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Thursday, September 13, 2018

Cotton stickiness: Origin Measuring and controlling


  Cotton stickiness: 

Origin Measuring and controlling




In the era of global market & economy, cotton plays a major role. Global competition in the production and consumption of cotton fibre combined with technological advancements in yarn manufacturing has accelerated efforts to enhance cotton fibre quality. Fibre properties influence both productivity and quality in the spinning process. Cotton stickiness caused by excess sugars on the lint, either from the plant itself or from insects, is a very serious problem for the textile industry -- for cotton growers, cotton ginners, and spinners. It affects the processing efficiency as well as the quality of the product. This article deals with the causes, effects on different departments, economics, determination techniques and remedies of cotton stickiness.
                                                                  Cotton stickiness
The contaminants are mainly sugar deposits produced either by the cotton plant itself (physiological sugars) or by feeding insects (entomological sugars), with the latter being the most common source of stickiness. The main honeydew-producing insects that infest cotton plants are cotton whitefly, Bemisia Tabaci (Gennadius) and the cotton Aphid Aphis Gossypii (Glover).
Figure 1.Cotton Aphid
Whiteflies and aphids are both sap-sucking insects that feed by inserting their long and slim stylets into the leaf tissues. The sap is digested and the excreta discharged as honeydew droplets. The honeydew attaches itself to the leaves and the fibres of opened bolls. The presence of these sugars on the lint reveals that the contamination is coming, at least partially, from insect honeydew. A high percentage of melezitose along with a low percentage of trehalulose reveals the presence of aphid honeydew. When both melezitose and trehalulose are present and trehalulose is dominant, whitefly honeydew contamination is indicated. The other sugars are generally found on both non-contaminated and honeydew-contaminated cottons. It was reported that glucose and fructose contained in the honeydew are synthesised from sucrose by the insect.
Figure 2. White Fly



 







Stickiness is a kind of contamination of cotton fibre caused by infestation of sucking insect pests. The most common insects causing cotton stickiness are whitefly (Bemisia argentifolli (whitefly) and aphids (Aphis gossypii). Those insects excrete a concentrated solution of sugars (honeydew) that melts and spreads through the fibres, damaging the quality of final product and industrial machinery (HEQUET & ABIDI, 2002). Cotton stickiness causes losses that reach millions of dollars to the world textile industry every year. There are several methods for detecting and quantifying honeydew, but all of them are time consuming and labour demanding, and sometimes they are unreliable depending on the sugar present in the sample. Detecting and quantifying cotton stickiness is difficult because the chemical structure of the honeydew’ sugars (melezitose and trahalose) is very similar to the sugars that occur naturally in the fibres (glucose and fructose) and to cellulose, which is the basic structure of cotton fibre (Table I).

Detecting cotton stickiness using images is an interesting option because the test can be applied to a large number of samples without increasing the costs or spending time and resources for laboratorial analysis. The option was made for Near Infrared Hyperspectral Imaging (NIR-HSI) considering that it is an analytical technique able to discriminate materials based on chemical composition and that can add both spatial and spectral data from a sample since each pixel from the image is connected with an individual spectrum. 




Table I - Chemical structure of honeydew (melezitose and trehalose), physiological sugars (glucose and fructose) and cellulose.






                                       

MATERIAL AND METHODS

Sample Preparation
The following sugars were dissolved in water to simulate the insects excrements: Sigma Aldrich Trehalose (> 98,5%), Melezitose (> 99,0%), Glucose (> 99,5%), Sucrose (> 99,5%), and Merck Fructose (> 99,0%). The concentration of each sugar was calculated by mass as described in Hequet & Abidi (2006). In order to obtain treatments contrasting for both sugars composition and total sugar concentration, the 5 types of sugars were arranged in 26 combinations. The combinations were planned providing that every solution had a different concentration of each sugar (i.e., there was not two sugars with the same concentration in the same solution), and that the total sugar content varied from 0,075% to 0,833% (m/m). An additional treatment consisted in the sugars applied in the crystal form: 50 mg of each sugar were mixed together in a mortar and pistil without dissolving in water. Control treatments were prepared applying water replacing the sugar solutions and with cotton samples without any treatment.

Regular cotton was used to prepare 2 g samples flatten to 15 x 5 cm. Each cotton sample was contaminated with 40 drops of 5 µL of each sugar solution. Ten samples were prepared repeating the solution 0.356% (m/m) in order to measure the repeatability of the method.

