uncertainty fitting algorithm version 2 (SUFI-2) in the SWAT-CUP tool [44,45] was adopted to perform sensitivity analysis and automatic calibration. Finally, 12 highly sensitive parameters for model calibration were

(SUFI-2) in the SWAT-CUP tool [44,45] was adopted to perform sensitivity analysis and automatic calibration. Finally, 12 highly sensitive parameters for model calibration were

have considerable impacts on runoff generation, and more runoff will be generated under wetter conditions [46]. Even for forest ecosystems, finite amounts of precipitation can be retained during extreme rainfall events [47–49]. The SCS curve number (CN) in Table 1 is for average soil moisture conditions (CN2), the SWAT model updates CN values on each day according to the current soil moisture levels, and whether the ground is frozen. The

selected, as presented in Table 1. It should be noted that antecedent moisture conditions have considerable impacts on runoff generation, and more runoff will be generated under wetter conditions [46]. Even for forest ecosystems, finite amounts of precipitation can be retained during extreme rainfall events [47–49]. The SCS curve number (CN) in Table 1 is for average soil moisture conditions (CN2), the SWAT model updates CN values on each day according to the current soil moisture levels, and whether the ground is frozen. The CN value is the highest (referred to as CN3) when the soil is at field capacity, and the lowest (referred to as CN1) when the soil is at wilting point. Both CN1 and CN3 are functions of CN2, and the details can be found in the theoretical documentation of SWAT [42].

**Table 1.** List of parameters used for calibration and their default values and ranges for calibration ("a\_\_", "v\_\_", and "r\_\_" mean an absolute increase, a replacement, and a relative change from the initial parameter values, respectively).

Two iterations were carried out in the process of calibration, with 1000 simulations in each iteration (a total of 2000 simulations were carried out in the calibration period) by using the objective function of the Nash–Sutcliffe efficiency coefficient (NSE) [50]. After each iteration, the parameter ranges were updated based on the new parameter ranges recommended by SWAT-CUP and the physical boundaries of the parameters. The best model result among the 2000 simulations was selected as the final model output to evaluate and compare different models' performance. Three indicators, including NSE, the coefficient of determination (R2 ), and the percent bias (PBIAS), were used to evaluate and compare the model outputs. This study referred to the widely used guideline [51] to classify the model performance in terms of NSE; models would be classified as unsatisfactory (NSE ≤ 0.50), satisfactory (0.50 < NSE = 0.65), good (0.65 < NSE = 0.75), and very good (NSE > 0.75). PBIAS measures the average tendency of the simulated data to be larger or smaller than their observed counterparts. PBIAS is ideally zero, with positive values indicating model overestimation and negative values indicate model underestimation. The low-magnitude PBIAS values indicate accurate model simulations [52].

#### **4. Results**

#### *4.1. Comparison of Different Meteorological Inputs*

Figure 3 illustrates the cumulative fraction of daily precipitation at the watershed scale from six sources (i.e., Gauge, CFSR, CMADS, TRMM, CMORPH, and CHIRPS) during the period 2009–2014. The probability of dry day for Gauge was 54%, and the values were much lower (i.e., 32%) for CMADS and much higher for CHIRPS (i.e., 72%). The other three datasets had similar values to that of Gauge (i.e., 45% for CFSR, 51% for TRMM, and 48% for CMORPH). For precipitation intensity within 1–5 mm/day, CFSR had obviously lower frequencies than Gauge, while CHIRPS had obviously higher frequencies. CMADS had higher frequencies of precipitation within 5–20 mm/day. The average annual precipitation

for Gauge, CFSR, CMADS, TRMM, CMORPH, and CHIRPS were 1178, 1230, 966, 1260, 1208, and 1238 mm/year during the entire period 2009–2014.

