 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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