                                       
Image acquisition and data processing
Hyperspectral image analysis has as its main objective to acquire pictures containing specific information about the composing compounds on the surface measured (Amigo et al., 2015). Using a correlation of individual spectrum for each pixel requires very specific parameter settings in order to collect the best set of information. NIR-HSI images were acquired using a Specim® hyperspectral camera (SisuChema SWNIR, Finland) on reflection mode with
50 mm lens, 1.6 ms exposition time, and 6.25 nm of spectral resolution in wavelength region between 1000 and 2500 nm at a spatial resolution of 10 nm with 150 µm2 pixel size. The cotton samples were analysed over a Teflon plate, which presents no activity in NIR and can be easily removed afterwards. Data was recorded using the software ChemaDAQ and processed with the hyperspectral image analyzer software Evince 2.7.5 (Umbio, Umea, Sweden), which was used to avoid further requirements on programming while executing image spectral and spatial processing.

 









Figure 1 - Schematic representation of the experimental workflow on synthetic sticky cotton samples imaging and statistical analysis. 1 – cotton samples were prepared for contamination with a combination of sugars at controlled concentrations; 2 – 40 drops of 5 µL sugar solutions were applied in the cotton sample; 3 and 4 - the samples were oven dried and stored in desiccator; 5 – samples were acclimated to standard temperature and humidity; 6 – samples placed in Teflon plate to acquire data; 7 - NIR-HSI image was acquired; 8 - spatial and spectral processing was performed; 9 – Principal Components Analysis clustered pixels of clean cotton and contaminated spots; 10 - medium spectra was recorded, and 11 – Partial Least Square regression was calculated.




For each image the following data processing was done: (1) standard normal variate (SNV) was applied to lower the light scatter effect; (2) complete background removal to keep only information about the sample; (3) variables between 900 and 993.75 nm and between 2356.25 and 2500nm were excluded because they are spectrum noisy that hinder the visualization of the spots contaminated with sugars.

Principal Component Analysis (PCA) is automatically generated when editing data in Evince allowing a statistical evaluation about differentiation between compounds. Because of this, the resulting PCA for each sample can be described in terms of different components (PCs) that maximize the clustering capacity of the data set. Once the Principal Components are established, pixels belonging to the sugar spots can have their medium spectra recorded and quantitative information inferred from NIR can be achieved. The medium spectra from the experimental planning were exported to the multivariate statistical software The Unscrumbler X.3 (CAMO, Oslo, Norway) to create a Partial Least Square linear regression for total sugar percentage. The complete experimental workflow is summarized in Figure 1.


RESULTS AND DISCUSSION

Visualization of sugar spots
The illustration of the results (Figure 2) is presented in the samples with minimum (A), medium (B), and maximum (C) sugar content and in the sample contaminated with sugar in crystal form (D). The sugar spots applied as solution cannot be visualized in the contaminated cotton (in the left) but the sugar in crystal form had a dark colour and were visible. After data analysis (in the right) the sugar spots become clear and the contamination spots are easily visualised. It was confirmed that the contamination made with sugar crystals are equally identified by the technique.









Figure 2 - Cotton samples with minimum (A), medium (B), and maximum (C) sugar content contaminated with sugar in crystal form (D) before (left) and after image analysis.



Exploratory Analysis
Exploratory data analysis on NIR hyperspectral images displays PCAs profiles confirms the ability to discriminate between clean and contaminated pixels and samples contaminated by both sugar solutions and sugar crystals are equally detected (Figure 3). The discriminating power of the method increased as the sugar solution was more concentrated (comparing Figure 3B to 3K) but it still efficient even with very low sugar concentration.

In addition to it is possible to observe which the Explained Variance (EV) and number of Principal Components are considerable different from what is usually seen in NIR- HSI analysis. The number of components mainly increases with declining in total sugar percentage, as can be noticed comparing, again, PCAs B and K in Figure 4, represented by PC 6 vs. PC 4 and PC 3 vs. PC 2, respectively. Although the results expose high number of PCs, the sum of their EVs (eg. EVPC1 to EVPC6 , in B) is ≥ 99.0%.







Figure 3 - PCAs’ scores maps by category for commercial cotton samples contaminated with sugars. Green dots are sugar-contaminated pixels and blue dots are clean cotton; A - sample contaminated with sugar crystals; B to K - cotton sample contaminated with sugar solutions increasing from 0.197 (B) to 0.833 % (m/m).