Figure 4 shows the monthly precipitation of six datasets averaged over the watershed from 2009 to 2014. All six datasets displayed similar rainy seasons centered from June to September. CMADS had generally less monthly precipitation than Gauge, and there were considerably higher peaks in several months in the CFSR precipitation, for instance, August in 2009 and 2012. The three satellite-based precipitation datasets (i.e., TRMM, CMORPH, and CHIRPS) were generally close to gauge measurements, except for some lower peaks, such as in July and September of 2010. *Water* **2022**, *14*, x FOR PEER REVIEW 7 of 23

**Figure 3.** The cumulative fraction of daily precipitation from six datasets (Gauge, CFSR, CMADS, TRMM, CMORPH, and CHIRPS) at the watershed scale during 2009–2014. The left subfigure shows the cumulative fraction for daily precipitation ranging from 0 to 100 mm/day; in order to show more details, the two subfigures in the right show the cumulative fraction for daily precipitation ranging from 0 to 5 mm/day and 5 to 40 mm/day, respectively. **Figure 3.** The cumulative fraction of daily precipitation from six datasets (Gauge, CFSR, CMADS, TRMM, CMORPH, and CHIRPS) at the watershed scale during 2009–2014. The left subfigure shows the cumulative fraction for daily precipitation ranging from 0 to 100 mm/day; in order to show more details, the two subfigures in the right show the cumulative fraction for daily precipitation ranging from 0 to 5 mm/day and 5 to 40 mm/day, respectively. *Water* **2022**, *14*, x FOR PEER REVIEW 8 of 23

**Figure 4.** Comparison of monthly precipitation totals from six datasets at the watershed scale during **Figure 4.** Comparison of monthly precipitation totals from six datasets at the watershed scale during 2009–2014, including (**a**) Gauge CFSR, and CMADS, (**b**) Gauge, TRMM, CMORPH, and CHIRPS.

Figure 5 shows the cumulative fraction of daily maximum and minimum air temper-

2009–2014, including (**a**) Gauge CFSR, and CMADS, (**b**) Gauge, TRMM, CMORPH, and CHIRPS.

(PET) over the basin from three datasets between 2009 and 2014. Figure 6 displays the monthly mean of daily maximum and minimum air temperature, solar radiation, wind speed, relative humidity, and the sum of PET over the entire period. Taking an overall look at Figures 5 and 6, despite some fluctuations, three datasets had a fairly good agreement in the maximum temperature, minimum temperature, and relative humidity. However, large discrepancies among the three datasets can be clearly seen for the solar radiation and wind speed. For solar radiation, the two gridded products had larger fluctuations than Gauge. For wind speed, CMADS was considerably lower than the Gauge and CFSR datasets which display very good agreement with each other. To compute the PET in SWAT, the Penman–Monteith method was used, which requires air temperature, solar radiation, relative humidity, and wind speed as inputs. Despite these large discrepancies in daily solar radiation and wind speed, overall monthly PET totals from the three datasets were in good agreement, as shown in Figures 5 and 6. This suggests the discrepancies in individual weather variables (wind speed and solar radiation in particular in this case) cancelled each other to a certain degree when integrated into a further output, the PET in this case. Since PET would affect the computation of water balance in SWAT, considering good agreement in monthly PET totals from all the three datasets, it was expected that solar radiation and wind speed inputs from three datasets had little influence on the

SWAT modelling results in this studied basin.

