Cotton fiber maturity is an important fiber physical and quality property that impacts downstream fiber processing. Fiber maturity refers to the degree of secondary cell wall thickening. The reference method for fiber maturity measurement is to quantify the secondary cell wall area relative to the perimeter of the fiber via cross-sectional image analysis, a tedious and slow process. A number of approaches have been developed which attempt to measure fiber maturity rapidly. The approach employed in this work is based on the use of attenuated total reflection Fourier transform infrared spectroscopy, and simple algorithms were developed from the spectra to estimate fiber maturity directly. To validate the efficacy of this approach against cross-sectional image analysis, two distinct fiber sets were examined that included a set of developing fibers and a diverse set of developed fibers. Comparison of image analysis and infrared maturity results imply a consistency and equivalency between the two maturity measurements.

The differential dyeing (DD) method has been a subjective method for visually determining immature cotton fibers. We attempted to quantitate DD results and offer an efficient means of elucidating cotton maturity, without visual discretion. Image analysis of cotton color obtained with a scanner was performed and compared to spectrophotometrically measured color. Low, Medium and High micronaire (Mic) cotton standards were dyed, and we determined that blending the cotton yielded more precise color measurement; with the mean red, green and blue (RGB) values for a low Mic sample being: 101.6 ± 23.9, 83.1 ± 22.0 and 98.3 ± 23.1, respectively; while blending yielded RGB values: 121.6 ± 9.0, 98.8 ± 8.6 and 116.0 ± 8.9, respectively. Comparing RGB values to Mic, it was found that R and B color parameters do not show a trend, but G values decrease with increasing Mic indicating a decrease in G dye uptake with an increase in maturity. The conventional L*a*b* color space values were also obtained for the dyed cottons and compared to RGB and Mic results. The %CV for a* values are higher compared to L* and b* measurements: ∼31.9%, 18.9% and 8.6% for low, medium and high Mic samples, respectively. L* and b* decrease while a* increases with Mic, indicating an increase in R dye with an increase in maturity. The DD Bath method was found to be reliable, with R showing the greatest variances. G and a* values obtained were found to be the best correlation of differences in Mic.

Specific levels of the carbohydrates melezitose and trehalulose deposited on the surface of cotton fibers are indicators of whitefly or aphid contamination. These deposits could cause stickiness problems during cotton ginning and textile processing. Cotton stickiness is highly complex, but surface carbohydrates may play the largest role in manifesting an issue. We utilized ion chromatography (IC) to identify and quantify nine sugars of interest present in the water extracts of 25 cotton samples to create sugar profiles for each sample: inositol, trehalose, glucose, fructose, trehalulose, sucrose, melezitose, raffinose and maltose. We compared the sugar profiles to the respective Minicard ratings of either NONE, LIGHT, MODERATE or HEAVY to draw correlations between the IC data and the rating. Trehalulose and melezitose in water extracts highly and positively correlate to Minicard ratings, confirming past researchers' attribution of cotton stickiness to insect sugars. Trehalose and maltose also highly correlated, possibly due to their marker content in honeydew. Glucose and fructose moderately correlated to the ratings. IC studies of the collected Minicard sticky spot material found trehalulose and melezitose were the most prevalent sugars in HEAVY rated samples. Glucose and fructose were present in larger amounts in the MODERATE versus HEAVY rated samples. This result may indicate that the Benedict Test, which attributes these reducing sugars to stickiness, may not be sufficient for conjecturing a stickiness issue. When comparing the averages of the nine sugars present in water extracts versus those sugars contained in Minicard sticky spots, the overall distributions were very similar.

Certain levels of the carbohydrates melezitose and trehalulose deposited on cotton surfaces are indicative of either whitefly or aphid contamination, which may cause problems during cotton processing. Raffinose and sucrose are isomers of melezitose and trehalulose, respectively, making it difficult to fully separate them via ion chromatography (IC), especially when analysis time is shortened. We have successfully developed an IC method to separate the retention peaks of melezitose from raffinose and trehalulose from sucrose, with baseline resolution and improved quantitation of these sugars. This improved separation may elucidate useful information about constituent sugars on cotton, aiding in the identification of carbohydrates possibly contributing to stickiness.