A clustering analysis with Principal Components Analysis considering the 41 samples with varying levels of sugar contamination illustrate the capacity to discriminate stickiness in cotton (Figure 4). For illustration, it was assumed that the threshold contamination to be considered sticky cotton was 0.3%, as suggested by Abidi & Hequet (2006). The analysis found a cluster with clean cotton (blue squares in the top left), a cluster with low sugar contamination (red circles in the centre), and a cluster with high sugar contamination (green triangles in the bottom right). There were minor overlapping and the pattern agrees between sugar concentration and calculated values.



Causes for stickiness
 
The two main causes of sugars or honey dew becoming sticky are heat and moisture.
During yarn formation the cotton fibres are exposed to friction forces that elevate the temperature of some mechanical parts, which affects the temperature-dependent properties of the sugars present. If one or more of the sugars melt, stickiness results.
Obviously moisture will cause sugars to change from a crystalline state (non-sticky) to an amorphous state (sticky). In particular, the relative humidity in the manufacturing environment may affect the moisture-dependent properties of the sugars present.
Effect of stickiness on different processes
 
Effect of stickiness on ginning

Sticky cotton tends to clog/choke the ginning machines. Stickiness reduces roller gin production by 10 to 15 pounds of lint per hour. It also causes additional financial losses due to frequent replacement of blades/saws.
Effect of stickiness on spinning

Stickiness will cause lint to stick to card clothing and draft rollers in subsequent processes.
 
Figure 3. Sticky deposits on the draw frame creel
Sticky fibres even if they pass through the spinning back process will create extra centrifugal forces during ballooning, causing the yarn to break.
In the OE frames stickiness will clog the turbine. No matter how we look at stickiness it will reduce efficiency and production to a considerable extent during spinning.
Low humidity will dry the sugars and they will cease to be sticky. If however, humidity is allowed to rise, sugars will become sticky again.


Effect of stickiness on weaving
 

Stickiness has minimal effect on warp as it is usually sized and the sugar present gets either dissolved in the hot size mix or is covered by it. However, in weft, sugar starts building up in shuttle, gripper or air jet and weaving efficiency drops to a level where it becomes uneconomic to continue weaving. Frequent cleaning of wefts passage would, therefore, be required. This is time consuming and expensive
Economics of stickiness
 

To growers, stickiness means higher costs for insect control and reduced cotton marketability. Cotton price is reduced for stickiness by the market at a rate proportional to the perception of risk.
To ginners, stickiness may mean special handling and processing requirements. Sticky cotton can reduce cotton gin output (in bales/hr) by up to 25%.
At the textile mill, stickiness means reduced processing efficiency, lower yarn quality, excessive wear and increased maintenance of machinery may occur even with slightly sticky cotton. For everyone concerned, stickiness means reduced profitability.
Stickiness detection and measurement


Figure 4. Sticky deposits on the draw frame drafting zone
‘Stickiness’ is the physical process of contaminated lint adhering to equipment
The degree of stickiness depends on chemical identity, quantity, and distribution of the sugars, the ambient conditions during processing—especially humidity —and the machinery itself. Stickiness is therefore difficult to measure. Nonetheless, methods for measuring sugars on fibre have been and are being developed. These measurements may be correlated with sticking of contaminated lint to moving machine parts.  The physical and chemical attributes of the lint and sugars that are correlated with stickiness have been measured in many ways, each with differing efficiency and precision.
Some of the measurement methods are given below:
 
Reducing sugar method
High performance liquid chromatography
Minicard method
Sticky cotton thermo detector
High speed stickiness detector
Fibre contamination tester

Solutions to eradicate stickiness
 
During cultivation:
 
The most efficient way now to prevent stickiness is by managing sugar sources in the field. These honeydew-producing insects may be managed by avoiding conditions leading to outbreaks, carefully sampling pest populations, and using effective insecticides when populations reach predetermined thresholds. The risk of having excessive plant sugars can be minimized by harvesting mature seed cotton.
In ginning:
If stickiness is a problem while ginning, the ginning rate of honeydew contaminated cotton can be increased by increasing the heat of the drying towers to reduce humidity.
At the textile mill:  
At the textile mill, stickiness may be managed by blending bales and by reducing humidity during carding. A lubricant in fog form may be introduced at the end of the hopper conveyor, and card crush rolls may be sprayed sparingly with a lubricant to minimize sticking.


Stickiness is a complex, three-component interaction that involves the source sugars, harvested seed cotton, and processing equipment. Stickiness caused by honeydew contamination has been reported to cause residue build-up on textile machinery, which may cause subsequent irregularities or yarn breakage. The complexity of this interaction indicates the need for an integrated solution that includes prevention, in-field mitigation, and processing adjustments.