Figure 5 shows the cumulative fraction of daily maximum and minimum air temperature, solar radiation, wind speed, relative humidity, and potential evapotranspiration (PET) over the basin from three datasets between 2009 and 2014. Figure 6 displays the monthly mean of daily maximum and minimum air temperature, solar radiation, wind speed, relative humidity, and the sum of PET over the entire period. Taking an overall look at Figures 5 and 6, despite some fluctuations, three datasets had a fairly good agreement in the maximum temperature, minimum temperature, and relative humidity. However, large discrepancies among the three datasets can be clearly seen for the solar radiation and wind speed. For solar radiation, the two gridded products had larger fluctuations than Gauge. For wind speed, CMADS was considerably lower than the Gauge and CFSR datasets which display very good agreement with each other. To compute the PET in SWAT, the Penman–Monteith method was used, which requires air temperature, solar radiation, relative humidity, and wind speed as inputs. Despite these large discrepancies in daily solar radiation and wind speed, overall monthly PET totals from the three datasets were in good agreement, as shown in Figures 5 and 6. This suggests the discrepancies in individual weather variables (wind speed and solar radiation in particular in this case) cancelled each other to a certain degree when integrated into a further output, the PET in this case. Since PET would affect the computation of water balance in SWAT, considering good agreement in monthly PET totals from all the three datasets, it was expected that solar radiation and wind speed inputs from three datasets had little influence on the SWAT modelling results in this studied basin. *Water* **2022**, *14*, x FOR PEER REVIEW 9 of 23

**Figure 5.** The cumulative fraction of daily maximum and minimum air temperature, solar radiation, wind speed, relative humidity, and potential ET from three different datasets (Gauge, CFSR, and CMADS) at the watershed scale during 2009–2014. **Figure 5.** The cumulative fraction of daily maximum and minimum air temperature, solar radiation, wind speed, relative humidity, and potential ET from three different datasets (Gauge, CFSR, and CMADS) at the watershed scale during 2009–2014.

**Figure 6.** Comparison of the monthly mean of daily maximum (TMX) and minimum (TMN) air temperature, solar radiation (SOL), wind speed (WIND), and relative humidity (HMD) from the Gauge, CFSR, and CMADS datasets, and their resulting potential evapotranspiration (PET) estimates at the watershed scale during 2009–2014. **Figure 6.** Comparison of the monthly mean of daily maximum (TMX) and minimum (TMN) air temperature, solar radiation (SOL), wind speed (WIND), and relative humidity (HMD) from the Gauge, CFSR, and CMADS datasets, and their resulting potential evapotranspiration (PET) estimates at the watershed scale during 2009–2014.


**2. Materials and methods**

### **2.1. Materials**

This study was conducted about the performance of denim fabrics containing dual core-spun yarns with filament fineness and elastane draft ratio variables. In that respect, the properties of drawn textured polyester filaments with conventional, fine and micro fineness are given in **Table 1**. All polyester (PET) filaments were selected among the most commonly used commercial types which are named as stretch textured yarns. Since microfilaments are sensitive to heat and it is necessary to omit the second heating zone during texturing, the PET filaments are in set form.

Basically, the study was focused on three different yarn types such as 100% cotton (Co) yarn, cotton covered filament core-spun yarn by using 110 dtex PET filament with different filament fineness as a control variables, and also cotton covered dual core-spun yarns by feeding 110 dtex PET filament with different filament fineness and 78 dtex elastane filament as core within the yarn structure. In the yarn production, cotton fiber with the physical properties of 28.53 mm staple length, 4.56 micronaire fineness, 31.53 gf/tex tenacity and 28.53% elongation was used as a sheath fiber.

#### *2.1.1. Yarn and denim fabric production*

Dual core-spun yarn samples were produced with modified ring spinning system which was designed by adding an extra creel for facilitating both elastane and filament feeding at the same time as a core. Schematic representation of dual core-spun yarn production, combination of materials and cross-sectional view of yarn are illustrated in **Figure 1(a–c)**, respectively.

As seen from this **Figure 1**, both PET filament and elastane are driven by positive feed roller, separately. These components are fed to the nip point of the front rollers by means of V-grooved roller and at the same time cotton fiber wraps over these components, as well (**Figure 1b**). Thus, the draft of PET filament and elastane are achieved by speed difference between yarn delivery and front roller of drafting unit. Here, PET filament draft was kept constant as 1.08. On the other hand, draft of elastane was varied with 2.9, 3.2, 3.5 and 3.8. In doing so, 16 Co/PET/Elastane dual core-spun yarn samples were obtained with two different cores in order to benefit from these properties.

