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2012 82: 1244 originally published online 7 March 2012Textile Research JournalMourad Krifa

and finenessber length distribution variability in cotton bale classification: Interactions among length, maturit

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Original article

Fiber length distribution variability incotton bale classification: Interactionsamong length, maturity and fineness

Mourad Krifa

Abstract

Proper classification and bale selection are prerequisites to success in a modern cotton spinning operation. Currently, forcrops where automatic High Volume Instrument (HVI) classification is the norm, fiber selection is done based on HVIdata which does not include adequate characterization of fiber length distribution. This research evaluates the effective-ness of current cotton fiber classification and selection procedures in controlling for variability in fiber length distribution

and presents a new approach to adequately clustering cotton bales into homogenous groups based on empirical lengthdistributions. The results show that using the common HVI parameters to group the bales produces categories withuncontrolled length distribution variability. Differences in distribution patterns appeared related to the potential for baleswith the same micronaire levels to differ significantly in maturity and thus in propensity to break.

Keywords

cotton classification, fiber selection, length distribution, cluster analysis, cotton variability

Cotton fiber traits are determined by complex interac-tions among genetic, environmental and processing

conditions. Because of these interactions, fiber proper-

ties vary significantly at multiple levels, that is, between

fields, between individual plants within fields, and even

within single plants and on the same seed.13 Thus, the

major challenge in cotton processing is to convert a

highly variable raw material into a uniform product

with quality that remains consistent over long produc-

tion cycles. To address this challenge, it is critical that

all the important fiber properties be adequately mea-

sured, and that accepted cotton bale classing based on

those measurements be made. Accordingly, cotton clas-

sing has historically had a vital impact not only on the

economics of cotton production and marketing, but

also on the efficiency and the ultimate profitability of

the textile manufacturing operation. In fact, decision

making in the cotton industry is often, if not always,

based on categorizing or clustering cotton bales into

relatively homogeneous quality groups using measured

fiber properties.

Cotton classing has considerably changed with prog-

ress in fiber quality measurement technology over sev-

eral decades. Early graders manually and visually

classified cotton according to grade, staple length andcharacter.4 The development of technology that

enabled automatic and rapid measurement of micro-

naire, color, then length, strength and trash, led to

the current classification system based on High

Volume Instruments or HVI.5 With the widespread

adoption of quality measurement and classification

technology and thus the availability of fiber informa-

tion, cotton bale selection and laydown arrangement

systems have evolved from the reliance on skills and

experience of spinners to highly sophisticated informa-

tion management and engineered decision-making

tools.6

In order to optimally use this information in fiber

selection, significant research efforts have been

Department of Textiles and Apparel, The University of Texas at Austin,

USA

Corresponding author:

Mourad Krifa, Department of Textiles and Apparel, The University of

Texas at Austin, 1 University Station A2700, Austin, Texas 78712, USA

Email: [email protected]

Textile Research Journal

82(12) 12441254

! The Author(s) 2012

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DOI: 10.1177/0040517512438124

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accomplished and various approaches have been devel-

oped over decades. For instance, the concept of

Engineered Fiber Selection or EFS was first developed

by Cotton Incorporated in the late seventies.79 El

Mogahzy10 proposed a linear programming approach

to optimize cotton purchase and planning decisions,

and to control warehouse inventory based on HVIdata. Later research went beyond purchase and inven-

tory management to integrate bale picking for laydown

mix selection.11 The various bale picking schemes in use

today are based on correctly and efficiently clustering

the population of bales into homogeneous groups with

respect to selected fiber characteristics. Those proper-

ties are limited to the major parameters available

through HVI testing (i.e. micronaire, length, strength,

and sometimes other characteristics such as color).12

Micronaire is typically considered as a primary crite-

rion in view of the major problems such as fabric barre

or color shade differences that inconsistent micronaire

can entail.13 Staple length is often the next essential

criterion when mixing laydowns; although in more gen-

eral terms, fiber properties may vary in importance

according to technology and end use. Since the estab-

lishment in 1991 of 100% classification by HVI in all

USDA classing offices, and over the following decade,

the widespread adoption of HVI by spinning mills the

world over,14 little has changed in the fundamentals of

the classing system. In an attempt to simplify the selec-

tion process by aggregating multiple criteria, complex

indices such as the fiber quality index (FQI), the spin-

ning consistency index (SCI), or the premium/discount

index (PDI) have been developed based on combina-tions of fiber properties and on regression models.1517

