Cotton is one of the dominating commercial crops in the United States of America, especially for the State of Texas. Texas ranks first in cotton yields which accounts for approximately 40% of the total production of the country; meanwhile cotton is the leading cash crop of the state (Matocha, Allen, Boman, Morgan, & Baumann, 2009). Texas cotton plantings are widespread in six major cotton-producing regions, which are named as high plains region, rolling plains region, black-lands, coastal bend and upper gulf coast, lower rio grande valley, and far west Texas (Robinson & Mccorkle, 2006; Matocha, et al. 2009). The heaviest concentration of cotton acreage in Texas (and the U.S., and the world) lies in the northwest High Plains region, which is specified in Figure 1 (Robinson & Mccorkle, 2006). This region is within 130-km radius around Lubbock, TX, where is semi-arid and around one-half of the cotton acreage is non-irrigated (Wanjura, Upchurch, Mahan, & Burke, 2002). The average cotton yield in this region was 703 and 555 lbs/acre, respectively, ranging from 2010 to 2011 (USDA/NASS, 2011). The yield variety highly depends on the local climate and irrigation capacity; precipitation is sporadic during the growing seasons, therefore, the irrigation source becomes crucial. Ogallala Aquifer, one of the largest freshwater aquifers, is the major available water supplement for local cotton growth and production (Colaizzi, Gowda, Marek, & Porter, 2008). Over 90% of Ogallala withdrawals in the Texas high plains region are for irrigation; however, Ogallala is a closed basin and withdrawals highly exceeded the recharge, leading to a severe groundwater level declination during irrigation season (Colaizzi et al. 2008). Irrigation water availability is now a priority concern in cotton production during the dry summer in the whole area. If the cotton can get enough water in their growth process, they tend to be more mature fibers, which will be good for dyeing in textile industry. More watering also means higher yield. The lack of water here, unexpectedly, caused some cotton boll to a creamy color instead of desired white, which decreased its selling price due to the hard dyeing process for the buyers (Dykowski, 2011). The harvest acres, relative to the percentage of planted acres, are also considerably and heavily influenced by the insect pests control, weeds control, and biochemical control etc. (Baumann & Lemon, 2007; Kosmidou-Dimitropoulou, 1973; Parker, Fromme, Knutson, & Jungman, 2004). From a long perspective, Texas cotton will keep facing a long-term challenge.
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Figure 1. Regional Concentration of Texas Cotton Plantings (Source: USDA/TASS)
1.2 Cotton Fiber Development Overview
Cotton fiber development mainly consists of five overlapping developmental stages: fiber initiation, primary cell wall synthesis (elongation), transitional phase, secondary cell wall synthesis, and fiber maturity (Kim & Triplett, 2001); during all above developmental stages, important structure changes leading to the cellulose macromolecules beta-D-1,4-glucopyranosyl formation (Timpa & Triplett, 1993). Table 1 roughly gives the approximate timing of the phases of cotton fiber development (Timpa & Triplett, 1993). More than 90% of mature cotton fibers are made of cellulose. The cellulose structure presents a hierarchy organization: the smallest grouping of cellulose chains is elementary fibrils; elementary fibrils bind together into microfibrils; then bundles of microfibrils form a cotton fiber structure which result in the formation of crystalline and amorphous regions. With the specific overall knowledge of cotton fiber development in mind, it is beneficial for the agriculturalists to identify the traits that mostly affected by drought stress, thereafter finding a way to enhance the fiber quality and modify related genes; thus, providing the manufactures with much more better cotton fibers. Meanwhile, this knowledge is also useful for us to understanding this thesis discussion part; therefore, a detailed introduction will be carried out in the following paragraphs.
Table 1. Approximate timing of the phases of cotton fiber development.
(Timpa & Triplett, 1993)
1.2.1 Cotton Fiber Initiation and Elongation
Cotton fiber initiation commences on or near the day of anthesis, which signals the onset of fiber morphogenesis (Abidi et al., 2010). Figure 2 elaborately described the process: before initiation the epidermal cell surfaces are rectangular to irregular until the fiber development starts, the differentiating cells become rounded and enlarged; subsequent expansion is mainly outward, thus the initials assume a spherical shape above the epidermis with a diameter about twice of the nonexpanding epidermal cells; thereafter, the growth is more rapid on the chalazal side of the cell (Butterworth et al., 2009; Stewart, 1975). Approximately 25% of the ovular epidermal cells differentiated into the commercially important lint fibers (Kim & Triplett, 2001).