With all these progresses show that the production of alternative functional fabrics has become possible with the use of dual core-spun yarns within the fabric structure. El-Tantawvy et al. investigated the pilling properties of fabrics produced from dual core-spun yarns with and without welding process. Elastic core-spun yarn was also produced to determine the differences. They found that both types of dual core yarns exhibit less pilling then the core spun yarn fabrics [29]. Bedez Ute was focused directly on mechanical and dimensional properties of denim fabrics made from double core and core-spun weft yarns used at different densities. It was concluded that weft density effect was higher than weft yarn composition for mechani-

It is envisaged that researches will be developed in the production of different yarn compositions, so the use of dual yarns in denim fabric production will probably become widespread due to its advantages properties with respect to conventional ones. This study was carried out in order to contribute to the use of dual core-spun yarns in denim fabrics and bring a different perspective. This is experimental study is designed in order to compare breaking force, breaking elongation, static tear force, elastic recovery, moisture management i.e. vertical wicking and water absorbency rate of twill 3/1 denim fabrics made from cotton covered filament, both

This study was conducted about the performance of denim fabrics containing dual core-spun yarns with filament fineness and elastane draft ratio variables. In that respect, the properties of drawn textured polyester filaments with conventional, fine and micro fineness are given in **Table 1**. All polyester (PET) filaments were selected among the most commonly used commercial types which are named as stretch textured yarns. Since microfilaments are sensitive to heat and it is necessary to omit the second heating zone during texturing, the PET filaments

Basically, the study was focused on three different yarn types such as 100% cotton (Co) yarn, cotton covered filament core-spun yarn by using 110 dtex PET filament with different filament fineness as a control variables, and also cotton covered dual core-spun yarns by feeding 110 dtex PET filament with different filament fineness and 78 dtex elastane filament as core within the yarn structure. In the yarn production, cotton fiber with the physical properties of 28.53 mm staple length, 4.56 micronaire fineness, 31.53 gf/tex tenacity and 28.53% elongation

Dual core-spun yarn samples were produced with modified ring spinning system which was designed by adding an extra creel for facilitating both elastane and filament feeding at the same time as a core. Schematic representation of dual core-spun yarn production, combination of materials and cross-sectional view of yarn are illustrated in **Figure 1(a–c)**, respectively.

cal and dimensional properties of denim yarns [30].

**2. Materials and methods**

**2.1. Materials**

22 Engineered Fabrics

are in set form.

was used as a sheath fiber.

*2.1.1. Yarn and denim fabric production*

filament and elastane core-spun yarns and 100% cotton yarn in weft.

**Figure 1.** Schematic illustration of modified ring spinning system, positioning the PET/elastane core at the nip point of the front roller, double core-spun yarn view (It may not be reproduced without permission); (a) modified ring spinning frame, (b) combination of materials, (c) simulated longitudinal and cross-sectional view of double core-spun yarn containing filaments [32].

In the production of the PET filament core-spun yarns, same system was used without extra elastane feeding and all production parameters were kept constant. Moreover, 100% Co ring spun yarn was also produced without both PET and elastane feeding. Combed cotton roving with 844 tex linear density was used for the production of all yarn types in order to produce 42 tex yarn samples at 9500 rev/min spindle speed and 660 turns/m twist.

*2.2.1. Moisture management*

liquid in textile clothing [31].

2 min.

order to comple the wicking tests in shorter time.

a developed algorithm (**Figure 4**).