Those indices often depend on the range of bales

used to develop the equations and are not readily gen-

eralizable to characterize the complex multivariate

nature of cotton fiber quality. In addition, those indices

consist of linear combinations of the same HVI param-

eters discussed above and thus, fundamentally, they

convey the same set of information with the same

shortcomings.

In particular, despite intensive research and develop-

ment efforts, classing data still fails to include meaning-

ful and reliable measurements of some fiber properties

now at the forefront of concerns for spinners, namely,

neps18 and short fibers or more generally fiber length

distribution.19,20 To evaluate those properties, spinners

depend on measurement methods with testing speeds

not compatible with those of HVIs. The Advanced

Fiber Information System, or USTER AFIS, is one

such method where fibers are individualized using an

aeromechanical opener/separator, then individually

conveyed through a set of optical sensors which gener-

ate electrical signals proportional to fiber length and

other dimensions.21,22

Thus, the criteria used as input to control the blends

that feed the spinning mill are exclusively based on HVI

measurements, while the spinners quality concerns at

the output of the mixing line are increasingly geared

toward parameters that cannot be measured using

HVIs, namely neps, short fiber content or fiber length

distribution.19,23,24

More generally and beyond fiberlength, the intrinsic variability of all fiber properties

(within cottons/bales) is not taken into account

during fiber selection and laydown arrangement. In

practice, each bale is identified by the average values

of its HVI fiber characteristics. Information about

within-bale variability or about distributions of individ-

ual fiber characteristics is usually unavailable at the

laydown constitution stage.

The absence of this information from HVI classing

data means that critical fiber properties are not taken

into account in the fiber selection and laydown consti-

tution process. This may lead to unpredictable changes

in within-laydown variability which can be rather det-

rimental,25 unless the current procedures would allow

an indirect control of this variability. For instance, if

those properties can be predicted using HVI parame-

ters, the current fiber selection practices may have the

potential to control for their variability in the laydown.

However, this assumption remains to be verified

because it is unclear whether controlling micronaire,

length, length uniformity and bundle strength is suffi-

cient to control variability in properties such as fiber

length distribution. Indeed, fiber length distribution

patterns typically show complex features and are there-

fore difficult to classify using parameters such as meanvalues.19,24 The research reported in this paper aims at

testing the aforementioned assumption with a focus on

fiber length distribution. We examine the performance

of HVI parameters as criteria for clustering cottons into

homogenous distribution patterns and present a new

approach to classifying cotton bales using empirical dis-

tributions of fiber properties.

Materials and methods

A total of 172 commercial US upland cotton bales with

a wide range of fiber properties were included in this

study. To ensure the representativeness of the fiber

property measurements, each bale was divided into 10

layers and fiber samples were collected from each layer

for testing on HVI (High Volume Instrument, four rep-

lications for micronaire, four for color, and 10 for

length and strength) and AFIS (Advanced Fiber

Information System, three replications of 3000 fibers

each). All testing was done after proper conditioning

(65% RH, 21C). Testing instrument calibration was

checked daily using standard cottons and proper daily

maintenance and monitoring procedures ensured

Krifa 1245



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reliability of all instruments.26 Table 1 contains a sum-

mary of the properties of the selected cottons and

shows the wide range achieved in all variables. In addi-

tion to the summary parameters, empirical histograms

for length, fineness and maturity were retrieved from

the AFIS test. Averages per bale for all HVI and AFIS

parameters, as well as for length distribution histo-

grams were derived to fully characterize each bale.