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Fiber cell elongation and fiber initiation are fairly synchronous at anthesis on each ovule and among the around 25 to 30 ovules per boll (Kim & Triplett, 2001). Fiber elongation process is also called the primary cell wall synthesis process; it lasts from anthesis around to 15 days post anthesis (dpa). Fibers will elongate and deposit a thin primary wall, which mainly composes essentially of uronic acid rich polymers, xyloglucan, some proteins and celluloses (Fagard et al., 2000). During this rapid polar elongation period, fiber cells elongate to over 2.5 cm whithin three weeks after anthesis (N Abidi et al., 2008; Timpa & Triplett, 1993). The level of cellulose in the very thin primary wall is relatively constant during elongation; thereafter it will represent around 20 to 25 % of the dry weight of the wall (Meinert & Delmer, 1977).
Figure 2. Details of Fiber Initiation. (Stewart, 1975)
1.2.2 Cotton Fiber Transitional Phase
Cotton fiber development transition phase is from 16 dpa until around 21 dpa; which is the most interesting phase of fiber development because it is characterized by the transition from primary to secondary cell wall synthesis (Abidi et al., 2010; Wang et al., 2010). During this stage, the cell wall first begins to thicken through the decomposition of a "winding" cell wall layer containing cellulose microfibrils with a 45 degrees angle relative to the longitudinal fiber axis (Singh, Cheek, & Haigler, 2009). The phase is characterized by an abrupt change in the rate of cellulose synthesis and dramatic increase of the degree of polymerization (from a DP of less than 5,000 to around 13,000) (Abidi et al. 2010). Subsequently, elongation ceases and the cellulose-rich secondary wall are deposited (Singh et al. 2009).
1.2.3 Cotton Fiber Cellulose Synthesis and Maturation
The stage of active secondary cell wall growth, which commences around 21 dpa and continues for approximately three to six weeks post anthesis is marked by the massive deposition of a thick cellulosic wall (Abidi et al. 2010; Timpa & Triplett 1993; Wilkins et al. 1999). In this stage, bundles of fabrils form the microfibrils, the main structure components in second cell wall, with the typical helicoidally arrangement (Delanghe). Meanwhile, microfibrils bundles of secondary walls are increasing in length by tip growth, and glucose residues are added to them simultaneously at all the cellulose chains they contain (Delanghe). During the growth of the cotton fiber, sucrose is converted by enzymes to one molecule of glucose and one molecule of fructose; then fructose is converted into glucose. Finally, the polymerization reactions (condensation) between glucose molecules lead to the formation of cellulose macromolecules (Abidi et al. 2010). Compared with the cellulose, the hemicelluloses and pectin substances are very rare in the secondary wall (Delanghe). By the time of boll maturation the secondary wall will normally fill the major part of the cell volume, leaving the lumen, the small central cavity (Delanghe). Mature cotton fibers are made of 99% of cellulose.
1.3 Analytical and Spectroscopic Methods
1.3.1 Electron Microscopy
Electron microscopy is an instrument using a beam of energetic electrons to show the interior or the surface of an object. Its advent is due to the limitation of the light microscopes which are limited by the physics of light (Bellis, About.com Guide). In the early 1930's the optical microscope had limited the scientists' desires to observe the interior structures that required a higher magnification. In 1931, Max Knoll and Ernst Ruska in Germany developed the first type of electron microscope, the transmission electron microscope (TEM), which uses a focused beam of electrons to take place of the light to see through the specimen (Bellis, About.com Guide). Until 1938, the first scanning electron microscope (SEM) was developed; its late show up is due to the electronics need to scan the beam of electrons across the samples (Bellis, About.com Guide). An electron microscope mainly consists of four components: an electron optical column, a vacuum system, a signal detection, and display.
A transmission electron microscope (TEM) operates on the same basic principal as the light microscope but utilizes the wave properties of moving electrons to generate highly resolved images of specimens (Nölting, 2006). The TEM use electron as "light source" and its much lower wavelength make it possible to get a resolution thousand times better than with a light microscope (Nölting, 2006). It produces a black and white image from the interaction between the prepared samples and the energetic electrons in the vacuum chamber; air was pumped out of the vacuum chamber to create a space where electrons are able to move, then the electrons will pass through multiple electromagnetic lenses, down to the column and make good contact with the screen where the electrons will convert to light and form an image (The complete Microscope Guide). Overall, it is a powerful instrument; the limitation of it is large and expensive.