"Moisture Management" includes all the terms of wicking, wetting, absorbency or transportation and these properties of the fabrics are related to the ability of a textile fabric to transport moisture away from the skin to fabric's outer surface in multi-dimensions. Wetting and wicking are considered as the most important parameters for absorption and transportation of

Denim Fabrics Woven with Dual Core-Spun Yarns http://dx.doi.org/10.5772/intechopen.80286 25

Wicking is the flow of a liquid in a porous substance in time which is driven by capillary forces [32]. Vertical wicking rate was conducted by various researchers in different ways [31–35]. In this study, to evaluate vertical wicking properties of denim fabrics, 20 cm × 2.5 cm strip test specimens for warp and weft direction were prepared. Denim fabrics were suspended vertically with its 3 cm of lower end immersed in a reservoir of distilled water colored with 0.01 g red dye to observe the rate of the uptake of the liquid easily. Because of the high areal density of denim fabric samples, to be tested pretension was applied with two clips totally to ensure 2 g of dead load. The wicking height of liquid rising was measured after 30 min time intervals and wicking rate was determined as mm/s. Wicking tests were conducted with five samples for both warp and weft directions of each fabric. In order to complete the wicking tests more quickly, a new wicking apparatus design was made (**Figure 2**). The designed apparatus consists of five clamps for sample hanging, five rulers placed next to the fabric samples for height measurement and reservoir. Since it is planned that five specimens will be applied to vertical wicking test procedure for each sample, such an apparatus is desinged and manufactured in

Wettability terms is explained as the first impression of fabric when get into touch with liquid. When the fabric is wetted the interaction between the forces of cohesion (within the liquid) and the forces of adhesion (between the fibers and the liquid) determines whether wetting takes place or not and also determines spreading and absorption of the liquid over the surface of the textile material [31]. In order to determine the absorption areas of the fabrics after 0.2 ml water was dropped, image analysis was conducted by using camera to catch the visual after

In order to determine the absorption rate and area of the fabrics wetted, image processing method was used. For this aim, an image acquisition system was built up. The system consists of a digital microscope camera, lightening system, camera attachment equipment and computer (**Figure 3**). Since the liquid existence within the fabric structure will lead to different light transmission level in comparison to dry regions, it was considered that the dry and wetted regions can be distinguished by applying a logical threshold operation in accordance with the pixel light intensity values. So, the back lightening system was selected for this study. The denim fabric samples were placed over the lightening unit. The colored solution with 0.2 ml volume was dropped on the fabric sample by means of a screwed syringe. At the same time, the video acquisition of the digital microscope camera was started. The image frames with the size of 640 × 480 pixels were snap shotted at instant of solution drop fell on the sample and 2 min after dropping. These image frames in JPG format were acquired from three different parts of each fabric sample. The acquired image frames were analyzed by means of

These yarn samples were used as weft yarn and indigo dyed 100% Co ring spun yarn with 59 tex linear density was used as a warp yarn. Twill denim fabrics with 3/1 pattern were manufactured at constant structure parameters such as; 26 ends/cm warp density, 20 picks/cm weft density, 480 rev/min weaving machine speed, 180 cm reed width, 65/4 reed number. After the denim fabric production, singeing, desizing and finishing processes and thermal fixation processes were carried out. Finally, 21 different denim fabric samples were obtained. Design of experiment for denim fabrics is shown in **Table 2**.

#### **2.2. Methods**

Denim fabric samples were conditioned in standard atmosphere conditions at 20 ± 2°C temperature and 65 ± 4% relative humidity for 24 hours in accordance with BS EN ISO 139 standard [33]. Tested denim fabric properties, related standards and test proceduresare illustrated in **Table 3**.

**Table 2.** Design of experiment for denim fabric samples.

**Table 3.** Test standards used and procedure achieve to determine denim fabric properties.

#### *2.2.1. Moisture management*

In the production of the PET filament core-spun yarns, same system was used without extra elastane feeding and all production parameters were kept constant. Moreover, 100% Co ring spun yarn was also produced without both PET and elastane feeding. Combed cotton roving with 844 tex linear density was used for the production of all yarn types in order to produce

These yarn samples were used as weft yarn and indigo dyed 100% Co ring spun yarn with 59 tex linear density was used as a warp yarn. Twill denim fabrics with 3/1 pattern were manufactured at constant structure parameters such as; 26 ends/cm warp density, 20 picks/cm weft density, 480 rev/min weaving machine speed, 180 cm reed width, 65/4 reed number. After the denim fabric production, singeing, desizing and finishing processes and thermal fixation processes were carried out. Finally, 21 different denim fabric samples were obtained.