Using the data collected, we evaluated bale classifica-

tion using clustering techniques based on three sets of

criteria:

1. The usual HVI parameters using average values per

bale; the parameters considered were micronaire,

Upper Half Mean Length (UHML), length unifor-

mity index, and bundle strength (this corresponds to

the set of criteria used in common practice).

2. AFIS length parameters using average values per

bale of the mean length by number (Ln), the 5th

length percentile, as well as dispersion parameters,

namely length CV% by number (LnCV%), and

short fiber content (SFCn%).

3. Empirical histograms of individual fiber length using

the average histogram per bale. Clustering the bales

based on the empirical distribution is considered the

reference ranking in this analysis since the criteria

used constitute the most complete information avail-

able about individual fiber properties, which should

yield the highest possible homogeneity within quality

groups.

With each of the sets of criteria above as dimensions,

we used the k-Means clustering algorithm available in

the STATISTICA Data Miner program27 to classify

the bales into homogenous groups by minimizing the

within-group distances in the respective criteria taken

simultaneously. The analysis was conducted using the

Generalized EM and K-Means Cluster Analysis tool

which allows for an a-priori unknown number of clus-

ters (k) and estimates k from the data using the v-foldcross-validation algorithm.27 Thus, the analysis gener-

ates an estimate of the number of clusters (k) from the

data, then partitions the observations into the k clusters

that minimize the distances or dissimilarities between

observations within clusters, and maximize the distance

between clusters. Each cluster is characterized by its

centroid (the vector of means for the continuous vari-

ables or criteria27). The dissimilarities between clusters

and between observations within clusters are estimated

using the squared Euclidean distance between centroids

or, respectively, between each observation and its clus-

ter centroid in the multidimensional space constituted

by the classification criteria. For instance, in the cluster

analysis using HVI properties as criteria, micronaire,

UHML, uniformity and strength constitute a four-

dimensional space.

The number of clusters was estimated based on the

empirical histogram data. With each set of classification

criteria, length distribution data of the bales partitioned

into groups was used to estimate a length distribution

centroid, and then squared Euclidean distance between

each bale and the corresponding cluster centroid was

calculated to estimate the dissimilarity in length distri-

bution patterns within clusters. Likewise, the centroid

distributions were used to calculate the distancesbetween clusters.

Results and discussions

As mentioned above, the classification of the bales

using the empirical histograms of the fiber length dis-

tributions was considered the reference ranking in this

analysis. To derive this classification, the frequencies

observed for each length bin were used as classification

criteria in the k-means cluster analysis. The number of

clusters estimated using the cross-validation algorithm

as discussed above was five. Thus, both HVI and AFIS

data were used to cluster the bales into five homoge-

nous quality groups. We first examine the reference

classification obtained with the individual fiber length

distributions, then discuss the clusters derived with the

commonly used HVI properties.

Bale classification using empirical histograms

of individual fiber length

Figure 1 depicts the observed probability density traces

of the individual bales classified into homogenous

Table 1. Main fiber properties of the selected bales (HVI and

AFIS measurements on raw cotton)

Fiber properties Min. Max. Average

HVI

Micronaire 2.3 5.1 4.0

Upper Half Mean Length(UHML, mm)

24.2 31.4 28.3

Length uniformity (%) 78.0 85.1 81.8

Strength (g/tex) 21.7 35.5 29.1

AFIS

Mean length by number (Ln, mm) 14.5 22.4 18.8

Short Fiber Content by number

(SFCn, %)

19.7 45.1 29.5

Upper Quartile Length by weight

(UQLw, mm)

28.3 37.8 33.6

Maturity ratio (MR) 0.73 0.95 0.86

Fineness (mtex) 142 184 163

1246 Textile Research Journal 82(12)



5/12

groups using the k-means cluster analysis with length

histograms as classification criteria. The density traces

for the individual bales are shown in fine gray lines. The

density trace shown in bold broken line represents the

centroid for the corresponding cluster. The broken ver-

tical line at x 30 mm was added to emphasize the

relative positioning of the five clusters on the length

axis.