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A scanning electron microscope (SEM) images a sample by scanning its surface with a focused beam of electrons to generate a series of signals, which could reveal related information about the sample morphology and crystalline structure etc. TM-1000 SEM (Hitachi High Technologies America, Inc. Pleasanton, CA.), a type of environmental SEM, was performed in this thesis to image the cotton fiber samples with a backscattered electrons detector at 15kV accelerating voltage. The SEM has a separation membrane between the specimen chamber and the electron gun, therefore, one chamber for the sample and one for the electron gun. The electron gun is at higher vacuum chamber, while the specimen is at lower vacuum chamber, which allows for the transfer of the electron beam to specimen chamber to image the samples at their natural status in a gaseous environment. The instrument is not only easy to use and maintain, but also has a superior resolution and higher magnification.
1.3.2 Fiber Quality Measurements
Cotton fiber quality determines its position in the international market; fibers with good quality always attract the attentions from importers. In reality, cotton fiber quality is affected by irrigation, freezing, pesticide, and quality of soils (Bradow et al. 1996; Karademir et al. 2011). The quality information obtained from related instruments will be helpful for breeders and agriculturalists to find ways to modify series genes and improve the cotton growth environment, thus improve fiber qualities. Therefore, it is urgent for us to grasp a fundamental fiber quality testing method. In this thesis, we particular focused on high volume instrument (HVI), advanced fiber information system (AFIS), cross-section, and cotton-scope to measure cotton fibers micronaire, maturity ratio, fineness, length distribution, and maturity.
HVI system is a widely used method based on fiber bundles testing to provide varied fiber qualities such as micronaire and so forth. In this system, lots of fibers are checked at the same time and the average value is determined by fibro-gram method. Bundles of fiber samples with known weight are compressed and examined at one time in a chamber of given volume; a steam of air would be passing through the plug of fibers, the volume of air we got is the micronaire. Moreover, the HVI system can give an indication of length distribution in quick. The length measured by HVI is called upper half mean length (UHML), which is an average of the longest one-half of the fibers in the sample.
AFIS is an advanced and reliable measurement to predict detailed individual fiber qualities (length, neps, fineness, maturity, and so on). It is more precise in short fiber measurement and could cover some properties not measured by HVI. It works as the following: a sliver should be shaped by hand first and be put into a canister, fibers and trash would be separated into different popes (individualizer); when the fibers passing through the laser beam and detector, the size and length of the fiber quality would be acquired through the sensor and the fiber travel speed. The limitation of AFIS is time-consuming as the fibers need to be looked one by one. Meanwhile, the needles on the instrument gear, the friction between cotton, and the friction between cotton and instrument all might cause a biased result.
Cotton fiber maturity is a fiber characteristic that measures the primary and secondary wall thickness. It defines the relative degree of thickening of the fiber wall, thus, it can be used as an index of cotton fiber maturity. Cotton fiber maturity determines its physical properties and value; completely developed, mature fibers have thicker secondary walls than undeveloped, immature fibers (Goynes Jr, 2003). Thin-walled fibers are easy to create some problem in dyeing and processing because they fail to accept dyes and can produce other defects in textiles (Goynes Jr, 2003). On the other hand, mature fibers are inherently stronger than immature fibers due to more crystalline cellulose structures existing. A cotton fiber consists of a cuticle, a primary layer, a secondary layer, and the central canal of the fiber lumen showed in figure 3 (Rjiba et al. 2009 & Hatch 1993). The secondary wall thickening of the mature fibers is very high and even invisible sometimes; however, in immature fibers, the secondary cell wall thickening is practically absent leaving a wide lumen throughout the fiber. The maturity quality may have a great affect on the fineness of cotton fiber samples. Additionally, the maturity of the fiber, i.e. the cell wall thickening, is highly sensitive to the cotton growth conditions. Severe weather, poor soil, diseases and pests all may increase the proportion of immature fibers and result in processing troubles like forming neps and dyeing specks. Furthermore, maturity will affect the yarn quality like loss of yarn strength and processing procedure especially in the spinning process. Therefore, an accurate and proper maturity measurement is essential to assess the fiber relative quality. Cotton fiber maturity can be measured by cross-sections and cotton-scope.