Gossypium raimondii Ulbrich, a wild diploid species of cotton, was sequenced due to its small genome size and similarity with the cultivated allotetraploid Upland cotton. The D-genome of G. raimondii has become the reference sequence used extensively in cotton genomic and genetic studies. However, phenotypic information is limited because photoperiodicity prevents flowering outside its native environment and its fiber quality cannot be measured by conventional methods. Fiber and seed properties of G. raimondii were measured and compared with those of Upland cotton cultivars. Fiber length, fineness, cellulose content, and seed lint percentage were all significantly reduced in G. raimondii compared to Upland cotton, whereas fiber maturities were comparable. Spectophotometric properties of G. raimondii fibers were similar to green Upland cotton fibers but differed from white and brown Upland fibers. Seed kernels of G. raimondii were smaller but their chemical compositions were similar to those of Upland cotton. Quantitative traits of G. raimondii will aid in interpreting its genome and accelerating comparative genomics approaches for identifying potential genes regulating fiber and kernel properties among Gossypium species.

Cotton fibre maturity and fineness are important properties for cotton quality and processing. Most methods for measuring fibre maturity and fineness involve the use of slow and laborious measurements, chemicals, and/or the use of expensive equipment and instruments. International interest has been expressed in new rapid, accurate, precise, and cost effective measurements of fibre maturity and fineness. A new small size instrument — the Cottonscope — has been introduced that simultaneously measures cotton fibre maturity and fineness. The Cottonscope yields average fibre maturity, fineness, and ribbon width, as well as distributions for maturity and width. A program was implanted to determine the capabilities of the Cottonscope instrument for maturity and fineness measurements. Examples of our experimental results and experiences with the Cottonscope are presented. Comparisons were made between the maturity and fineness results from the Cottonscope, crosssectional image analysis (reference method), SRRC Fibre Maturity Tester (FMT), and Uster Advanced Fibre Information System (AFIS) measurements. It was demonstrated that the Cottonscope yields a simultaneous, rapid, precise, and accurate measurement of fibre maturity and fineness. The Cottonscope measurement was relatively fast (less than a total of 10 minutes for 6 measurements per sample) and easy to perform. The primary impact on the Cottonscope measurement results was the change in environmental conditions (room temperature and relative humidity), and that impact was major only for fibre fineness. Measurements on experimental breeder samples indicated that the Cottonscope yielded a more representative and responsive measurement of fibre maturity and fineness compared to the corresponding AFIS results.

A fundamental understanding of the relationship between cotton fiber strength (or tenacity)/elongation and structure is important to help cotton breeders modify varieties for enhanced end-use qualities. In this study, the Stelometer instrument was used to measure the bundle fiber tenacity and elongation properties of different cotton fibers. This instrument is the traditional fiber strength reference method and could be still preferred as a screening tool owing to its significant low cost and portability. Fiber crystallinity ( CIIR) and maturity ( MIR) were characterized by the previously proposed attenuated total reflection (ATR)-based Fourier transform infrared protocol that has microsampling capability and is suitable for the tiny Stelometer breakage specimens (2 ∼ 5 mg), which cannot be readily analyzed by a conventional X-ray diffraction pattern. Relative to the distinctive increase in fiber tenacity with either CIIR or MIR for Pima fibers ( Gossypium barbadense), there was an unclear trend between the two for Upland fibers ( G. hirsutum). Although fiber elongation increases with elevated CIIR and MIR for Pima fibers, it generally decreases as CIIR and MIR increase for Upland fibers. Furthermore, small sets of Upland fibers with known varieties and growth areas were examined, and their responses to both CIIR and MIR are discussed briefly.