The plots generated for the five groups show distinct

patterns across clusters with relatively homogenous dis-

tribution shapes within clusters. Therefore, using the

k-means clustering approach and the observed length

distribution data, it was possible to automatically and

quickly classify a sizeable number of cotton bales into

groups with homogenous distribution patterns.

Observed cotton fiber length distribution patterns

result from a combination of intrinsic (genetic and envi-

ronmental) and processing factors. Mechanical damage

in cotton fiber processing, both shifts the fiber length

distribution and alters its shape. As a result of these

interactions, the distributions exhibit complex, often

bimodal, patterns which depend on the degree of fiber

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

Probab

ility

0 10 20 30 40 50 60

Length (mm)

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

Probability

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0 10 20 30 40 50 60

Probability

Length (mm)

(c)

(b)

(d)(a)

(e)

Individual bales

Cluster centroids

X = 30 mm

Figure 1. Probability density traces of the 172 bales categorized into five clusters using empirical length histograms.

Krifa 1247



6/12

damage undergone by the cotton.19,24,28 With such

complex shapes, the summary statistics typically used

to describe fiber length (means, percentiles, short fiber

content. . .) are not representative of the distribution,

and cannot be used to classify cottons into groups

with similar distribution patterns. Thus, the common

way to compare and classify samples with varied

degrees of fiber damage into groups with similar distri-

bution shapes is to visually examine the empirical

length histograms. However, this can only be done

with a limited number of samples and cannot be prac-

tically applied when dealing with hundreds or thou-

sands of bales to constitute laydowns, or when

analyzing hundreds of samples to select genotypes in

breeding programs. The approach we show above over-

comes this problem and allows the automatic and quick

classification of a large number of samples into groups

with similar distribution patterns.

The distribution groups, shown in Figure 1, differ in

both shape and position on the length axis, which, as

indicated above, corresponds to both intrinsic and pro-

cess-related sources of variability. We have sorted the

five groups on Figure 1 (from A to E) by order of

increasing fiber damage according to the characteristic

distribution shapes.19 In particular, clusters A, B, and C

(Figure 1) show a clear bimodal shape with a peak in

the range of very short fibers (x< 5 mm), and another

distinct peak in the length categories between 20 and

30 mm. This pattern is characteristic of an intermediate

stage of fiber breakage process typically seen in raw

cotton that underwent some degree of mechanical

aggressiveness in ginning and lint cleaning.19,28

Clusters D and E (Figure 1) on the other hand, still

exhibit the peak at x


7/12

significantly in HVI fiber properties, with exception

made of the fact that clusters A and B have equal

fiber strength values. Those two clusters, seen above

as having a distribution pattern characteristic of low-

intermediate degree of fiber damage, appear to be con-stituted of the strongest bales (average strength is

30.2 g/tex), and are characterized by the two highest

micronaire levels, respectively 4.2 and 4.6 (Figure 2).

At the other end of the spectrum, cluster E shows the

lowest micronaire (2.9) and strength (24.6 g/tex), and as

discussed above, the length distribution pattern with

the most advanced fiber damage level. Overall, the dif-

ferent distribution shapes seen across the five groups of

bales correspond to different degrees of damage that

can be caused by variations in upstream processing

conditions (mechanical aggressiveness in ginning and

lint cleaning) or variations in the cottons propensity

to break, which was shown to depend on fiber maturity

and strength.19,24 For instance, the distribution shape

seen in cluster E (Figure 1) is distinctive of immature-

weak cotton that reached a degree of extensive fiber

damage even at the bale stage. Therefore, the k-means

clustering approach using observed length distributions

allowed the classification of the tested cotton bale pop-

ulation into homogenous groups. The various distribu-

tion patterns observed for those groups appear to be

representative of varying degrees of fiber damage.