Fiber cross-section is a direct and accurate measurement of fiber fineness and maturity, which is a process to extract useful information from images. What's more, the cross-sectional measurements of cotton maturity may be applied as a reference when other methods need to be calibrated (Xu & Huang, 2004). A microscope and software were applied to analyze the cross-sections. Cotton cross-section followed by image analysis is used to increase the efficiency and accuracy of fiber separation and feature extraction. Fiber cross-sections were performed according to the protocol (Hequet, 2006): firstly, the fiber sample bundles were embedded in a methacrylate polymer, which holds the cotton fibers until they could be glued to a slide for observation. Then, the methacrylate polymer is dissolved in methyl ethyl ketone (MEK). After the 1Î¼m thick cross-sections slides are well prepared, the images were observed with a microscope and a Hitachi CCD Camera Model HVC-20 with a Coreco Oculus TCX Frame Grabber. Figure 4 (Hequet, 2006) shows the schematic of a cotton fiber cross-section, from which we can deduce the following equitation:
Where Aw is the cell-wall area (cross-sectional area minus lumen area), R1 represents the inside radius, and the R2 represents the outside radius (Hequet, 2006). Following, the degree of secondary wall thickening, Î¸ (theta), defines the ratio of the area of the cell wall to the area of a circle having the same perimeter as the fiber section in equation 2 (Hequet, 2006):
Where Î¸ is the degree of secondary wall thickening and P2 is the outside perimeter of the fiber in microns.
Thereafter, the FIAS software developed by Xu & Huang (2004) was performed to analysis the image files. The limitation of this method is time consuming and tedious procedure for both preparing cotton samples and processing cross-sectional images, which need take five days to test one sample.
Cotton-scope is another very useful instrument for cotton fiber development studies. This instrument is an automated version of polarized light microscopy (PLM) Standard Test Method (ASTM D 1442,00), which used interference colors to identify the maturity of a cotton specimen (Brims & Hwang, Cottonscope Pty Ltd.). It is a computer controlled, digital video microscope developed for fast measurement of cotton fibers (Brims & Hwang, Cottonscope Pty Ltd.). It uses polarized light microscopy and image analysis to measure maturity. It can handle very small sample sizes, normally 50 mg per replication. Against the magenta background, the mature fibers appear yellow while the immature fibers appear red or blue, which is showed in figure 5 (Brims & Hwang, Cottonscope Pty Ltd.). The cotton-scope instrument need to be calibrated using a subset of the 104 International Textile Center (ITC) fiber maturity reference cottons (Hequet, 2006; Brims & Hwang, Cottonscope Pty Ltd.).
Figure 3. a. Structure of Cotton Fibers, b. Cross-section of the Cotton Fiber.
(Rjiba et al. 2009 & Hatch 1993)
Figure 4. Schematic of a Cotton Fiber Cross-section. (Hequet, 2006)
Figure 5. Cotton-scope Image: Mature (yellow) and Immature (red or blue)
(Brims & Hwang, Cottonscope Pty Ltd.)
1.3.3 Thermal Properties
The thermal property refers to the character of a cotton fiber when it is subjected to heat. It mainly includes of thermal conductivity, heat of wetting or heat of absorption, glass transition temperature, melting temperature, heat setting, and thermal expansion (Ageorges, Ye, & Hou, 2001). Cotton fibers are commonly applied in textile fabrics to protect the wearer from cold to heat; thus, it is a must for the clothing fabrics to ensure appropriate heat transfer between human body and its environment in order to maintain the physiological thermal balance of weather (StankoviÄ‡, PopoviÄ‡, & PopariÄ‡, 2008). Therefore, it is of great importance to understand the heat transfer phenomena through the cellulose existing in the cotton fibers and investigate the thermal properties of cotton fibers (StankoviÄ‡ et al., 2008).
Thermogravimetric Analysis (TGA) was employed to measure the cotton fiber thermal property, which measures the weight changes in cotton fibers as a function of temperature increases; therefore, it allows determination of characteristics of polymers such as degradation temperature, the amount of absorbed water, et (Delhom et al., 2010; John & Keller, 1996). TGA analyzer consists of high precision balance with a pan (generally ceramic), which is placed in a small electricity heated furnace equipped with a thermocouple to accurately measure the temperature; the atmosphere around the sample would be purged with inert gas (N2) to prevent oxidation and other undesired reactions. A computer would be used to control the instrument. The derived characteristic thermogravimetric curves will reveal some sample properties, like structure and constitute. As mentioned above, TGA can provide quantitative results regarding the weight loss of a sample as a function of increasing temperatures. In this thesis, TGA of fiber samples was performed using the Pyris1-TGA equipped with a 20-sample autosampler (PerkinElmer Shelton, CT). TG temperature was calculated with the curie point of alumel and nickel alloys at 10Â°C/min. TG curves were recorded between 37 and 600°C with a heating rate of 10°C/min in a flow of nitrogen at 20 mL/min. Cotton fiber samples were rolled into small bolls (between 1.5mg to 2.0 mg) by hand, and then placed on the sample pans. Samples from 14, 20, 24, 35, and 56dpa were performed, three replications for each sample. Pyris software was used to calculate the first derivatives of the thermo-grams and to calculate the percent weight loss of each sample. The first derivatives were adopted to compare thermo-grams; the inflection point at the first derivatives is the point where the degradation rate is the fastest. By using the inflection points, the thermo-grams of different cotton fiber samples were compared, which can be interpreted as the amount of water, the amount of non-cellulosic materials, the amount of cellulose and crystallinity. Cotton fiber samples were conditioned in the laboratory at 65Â±2 % relative humidity and 21Â±1°C temperature for at least 48 hours prior to the test. When it is used in combination with FTIR, the TGA/FTIR is capable to produce detailed information related to cotton fibers.