Sugar content test in Cotton

Cotton fibre with normal sugar content might not affect the spinning process. If the sugar content is too high, it might cause storage mildew and metamorphism. During the spinning process, it might also cause twining and breakage, and lower yarn quality and production efficiency. Spectrophotometry is used as a quantitative determination method to detect the total sugar content, and 3,5-dihydroxytoluene-sulfuric acid solution is used as the colour developer. This International Standard supplies the basic information for sugar content of cotton fibres.

Benedict Test

Benedict’s solution (deep-blue alkaline solution) is also
used to test for the presence of the aldehyde functional
group, –CHO. The substance to be tested is heated with
Benedict’s solution; formation of a brick-red precipitate
indicates presence of the aldehyde group. Since simple
sugars like glucose give a positive test, this solution is
also used to test for the presence of glucose in urine, a
symptom of diabetes. One liter of Benedict’s solution
contains 173 grams of sodium citrate, 100 grams of
sodium carbonate, and 17.3 grams of cupric sulfate
pentahydrate. It reacts chemically like Fehling’s
solution: The cupric ion (complexed with citrate ions)

is reduced to cuprous ion by the aldehyde group (which
is oxidized), and precipitates as red cuprous oxide,
Cu2O.
As shown above, Fehling and Benedict tests are based
on the same principle. In the Benedict test the sugar
content is estimated from the color of the solution as
follows:
Blue Very low
Green Low
Yellow Moderate
Orange to red High
A large number of variations of this procedure are
in use in different parts of the world. One consists
of spraying the Benedict solution on the surface
of a cotton sample. The sample is then placed in a
microwave oven for 30 seconds to 2 minutes to allow
the reaction. The evaluation of the color change is
subjective as for the Benedict test.
Another variation is the Clinitest method in which
Clinitest tablets (commercially available to test for
glucose in urine) are used as a source of the cupric ion.

 

Bremen Honeydew Test

The “bremen honeydew test” (Sisman and Shenek
1984) is one of the numerous complexing methods. The
complexing methods involve the formation of colored
compounds from the treatment of the water extract
of cotton with strong acid to hydrolyze the sugars.
The resulting monosaccharides are then reacted with
an aromatic compound to form colored complexes.
There are a number of aromatic molecules suitable for
carrying out this reaction.
In the bremen honeydew test, a water extract of cotton
is treated with 3,5-dihydroxytoluene in concentrated
sulfuric acid. The intensity of the red complex formed
is proportional to the amount of sugars or hydrolysable
carbohydrates present.
A common problem with the complexing methods is
the color of the water extract of cotton. The colored
extracts could interfere with the readings and for some
of them the nonspecificity of the chemical reaction
could bias the results.
To our knowledge none of the complexing methods are
in use to any extent today.

Minicard Test

The minicard test (figure 2) is a mechanical method for
rating cotton stickiness based on processing the cotton
through a miniature carding machine and assessing
the degree of stickiness on the delivery rolls as the
resulting web passes through. The rating system is
based primarily on the tendency of the fiber web to
wrap around the delivery rolls as a result of a sticky
spot adhering to the rolls. Higher numbers of sticky
spots on the web result in a higher number of wraps,
and the cotton is then rated in one of four categories:
0 no stickiness
1 light stickiness
2 moderate stickiness
3 heavy stickiness
Requirements for performance of the test include
maintaining the relative humidity between 55 and 65
percent and regularly cleaning the delivery rolls to
prevent sugar buildup. The results of the minicard test
are widely believed to correlate well with stickiness
in the mill due to an essentially identical carding
process, but the method is time-consuming and requires
relatively expensive equipment. As a result, the
method is used primarily as a reference method with
which faster, simpler, and less expensive methods for
measuring stickiness may be calibrated.

HIGH SPEED STICKINESS DETECTOR:
The High Speed Stickiness Detector (H2SD) is a quicker, automatic version of the thermodetector. The cotton sample is pressed between a heated (54°C for 30 sec.) and an unheated pressure plate. Sticky points are counted and point size distribution determined by image-processing computer software. Plates are automatically cleaned between samples. The H2SD is able to analyze a sample in 30 seconds.


Conclusion

Stickiness is a complex, three-component interaction that involves the source sugars, harvested seed cotton, and processing equipment. Stickiness caused by honeydew contamination has been reported to cause residue build-up on textile machinery, which may cause subsequent irregularities or yarn breakage. The complexity of this interaction indicates the need for an integrated solution that includes prevention, in-field mitigation, and processing adjustments

Cotton Stickiness Test









Ref : USDA article

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