Denim fabric samples were conditioned in standard atmosphere conditions at 20 ± 2°C temperature and 65 ± 4% relative humidity for 24 hours in accordance with BS EN ISO 139 standard [33]. Tested denim fabric properties, related standards and test proceduresare illustrated in **Table 3**.

**Fabric type Filament fineness (dtex) Elastane draft**

Dual core-spun denim 3.05, 1.15, 0.57 and 0.33 2.9, 3.2, 3.5 and 3.8

100% Co denim — — Filament core-spun denim 3.05, 1.15, 0.57 and 0.33 —

of each fabric were cut with dies and weighed on a precision scale and then

Breaking force and elongation for both warp and weft direction were determined

Test samples were prepared in accordance with single-tear method. Tests were performed for warp and weft yarns, separately, for each specimen at 100 mm/min

To determine the elasticity properties of fabrics, Method A was used. The number of the cycling load was 50 cycles (instead of 5 cycles because of detecting elastic recovery under higher number of cyclic loading) with applying load of 6 N/cm

.

multiplied by 100. The fabric weight was calculated in g/m2

BS EN 1049-2 The numbers of warp and weft yarns in 1 cm were determined for each fabric

Fabric thickness ISO 5084 Thickness measurements for each fabric samples were taken by means of digital

at 200 mm gauge length, 100 mm/min test speed.

thickness tester.

samples.

test speed.

width of the fabric.

**Table 3.** Test standards used and procedure achieve to determine denim fabric properties.

42 tex yarn samples at 9500 rev/min spindle speed and 660 turns/m twist.

Design of experiment for denim fabrics is shown in **Table 2**.

**2.2. Methods**

24 Engineered Fabrics

Warp and weft density

Breaking force and elongation

Static tear force BS EN ISO

Elasticity BS EN

**Properties Standard Procedure** Fabric weight ISO 3801 100 cm2

**Table 2.** Design of experiment for denim fabric samples.

BS EN ISO 13934-1

13937-2

14704-1

"Moisture Management" includes all the terms of wicking, wetting, absorbency or transportation and these properties of the fabrics are related to the ability of a textile fabric to transport moisture away from the skin to fabric's outer surface in multi-dimensions. Wetting and wicking are considered as the most important parameters for absorption and transportation of liquid in textile clothing [31].

Wicking is the flow of a liquid in a porous substance in time which is driven by capillary forces [32]. Vertical wicking rate was conducted by various researchers in different ways [31–35]. In this study, to evaluate vertical wicking properties of denim fabrics, 20 cm × 2.5 cm strip test specimens for warp and weft direction were prepared. Denim fabrics were suspended vertically with its 3 cm of lower end immersed in a reservoir of distilled water colored with 0.01 g red dye to observe the rate of the uptake of the liquid easily. Because of the high areal density of denim fabric samples, to be tested pretension was applied with two clips totally to ensure 2 g of dead load. The wicking height of liquid rising was measured after 30 min time intervals and wicking rate was determined as mm/s. Wicking tests were conducted with five samples for both warp and weft directions of each fabric. In order to complete the wicking tests more quickly, a new wicking apparatus design was made (**Figure 2**). The designed apparatus consists of five clamps for sample hanging, five rulers placed next to the fabric samples for height measurement and reservoir. Since it is planned that five specimens will be applied to vertical wicking test procedure for each sample, such an apparatus is desinged and manufactured in order to comple the wicking tests in shorter time.