1.3.4 Spectroscopic Methods
Fourier transforms infrared reflectance, an easy-to-use and timesaving analytical instrument, has demonstrated itself as a versatile tool in various applications (McCann et al. 1992; Perkins et al. 1992; Michael et al. 1995; Melin et al. 2004; Davis & Mauer, 2010). It helps researchers to determine the type of compounds of functional groups, speculates on the molecular structure of simple chemical compounds, and supplies data for quantitative analysis, etc. (Jackson & Mantsch, 1995; Levin & Bhargava, 2005). It allows for the rapid characterization of varied functional groups such as lipids, proteins, and polysaccharides in complex structures (Melin et al. 2004; Bozkurt et al. 2007; Dogan et al. 2007; & Toyran et al. 2007). FTIR technology started its journey as early as 1910s, which was first suggested for the analysis of biological samples (Kumar & Prasad, 2011). By the late 1950s, FTIR spectroscopy has become an accepted tool for the characterization of chemical molecules and identification of chemical bonds tyes (Rodriguez & Directol, 2000). The widespread use of FTIR spectroscopy can be easily found in biology, agriculture, and textile industries (Williams et al. 1987; Hammond 1997; Raghavachari et al. 2000). Rapid and specific detection of the microstructures of compounds now is increasingly attracting scientists' interest (Al-Qadiri et al. 2006; Cheng et al. 2010; O'Gorman et al. 2010). Thus, understanding of the spectroscopic methods is of great importance.
Infrared (IR), also known as molecular vibration-rotation spectrum, resides in the absorbance spectrum. Its wavelength range is between 0.76 and 500Î¼m, which can be categorized into near-IR (0.78-2.5Î¼m), mid-IR (2.5-25Î¼m) and far-IR (25-500Î¼m). The mid-IR (wavenumber ranges from 4000Â cm to 650Â cm) is the most widely used method based on studying the interaction of infrared radiation within samples (Davis & Mauer, 2010). As a continuous frequency-changed infrared radiation is passing through a sample, the sample molecule will absorb some radiation, thus the molecular vibration and rotation will cause a net change of the dipole and the molecular energy will turn from the ground state to the excited state as well (Smith, 1996). The presence of chemical bonds in a material is a necessary condition for infrared absorbance to occur. The infrared radiation would be absorbed when interacting with a matter, which would cause the chemical bonds in the material to vibrate. The major molecular vibration types are stretching and bending (Hsu, 1997), which can further be categorized into liner and non-liner molecules vibrations showed in figure 6 (Parson, 2007). During absorption of the IR light, a molecule should meet two necessary conditions, 1) the molecule must have a vibration change during which the change in dipole moment with respect to the distance is non-zero; and 2) the energy of the light impinging on a molecule must be equal to a vibrational energy level difference within the molecule (Lau, 1999).
Figure 6. Molecular Vibrations: Linear (A-C) and Non-Linear Molecules (D-F). (Parson, 2007)
A FTIR spectrometer instrument typically consists of a beam splitter, a fixed mirror, and a mirror that moves back and forth (Newport, Inc.). The core of the FTIR is a Michelson Interferometer showed in figure 7 (Nölting, 2006). Firstly, a beam of radiation from the IR source is focused on beam splitter so that half the beam is transmitted to a moving mirror and the other half is reflected to a fixed mirror (Nölting, 2006). The moving mirror moves at a fixed rate. Its position is determined accurately by counting the interference fringes of a collocated He-Ne laser (Shivaram). Both the moving mirror and the fixed mirror will reflect the beam back to the beam splitter which reflects the half of both beams to the detector at which place they interfere based on their phase difference (Nölting, 2006). Once a sample is inserted in one of the beam paths, the interference will be changed. Interferograms with or without sample are all recorded and the absorption of the sample will be calculated finally using inverse Fourier transform showed in figure 8 (Nölting, 2006). Inverse Fourier transforms of two interferograms yields the IR intensities; the IR absorption spectrum is going to be calculated by the logarithm of the intensity quotient (Nölting, 2006).