An investigation of the relationships among fiber linear density, tenacity, and structure is important to help cotton breeders modify varieties for enhanced fiber end-use qualities. This study employed the Stelometer instrument, which is the traditional fiber tenacity reference method and might still be an option as a rapid screening tool because of its low cost and portable attributes. In addition to flat bundle break force and weight variables from a routine Stelometer test, the number of fibers in the bundle were counted manually and the fiber crystallinity (CIIR) was characterized by the previously proposed attenuated total reflection-sampling device based Fourier transform infrared (ATR-FTIR) protocol. Based on the plots of either tenacity vs. linear density or fiber count vs. mass, the fibers were subjectively divided into fine or coarse sets, respectively. Relative to the distinctive increase in fiber tenacity with linear density, there was an unclear trend between the linear density and CIIR for these fibers. Samples with similar linear density were found to increase in tenacity with fiber CIIR. In general, Advanced Fiber Information System (AFIS) fineness increases with fiber linear density.

The co-occurrence of different types of trash in commercial cotton bales compromises the value of cotton, requires more cleaning, and influences the quality of yarn and fabric. To meet the challenge of determining the trash content, two testing methods (i.e., High Volume Instrument [HVI (TM)] and Shirley analyzer [SA]) have been utilized by trade and regulatory offices and laboratories in the cotton industry. However, these methods only report the trash amounts in total, instead of the content for individual trash components. Likely, the complexity of the co-existence of various trash types, including leaves (leaf and bract), seed coats, hulls, and stems, contributes to this limitation. To address this problem, a set of mixtures with known amounts of both clean lint fibers and individual trash components (leaves, seed coats, hulls, stems, and sand/soil) was prepared and the visible/NIR spectral response was related to corresponding trash contents. Comparison of model performances revealed the feasibility of visible/NIR technique in the precise and quantitative determination of total trash, leaf trash, and non-leaf trash components.

Fiber length is one of the key properties of cotton and has important influences on yarn production and yarn quality. Various parameters have been developed to characterize cotton fiber length in the past decades. This study was carried out to investigate the effects of these parameters and their combinations on yarn properties. Linear regression models with different numbers of fiber length parameters and their combinations were developed for predicting ring and open-end (OE) spun yarns’ properties. The R2 and Mallows’ Cp plots of the models were compared for model selections. The results indicate that, for predicting a yarn property, a model usually involves more than three length parameters to achieve better prediction when considering the R2 and Cp values. This may be because only one single length parameter cannot sufficiently represent fiber length characteristics. The results also show that the variations in fiber length distributions play important roles in predicting yarn properties, such as strength and irregularity. The best prediction models for the properties of different yarns (ring, OE) include different combinations of length parameters. Not all yarn properties can be well predicted by linear regression models with length parameters: other fiber properties (strength, micronaire, etc.) need to be included to further improve the models.

To assess the potential of a rapid and low-cost method that can be used, away from the laboratory, in places such as ginning sites, near infrared (NIR) spectroscopy has been applied to perform the qualitative classification and quantitative prediction of various cotton quality indices, including cotton trash. It is well-known that current-in-use trash measuring devices only generate the trash amounts in some aspects, instead of the content for individual trash components. This difficulty comes from the complexity of the co-existence of different cotton-plant related trash types, for example, leaves (leaf and bract), seed coats, hulls, and stems. To this regard, mixtures of known trash components (e.g., leaves, seed coats, hulls, stems, and sand/soil) with cut lint fibers were prepared physically and then their NIR spectra were correlated with the respective trash contents. Then the NIR models were examined for the feasibility of determining the trash amounts in commercial and uncut cotton fibers. One of great challenges is how to calibrate or validate the results from NIR model transfer by appropriate sample standards.