Wettability terms is explained as the first impression of fabric when get into touch with liquid. When the fabric is wetted the interaction between the forces of cohesion (within the liquid) and the forces of adhesion (between the fibers and the liquid) determines whether wetting takes place or not and also determines spreading and absorption of the liquid over the surface of the textile material [31]. In order to determine the absorption areas of the fabrics after 0.2 ml water was dropped, image analysis was conducted by using camera to catch the visual after 2 min.

In order to determine the absorption rate and area of the fabrics wetted, image processing method was used. For this aim, an image acquisition system was built up. The system consists of a digital microscope camera, lightening system, camera attachment equipment and computer (**Figure 3**). Since the liquid existence within the fabric structure will lead to different light transmission level in comparison to dry regions, it was considered that the dry and wetted regions can be distinguished by applying a logical threshold operation in accordance with the pixel light intensity values. So, the back lightening system was selected for this study.

The denim fabric samples were placed over the lightening unit. The colored solution with 0.2 ml volume was dropped on the fabric sample by means of a screwed syringe. At the same time, the video acquisition of the digital microscope camera was started. The image frames with the size of 640 × 480 pixels were snap shotted at instant of solution drop fell on the sample and 2 min after dropping. These image frames in JPG format were acquired from three different parts of each fabric sample. The acquired image frames were analyzed by means of a developed algorithm (**Figure 4**).

**Figure 2.** Vertical wicking apparatus.

**Figure 4.** Absorption area calculation algorithm.

Denim Fabrics Woven with Dual Core-Spun Yarns http://dx.doi.org/10.5772/intechopen.80286 27

**Figure 5.** Absorption area at the drop fell.

**Figure 6.** Absorption area 2 min after dropping.

**Figure 3.** Image acquisition system.

First of all, the color image frame in RGB format was converted into gray scale. Then, the image enhancement operations such as noise removing and smoothing were applied by using average and Gaussian filters respectively. Gaussian smoothing commonly forms the first stage of an edge-detection algorithm [36].  In order to eliminate the noises caused from lightening condition and electrical reasons, the noise removing filters are applied [36]. The enhanced image frame was converted into binary form by applying a suitable threshold value. All pixel values of the filtered gray image were replaced with the value 1 (white) when the corresponding pixel value greater than threshold level, otherwise it was replaced with the value 0 (black). The white pixels in the binary image correspond to the liquid absorbed area and the black pixels correspond to dry area. In order to determine the exact absorption area and remove the other unnecessary parts, opening morphological operation was applied. Morphological opening is erosion followed by dilation, using the same structuring element for both operations. The opening operation has the effect of removing objects that cannot completely contain the structuring element. Boundary of the absorption area was labeled by means of "canny" edge detection method. Finally, the area of the absorption region was calculated. The absorption rate of each denim fabric sample is calculated as the percentage of white pixels to the whole pixels of the binary image. The application results of the developed algorithm at the instant of drop fell and 2 min after dropping were given in **Figures 5** and **6**, respectively.

**Figure 4.** Absorption area calculation algorithm.

**Figure 5.** Absorption area at the drop fell.

First of all, the color image frame in RGB format was converted into gray scale. Then, the image enhancement operations such as noise removing and smoothing were applied by using average and Gaussian filters respectively. Gaussian smoothing commonly forms the first stage of an edge-detection algorithm [36].  In order to eliminate the noises caused from lightening condition and electrical reasons, the noise removing filters are applied [36]. The enhanced image frame was converted into binary form by applying a suitable threshold value. All pixel values of the filtered gray image were replaced with the value 1 (white) when the corresponding pixel value greater than threshold level, otherwise it was replaced with the value 0 (black). The white pixels in the binary image correspond to the liquid absorbed area and the black pixels correspond to dry area. In order to determine the exact absorption area and remove the other unnecessary parts, opening morphological operation was applied. Morphological opening is erosion followed by dilation, using the same structuring element for both operations. The opening operation has the effect of removing objects that cannot completely contain the structuring element. Boundary of the absorption area was labeled by means of "canny" edge detection method. Finally, the area of the absorption region was calculated. The absorption rate of each denim fabric sample is calculated as the percentage of white pixels to the whole pixels of the binary image. The application results of the developed algorithm at the instant of drop

**Figure 2.** Vertical wicking apparatus.

26 Engineered Fabrics

**Figure 3.** Image acquisition system.

fell and 2 min after dropping were given in **Figures 5** and **6**, respectively.