Because of the close relationship between fiber

damage and maturity and strength,19,24 clustering the

cotton bales into homogenous groups according to

length distribution patterns shows the potential of

effectively discriminating between cottons with differing

micronaire and strength levels.

Parametric classification using HVI and AFIS statistics

In addition to the classification discussed above, both

HVI and AFIS parameters were used to cluster the

bales into five homogenous quality groups. HVI classi-

fication constituted bale clusters based on micronaire,

UHML, length uniformity index, and bundle strength.

AFIS classification was based on four length parame-

ters by number (mean length, length CV%, length 5th

percentile, and short fiber content19). The clustering

technique was similar to above; the analysis constituted

five groups that minimized the within-group and max-

imized the between-group variability in the selected

classification criteria.

Table 2 summarizes mean values of micronaire,

UHML (mm), length uniformity index (%) and

strength (g/tex) for the five bale groupings constituted

based on the four HVI criteria. The clusters are shown

in Table 2 by increasing micronaire value. The cluster

mean values and associated standard deviations

showed significant differences between clusters in each

of the four HVI fiber properties.

Table 3. Cluster means for AFIS mean length by number (Ln), length CV%, and length 5 th percentile

(Pc5.0)

Cluster Ln (mm) LnCV(%) SFCn(%)

Pc5.0

(mm)

Number

of cases

Percentage

(%)

AF-1 16.09 56.0 38.0 30.2 18 10.5

AF-2 17.69 52.6 31.5 31.5 25 14.5

AF-3 18.55 55.3 31.8 34.3 56 32.6

AF-4 19.77 47.6 23.9 32.8 31 18.0

AF-5 20.45 51.6 26.0 35.9 42 24.4

Table 2. Cluster means for micronaire, staple length (mm), length uniformity index (%), and bundle strength

(g/tex)

Cluster Micronaire

UHM length

(mm)

Uniformity

index (%)

Strength

(g/tex)

Number

of bales

Percentage

(%)

HVI-1 2.7 26.8 79.3 24.4 9 5.2

HVI-2 3.5 28.8 81.3 27.8 42 24.4HVI-3 3.8 25.4 80.5 26.0 13 7.6

HVI-4 4.1 29.9 82.9 30.9 52 30.2

HVI-5 4.5 27.6 82.0 29.3 56 32.6

Krifa 1249



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Table 3 summarizes the data observed for each of

the five clusters obtained with the four AFIS length

parameters. Overall, the clusters exhibit sizably differ-

ent mean values in each criterion with the exception

made of the comparable short fiber content in the

groups labeled AF-2 and AF-3.

We now examine the three classifications obtained

above and compare the performance of each set of cri-

teria in adequately grouping the bales, that is, in pro-

ducing distinct and homogenous clusters that minimize

the within-group variability and maximize the between-

group variability.

Classification performance

As mentioned in the methods section, the dissimilarity

of the distribution patterns within clusters was esti-

mated using the squared Euclidean distances between

the length distributions of individual bales and the cor-

responding cluster centroid. Respectively, the dissimi-

larity of the distribution patterns between clusters was

estimated using the squared Euclidean distances

between cluster centroids. This was done for each of

the three classifications discussed above, namely, the

classification based on the empirical histograms and

the two parametric classifications based on HVI and

AFIS parameters. The squared Euclidean distance

results were used to calculate the ratio of total

between-cluster over the total within-cluster variability

of distribution patterns for each of the three classifica-

tions. Based on the discussion above, this ratio mea-

sures the classification performance because the higher

it is, the more distinct and homogenous the clusters are.

Figure 3 shows the ratios so obtained for the three

classifications.

It is apparent that as expected, the criteria based on

the empirical histograms produce the classification with

the highest ratio. The classification based on AFIS

parameters produces the middle ratio while the one

based on HVI parameters produces the lowest ratio.