The strength of cotton fibers is one of several important end-use characteristics. In routine programs, it has been mostly assessed by automation-oriented high volume instrument (HVI) system. An alternative method for cotton strength is near infrared (NIR) spectroscopy. Although previous NIR models have been promising in the prediction of HVI strength, in the latest research we have reported a much improved NIR model for HVI strength with a proposal of applying the pre-screening procedure to determine appropriate calibration samples. As a different and complementary approach, the present study was involved with partial least squares (PLS) analysis on mid-infrared (IR) spectra and cotton Stelometer strength. The model performance from the 1800 to 800 cm(-1) IR region was nearly equivalent to that from the full 3600 to 600 cm(-1) region. Considering the heterogeneous distribution of strength in native fibers and different sampling spotsbetween IR spectral and reference measurement, a 90% confidence interval was applied to exclude outlier samples from the calibration and validation sets. The recalibrated model revealed the feasibility of the IR technique for the quantitative determination of cotton Stelometer strength. Of most interest is that the capability of IR model for Stelometer strength is in good agreement with the NIR model for HVI strength.

Despite considerable efforts in developing curve-fitting protocols to evaluate the crystallinity index (CI) from X-ray diffraction (XRD) measurements, in its present state XRD can only provide a qualitative or semi-quantitative assessment of the amounts of crystalline or amorphous fraction in a sample. The greatest barrier to establishing quantitative XRD is the lack of appropriate cellulose standards, which are needed to calibrate the XRD measurements. In practice, samples with known CI are very difficult to prepare or determine. In a previous study, 14 we reported the development of a simple algorithm for determining fiber crystallinity information from Fourier transform infrared (FT-IR) spectroscopy. Hence, in this study we not only compared the fiber crystallinity information between FT-IR and XRD measurements, by developing a simple XRD algorithm in place of a time-consuming and subjective curve-fitting process, but we also suggested a direct way of determining cotton cellulose CI by calibrating XRD with the use of CIIR as references.

Cotton fiber quality and textile yarn quality can be affected by ginning processes and cotton cultivars. The overall goals of this study were to utilize a microgin to evaluate the effect of seed cotton cleaner and lint cleaner on fiber and yarn quality, and to benchmark FiberMax 1740 and Phyto Gen 370 against Deltapine 555 grown in Georgia. Six cleaning treatments in a microgin were arranged by varying seed cotton cleaners (stick machine, cylinder cleaner, and Trashmaster®) and one saw-type lint cleaner. Cotton fiber quality and cotton trash content were measured via High Volume Instrument (HVI), Advanced Fiber Information System (AFIS), and Shirley trash analyzer. Ring yarn quality was measured in terms of spinning efficiency, tensile strength and elongation, hairiness, defects, and waste. Fiber quality measured by HVI showed significant differences between the six cleaning treatments in both trash content and fiber length properties. Specifically, the saw-type lint cleaner was more effective in reducing trash content but was more likely to create short fiber content than the seed cotton cleaner. Deltapine 555 demonstrated lower upper half mean length and lower length uniformity than FiberMax 1740. A similar pattern was observed in AFIS data, although cleaning treatments only showed impact on total trash count, visible foreign matter, and neps. The three cotton cultivars exhibited significant differences in cotton length properties. Deltapine 555 had the highest short fiber content (by weight) and lowest length uniformity among the three cultivars. Results from Shirley analyzer revealed that the cleaning treatments without the saw-type lint cleaner had both higher visible and invisible trash content. As for the ring yarn quality, cotton lint with less cleaning processes exhibited lower defects, lower hairiness (irregular CV), but more waste than that with more cleaning processes. Given the waste can be cleaned during the carding process and easily manageable, the less ginning options could be beneficial to spinning process. Among the three cultivars, Phyto Gen 370 generated the highest quality yarn suggesting that Deltapine 555 can be replaced by other cultivars with improved yarn quality. This study sheds light on the effect of cleaning and cotton cultivar on lint fiber quality and the spinning performance of the fiber. The information could be useful to improve the profitability for cotton growers, ginners, and spinners alike.