**Figure 6.** Absorption area 2 min after dropping.

#### *2.2.2. Statistical analysis*

In statistical analysis, multivariate analysis of variance (MANOVA) was achieved at 95% confidence interval by means of SPSS package program to determine whether there was statistically significant effect of the filament fineness, and elastane draft on denim fabric breaking force, breaking elongation, static tear force, elasticity, wicking rate and water absorption rate. The evaluated independent parameters were used as weft yarn in the denim fabric production so all response variables were analyzed in weft direction as well.

**3.1. Breaking force and elongation**

filament core-spun yarns and dual core-spun yarns.

To advance the comfort performance of the denim fabrics during body movement, dual core-spun yarns including elastane that provide higher elasticity and recovery are preferred. However, this advantage brings a disadvantage together and it leads to decrease in tensile strength of denim fabrics [30, 37]. The tensile test outcomes of the presented study is illustrated in **Figure 7** the breaking force of denim fabrics in weft wise composed of 100% Co,

Denim Fabrics Woven with Dual Core-Spun Yarns http://dx.doi.org/10.5772/intechopen.80286 29

It was clearly seen in **Figure 7** that denim fabrics made from filament core-spun yarns have the higher breaking forces than both 100% Co and dual core-spun denims. The highest breaking force was detected at 0.33 dtex filament core-spun denim fabric, this result is attributed to the fact that more filaments in the yarn cross-section can provide more resistance against tensile force. When the breaking forces of dual core-spun denim fabrics are taken into consideration in terms of effect of elastane draft, it is observed that the breaking force of samples with conventional firmament have increasing trend by increasing elastane draft. However, the fabric samples with micro fineness filament have decreasing trend with the elastane draft increase. Since increasing the elastane draft leads to decrease of elastane ratio within the fabric increase and so increase in breaking force of the fabric. However, this result is not clearly seen for dual

The breaking elongation of denim fabrics are shown in **Figure 8**. Elastane content contributes the elongation of the denim fabrics and this situation is clearly observed among the all fabric samples. Elongation directly affects the elasticity properties of the denim fabrics this is why elastane is used. The lowest breaking elongation was obtained with 100% Co denim fabric without both filament and elastane. When the denim fabrics made from filament core-spun yarns are investigated, it is seen that 3.05 dtex filament core-spun denim fabrics has the highest breaking elongation value and breaking elongation values of all filament core-spun denim fabrics are similar. From these results, it can be revealed that filament core part contributes

It can be possible to see how dominant the effect on the denim fabric elongation performance of the elastane draft ratio is. It is observed that with the increase of the elastic draft ratio, the breaking elongation of the fabric increases. This situation may be attributed to the fact that higher elastane draft may probably leads to increase in cohesion forces between filament, elastane and Co, and so breaking elongation can probably increase. Hereby, it can be said that elastane represents a large majority of the extensible part of fabric under the tensile force. In terms of filament fineness parameter of dual core-spun denim fabrics, it can be said that breaking elongation varies from 38.18 to 44.08%. The predominant elasticity property of elastane makes it difficult to see the effect of filament fineness in **Figure 8**, however, with

Static tear force in weft direction of denim fabric samples are illustrated in **Figure 9**.

According to tear force values, it is seen that 100% Co denim fabric has lower value than that of denim fabrics containing both filament and dual core-spun yarns. On the other hand, PET

core-spun denim fabrics due to involving both PET filament and elastane.

strength of the fabric with an acceptable elongation value.

statistical analysis effects can be examined in detail.

**3.2. Static tear force**