This indicates that as we move from the empirical his-

togram to the parameters used in the industry to clas-

sify cotton bales, the probability to obtain balecategories with heterogeneous distribution patterns

increases.

To scrutinize this observation in more depth, we

examine the detail of the distances obtained for the

individual bales partitioned into the five clusters using

HVI parameters. The results of this analysis are shown

on Figure 4 where both individual values (upper plot)

and standard deviations (lower plot) of the squared

Euclidean distances are plotted against the five HVI

groups (HVI-1 to -5).

The results in Figure 4 show a high dispersion of the

Euclidean distance for the clusters with high micronaire

levels (cluster HVI-5 and to some extent cluster HVI-4

which has two bales with extreme length distribution

dissimilarity in comparison to the cluster centroid).

These results indicate that the clustering of the bales

based on HVI parameters resulted in some groups of

bales with relatively heterogeneous length distribution

patterns. The heterogeneity within groupings appears

to be higher for the categories with high micronaire

levels.

The practical implication of the observation made

above is that in constituting the spinning laydowns

Figure 4. Variability chart for length distribution pattern dis-similarity within HVI clusters (squared Euclidean distance).

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Empirical histogramAFIS (Ln)HVI

DistanceRatio(between/withinclusters)

Figure 3. Between-/within-cluster ratio of Euclidian distance

(distribution dissimilarity) based on the three sets of criteria.




9/12

based solely on HVI data, bales with dissimilar length

distribution patterns could be substituted for each

other (being from the same category) and could there-

fore result in variability between laydowns that remains

unaccounted for. In the particular case of the popula-

tion we tested, bales within the 4.1 and 4.5 micronaire

categories (see Table 2) could be considered essentially

identical because of having similar HVI properties,

but may represent significant variability in length

distribution. An illustrative example of this variability

in each of the two groups of bales is shown in Figure 5.

For each group, we plotted length distribution den-

sity traces for two bales showing high Euclidean dis-

tance from the clusters centroid (shown in broken bold

line).

In both cases shown in Figure 5, the distribution

patterns are different and exhibit distinct shape features

that typically correspond to cottons with different

degrees of fiber damage, (i.e. different propensities to

break and/or processing history).19 Those bales were

classified in different clusters when using the empirical

histograms as criteria but were attributed to the same

groups when HVI parameters were used as criteria.

This result is indicative of the fact that the four majorHVI fiber properties are not sufficient to predict length

distribution patterns since cotton bales having similar

HVI measurements may have very distinct length

distributions.

The bales depicted in Figure 5 are just examples

among about 39 bales (or 23% of all tested bales)

that appeared to be misclassified based on HVI data.

In order to identify the factors impacting this misclas-

sification, we examined the combinations of fiber prop-

erties of pairs of bales that had comparable HVI

properties but exhibited significantly different distribu-

tion patterns (similar to the cases illustrated in

Figure 5). A particular emphasis was placed on those

fiber characteristics that are known to impact the cot-

tons propensity to break and thus length distribution

pattern, which include fiber maturity.19,24

Figure 6 depicts the relationship between micronaire

and the two fiber maturity parameters measured using

the AFIS (i.e. the immature fiber content (IFC %) and

maturity ratio) for the bale clusters exhibiting misclas-

sified bales with HVI. The pairs of bales with similar

HVI properties but distinct length distribution patterns

are represented using two different point markers

depending on the distributions positioning relative to

the respective clusters centroid. Bales with distribu-tions at the left of the centroid, that is those having

degraded length with relatively extensive fiber damage

(e.g. bales #30 and #12 in Figure 5), are shown in bold

dark dots. Bales with distribution at the right of the

centroid (i.e. with a lower degree of fiber damage),

are shown with asterisk point markers. Two curves

(dotted lines) were added to the scatter plots to outline

each of the two groups of bales. The rest of the bales

are shown in gray circle markers.