Figure 7. Basic Components of a FTIR Interferometer. (Nölting, 2006)
Figure 8. Principal of Operation of a FTIR Spectrometer. (Nölting, 2006)
IR spectrum is the fundamental measurement obtained in IR spectroscopy, which is received by calculating the intensity of the IR radiation before and after it passes through a sample (Davis & Mauer, 2010).The spectra are conventionally plotted with high wavenumber on the left and low wavenumber on the right on the X-axis; the Y-axis can be either absorbance or transmittance units (Smith, 1999). FTIR absorbance spectra follow Beer's law (Equ.1),
A = Îµ*I*c (1)
Where A=absorbance; Îµ = absorptivity (constant); I = pathlength; c = concentration
The transmittance can be expressed in the Equ. 2.
Where IS=Intensity of IR beam after passing through the sample; IR= Intensity of IR beam before passing through the sample; T= Transmittance.
The spectra of a pure compound are unique that just like a molecular fingerprint; while the spectra of a mixture are often the combination of the spectra of its constitute species (Chen & Wang, 2001). Therefore, it is a necessary if the component spectra can be separated from the mixture. Various software packages are used to extract the useful data from complex spectra using multivariate statistical approaches (Davis & Mauer, 2010). Hierarchical cluster analysis (HCA) is a method helping to identify similarities between the spectra of mixtures by using the distance between spectra and aggregation algorithms (Beveridge & Graham, 1991). The partial least squares methods (PLS) is another methods used to build predictive models for qualitative analysis with no restrictions on the wavenumber range (Navas, 2008).Principal component analysis (PCA) is the most often used method to help reducing the multidimensionality of the data set into its most dominant scores or components, which will maintain the relevant variation between the data points (Davis & Mauer, 2010). It was invented by Pearson in 1901 and could be mathematically identified as an orthogonal linear transformation that transform the data set to a new coordinate system such that the greatest variance by projection of the data comes to lie on the first principal component, the second greatest variance on the second principal component, and so forth (Al-Qadiri et al., 2006; Lin et al., 2004). Score plots are adopted to highlight the similarities and differences. The closer the samples are within a score plot, the more similar they are with respect to the principal component score evaluated (Lin et al., 2004). The main advantage of PCA method is that once the patterns in the data were found, we can compress the data to reduce the number of dimensions without much loss of information (Patil & Ruikar, 2012).
In plant science, FTIR is mainly employed to identify cell component structures and function groups etc. In particular, it is a very useful technique in identifying unknown components in a plant tissue. In 1999, Zeier and Schreiber adopted FTIR as a direct and fast way to investigate the isolated plant cell walls. They successfully assigned FTIR frequencies to functional groups present in the cell wall, as well detected different structures under cell development stages. Katrient, et al. published their studies in 2006, discussing how FTIR were used as a rapid detection of sugars and acids profile in four commercial tomato cultivars. Without needs for laborious sample preparations and skilled technicians, both chemical compositions and taste determining compounds of individual tomato were evaluated. In this thesis, the cotton fiber samples FTIR spectra were recorded using the universal attenuated total reflectance Fourier transform infrared (UATR-FTIR) (Perkin Elmer, Waltham, MA). The UATR-FTIR was equipped with a Zn-Se Diamond crystal, allowing the spectra collection and analysis on the materials surface without any special preparations. A background scan of clean Zn-Se Diamond crystal was processed before the sample scanning procedures. There is a "pressure arm" attached to the UATR-FTIR; it is a necessary accessory to make sure a good contact pressure between the crystal and the samples positioned on its top. The pressure applied in the cotton fiber samples was 122N. All FTIR spectra were collected with spectrum resolution of 4 cm-1, with 32 co-added scans over wavenumber 4000-650 cm-1. The obtained spectra were baseline corrected, normalized and subjected to Principal Component Analysis (PCA). Before the testing, cotton fiber samples were all kept in the laboratory conditioning at 65Â±2 % relative humidity and 21Â±1°C temperature for at least two days.