Presence of trash in commercial cotton bales compromises their market values and further influences the end-use qualities. In order to ensure a fair trading, the USDAs Agricultural Marketing Service (AMS) has implemented the high volume instrument (HVI) readings as the universal quality indices to grade the cottons. Compare to HVIs geometric method that represents the trash portion only on a samples surface, traditional Shirley Analyzer (SA) is a gravimetric-based method and is being routinely utilized in the laboratories. With the increasing acceptance of HVI readings in domestic and international trading, there is a continued interest in understanding the conversion constant between two trash testing results from the customers, regulatory and trading organizations. Due to the complexity of trash type and size and also the nature of HVI and SA measurement, apparently bridging the two types of trash readings is a great challenge. This investigation addressed the need by proposing an innovative approach to establish the conversion constant, through sub-grouping the samples and then verifying the findings from independent NIR technique.

Traditionally, XRD had been used to study the crystalline structure of cotton celluloses. Despite considerable efforts in developing the curve-fitting protocol to evaluate the crystallinity index (CI), in its present state, XRD measurement can only provide a qualitative or semi-quantitative assessment of the amounts of crystalline and amorphous cellulosic components in a sample. The greatest barrier to establish quantitative XRD is the lack of appropriate cellulose standards needed to calibrate the measurements. In practical, samples with known CIs are very difficult to be prepared or determined. As an approach, we might assign the samples with reported CIs from FT-IR procedure, in which the threeband ratios were first calculated and then were converted into CIs within a large and diversified pool of cotton fibers. This study reports the development of simple XRD algorithm, over time-consuming and subjective curve-fitting process, for direct determination of cotton cellulose CI by correlating XRD with the FT-IR CI references.

Two-dimensional (2D) correlation analysis was applied to characterize the attenuated total reflection (ATR) spectral intensity fluctuations of immature and mature cotton fibers. Prior to 2D analysis, the spectra were leveled to zero at the peak intensity of 1800 cm(-1) and then were normalized at the peak intensity of 660 cm(-1) to subjectively correct the variations resulting from ATR sampling. Next, normalized spectra were subjected to principal component analysis (PCA), and two clusters of immature and mature fibers were confirmed on the basis of the first principal component (PC1) negative and positive scores, respectively. The normalized spectra clearly demonstrated the intensity increase or decrease of the bands ascribed to different C-O confirmations of primary alcohols in the 1050-950 cm(-1) region, which was not apparent from raw ATR spectra. The PC1 increasing-induced 2D correlation analysis revealed remarkable differences between the immature and mature fibers. Of interest were that: (1) Both intensity increase of two bands at 968 and 956 cm(-1) and the shifting of 968 cm(-1) in immature fibers to 956 cm(-1) in mature fibers, together with the intensity decreasing and shifting of the 1048 and 1042 cm(-1) bands, are the characteristics of cotton fiber development and maturation. (2) Intensities of most bands in the 1800-1200 cm(-1) region decreased with the fiber growth, suggesting they are from either noncellulosic components or CH and OH fractions in amorphous celluloses. (3) The reverse sequence of intensity variations of the bands in the 1100-1000 cm(-1) and 1000-900 cm(-1) region of asynchronous spectra indicated a different mechanism of compositional and structural changes in developing cotton fibers at different growth stages.

This paper examines changes in fiber properties, measured by the AFIS, as cotton stock progresses from opening to ring and open end spinning. As stock is processed in preparation for spinning, fiber length (measured by Lw, Ln, UQLw, UQLn or L5%) decreases in a systematic way while length variation (LwCV and LnCV) and short fiber content (SFCw and SFCn) show corresponding increases. Of the twenty AFIS fiber properties measured on the raw stock and at subsequently stages of processing prior to spinning, twelve of them are propagated in the sense that correlations between RS properties and those at subsequent stages are moderately to strongly positively correlated. For these properties, cottons, which have relatively ‘good’ (or ‘bad’) raw stock values, remain relatively ‘good’ (or ‘bad’) in processing. The relationship of fiber properties to yarn properties is generally not stronger if the properties are measured at later production stages rather than on the raw stock. The important predictors, regardless of stage, are variables that are propagated. For both ring and open end yarn, these are Fineness, SFC, fiber length, Length CV, MatRat or IFC, and Neps. Introduction