It can be seen from Figure 6 that the two groups

correspond to pairs of bales having two levels of matu-

rity for the same micronaire values. The samples with

degraded length distributions are those that have a

higher IFC% and a lower maturity ratio, while the

bales with a lower degree of damage have a lower

IFC% and a higher maturity ratio for the same micro-

naire levels. It appears therefore that the misclassifica-

tion of some of the bales based on HVI parameters is

related to the fact that bales having the same micro-

naire may correspond to different maturity levels, given

the nature of micronaire as a complex measure of both

maturity and fineness. Thus bales having the same

micronaire (i.e. classified in the same HVI categories),

0

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0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

Probability

Bale#12

Bale#121

Cluster centroid

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0 20 40 60

Probability

Length (mm)

Bale#30

Bale#75

Cluster centroid

(a)

(b)

Figure 5. Length distribution pattern variability within clusters

HVI-4 (a) and HVI-5 (b).

Krifa 1251



10/12

but having different maturity levels will ultimately lead

to the high variability in fiber length distribution as

illustrated in Figure 5.

Conclusions

In order to test the effectiveness of current cotton fiber

classification and selection procedures in controlling for

variability in fiber length distribution, k-means cluster

analysis was used to classify a broad range of 172 com-

mercial cotton bales into homogenous quality groups

based on three sets of classification criteria. The first set

of criteria consisted of the major HVI parameters com-

monly used in commercial classification and in fiber

selection in spinning operations; the parameters consid-

ered were micronaire, UHML (mm), length uniformity

index (%) and bundle strength (g/tex). Another set of

criteria consisted of fiber length parameters provided by

the AFIS. In addition to those parametric criteria, a

new approach based on empirical histograms of fiber

length distribution was also used. Using this new

approach, it was possible to quickly classify a sizable

number of cotton bales into groups with homogenous

length distribution patterns which appeared representa-

tive of varying degrees of fiber damage. Because of the

impact of fiber maturity and strength on the propensity

to break, clustering the bales based on length distribu-

tion patterns resulted in groups with different micro-

naire and strength levels.

A comparative analysis of the three approaches

revealed that when classification was done using HVI

properties only, approximately 23% of the bales

appeared misclassified, (i.e. cottons with significantly

different length distributions were attributed to the

5

6

7

8

9

10

11

12

13

ImmatureFiberContent(IFC%)

Bales with length distributions left of centroid

Bales with length distributions right of centroid

3.6 3.8 4.0 4.2 4.4 4.6 4.8 5.0

Micronaire

0.78

0.80

0.82

0.84

0.86

0.88

0.90

0.92

0.94

0.96

Maturityratio

(b)

(a)

Figure 6. Relationship between micronaire and maturity parameters; (a) Immature Fiber Content (IFC%), and (b) Maturity ratio.

Bales showing extreme length distribution patterns within clusters are shown in distinct point markers.




11/12

same categories), which could result in undesirable lay-

down variability in critical properties such as short fiber

content. The examination of the interactions among

fiber properties indicated that the misclassification of

those bales based on HVI parameters is related to the

nature of micronaire as a complex measure of both

maturity and fineness. Therefore, bales having thesame micronaire may correspond to different maturity

levels, and given the link between maturity and fiber

damage, this can result in significant variability in

fiber length distribution. This result underscores the

need for, and the potential usefulness of high volume

measurement tools that could provide separate deter-

mination of maturity and fineness. The availability of

such methods could prevent bale misclassifications

resulting from misinterpretation of micronaire values

and thus ensure better control of the variability in

fiber length distribution. This research continues in

order to quantify the potential impact of this variability

on processing performance and, ultimately, on yarn

quality, as well as to identify combinations of classifi-

cation criteria that could help minimize variability

within bale categories.

Acknowledgements

This research was funded, in part, by the Food and Fibers

Research Grant Program administered by the Texas

Department of Agriculture (grant number FF-d1011-7) and

by Cotton Inc., Texas State Support (grant number 11

813TX).

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