New Prediction Equations for Yarn Strength of Egyptian Cotton
Utilizing HVI Spectrum Data.
M.G. Sief, H.M. Arafa, and R.M. Hassan
Cotton Research Institute, Agricultural Research Center, Giza, Egypt.
Abstract
This investigation was conducted on ten Egyptian cotton varieties and
promising
crosses
namely,
Giza
80,
Giza
90,
(Giza
90×Aust.)
and
[G83×(G75X5844)]×G80 (Upper Egypt long staple cottons), as well as Giza 86 and
(10229×Giza 89) (Delta long staple cottons), Giza 88, Giza 92, Giza 93 (Giza77×Ps6)
and [G84×(G70×51B)]×P62 (extra long staple cottons), to develop prediction
equations for skein strength of 40’s and 60’s carded yarns by subjecting HVI testing
data (measurements of cotton quality properties and SCI) to multiple regression
analysis. The developed equations were applied to HVI spectrum data to test the
accuracy and reliability of the obtained predicted skein strength values. The predicted
values of skein strength matched very well the corresponding determined ones.
Regression and correlation between skein strength and SCI were studied.
Introduction
Classical low volume instruments (LVI) used for characterization of cotton
fiber as Fibrograph, Stelometer, Pressley tester, Micronaire…etc. were for practical
applications replaced by HVI (high volume instrument) testing systems which
provide rapid and accurate measurements of the basic cotton fiber properties. Using
HVI makes it possible to obtain a big amount of data, furthermore it is provided by
special software for automatic calculation of predicted yarn strength in the old
generations of HVI (900 system) and predicted SCI (spinning consistency index) in
HVI spectrum and new models. The predicted values of yarn strength and SCI were
calculated for each tested sample using the measured fiber quality data according to
the regression equation
y= a+ bix1+ b2x2+…………bnxn
Where y= yarn strength in HVI 900 systems and SCI in Spectrum and 1000
HVI systems.
x1, x2, …………xn are measurements of fiber quality properties.
Nemours research work has been carried out to study the relationship between yarn
quality and fiber properties as well as to develop prediction equations that provide
calculated values of yarn strength based on fiber quality measurements. Ethridge et
al. (1982), El Mogazhy and Broughton (1990), El Mogazhy et al (1992), Frydrych
(1992), Sief et al (1994) Cheng and Adams (1995), Moon et al (1998), Suh et al
(1998), Pan (2001), Ureyen and Kadoglu (2006) and Hequet and Abidi (2008)studied
this relationship using different statistical models and techniques and came to
different prediction equations, For instance, Ethridge et al. (1982) found a linear
empirical relationship between rotor yarn strength, fiber strength, micronaire, fiber
length uniformity ratio and grayness. They defined the CSP at the rotor yarn as:
CSP = - 6487.01 + 728.84 InS – 2913.89 M + 658.41 M2 – 50.10 M3 + 2258.54 InU –
0.00003(GXY)2
Where InS = the natural logarithm of fiber strength
M = the micronaire index raised to the power
InU = the natural logarithm of length uniformity ratio
( GXY)2 = the square product of grayness multiplied by yellowness
Hunter et al. (1982) proposed a range of regression equations for predicting the
strength of ring spun yarns from a range of fiber properties, some of these equations
are follows:
Ring yarn CSP = 43 UR + 125 BT – 103 BE – 65 Mc + 10.5 YC + 47.3 TF - 3601
Ring yarn tenacity ( cN/tex) = 0.31 UR + 0.80 BT – 1.1 BE – 0.73 Mc + 0.062 YC +
35 TF – 21.8
Where: UR = the uniformity ratio BT = bundle tenacity
Mc = micronaire value
BE = bundle elongation
YC = yarn count m/tex F = twist factor
TC = trash content measured by Shirley trash analyzer
NTP = the number of trash particles
SL 50% = 50% Span length
El- Moghazy (1988) developed the following regression equation for calculating the
count strength product (CSP) of yarns:
CSP = - 4184 + 1141.2 FL + 71.2 LU + 49.4 FE – 22.7 Rd + 2041/FF
Where: FL = UHM length in inches LU = length uniformity FS = fiber strength
FE = fiber elongation
Rd = reflectance
FF = micronaire value.
Ghosh et al. (2003) developed the following equation for spun yarn tenacity Q1 as:
Q1 = nh/ne. Fn. Qb/100. Con2 Q1
Where nh = the number of the fiber at the place of yarn break
ne = the average number of fiber in the yarn cross section
Fn = the fiber bundle tenacity
Qb = the percentage of broken fiber in the yarn failure zone
Q1 = the average helix of fiber at the time of yarn failure
Regarding Spinlab 900 HVI system the manufacturing company provided the system
with two equations for predicting skein breaking load and skein strength (CSP).
y1= 173.58+ 86.42 x2+ 1.95 x3+ 3.0 x4+ 8.72 x5+ 0.99 x6
y2= 1818.0- 24.0 x1+ 270.0 x2- 8.0 x3+ 45.0 x4- 112.0 x5- 2.21 x6- 1.10 x7
where y1 skein breaking load (SBr), y2 skein strength (count strength product)
for 22s carded yarns, x1 trash area %, x2 2.5% SL in., x3 LUR%, x4 fiber strength, x5
micronaire value, x6 Rd% and x7 +b.
In 1989 CRI found that the predicted values of yarn strength and breaking load
obtained from the mentioned two equations of HVI 900 system did not match well
the determined values of yarn strength and braking load for the 60’s carded yarns
spun from Egyptian cotton which was logic since the two equations were derived
from Upland cotton data and they were for 22s carded yarns. Therefore, a research
trial was conducted in Cotton Fiber Res. Section during 1990 and 1991 seasons to
develop two equations for predicting skein strength and breaking load for 60’s carded
yarns spun from the Egyptian cottons under the conditions of the 60 gm. technique
used in CRI (Sief et al. 1994). The obtained equations were:
y1 (breaking load) = 53.1+ 28.0 x2+ 0.84 x3+ 0.94 x4- 0.89 x5- 0.11 x6
y2 (CSP) = 1383.3- 326.9 x1+ 1766.1 x2- 27.4 x3+ 58.4 x4- 89.2 x5+ 13.2 x6- 17.8 x7
Where: y1 skein breaking load (SBr), y2 skein strength (count strength product CSP)
for 60s carded yarns, x1 trash area %, x2 2.5% SL in., x3 LUR%, x4 fiber strength, x5
micronaire value, x6 Rd% and x7 +b.
Applying the obtained equation of skein strength to HVI 900 fiber data of
Egyptian cotton automatically provided reliable predicted values of yarn strength for
about 10 years which was very useful in the quality evaluation of the big number of
samples delivered from the breeding program and other research trials and programs
of Cotton Res. Institute.
In 2002 fiber quality evaluation labs of CRI received the HVI Spectrum
system. The manufacturing company made a lot of modifications in this system to be
calibrated using HVI calibration cotton only, leading to big differences in fiber
strength values besides measuring UHML, ML and uniformity index % instead of
50%, 2.5 % span lengths and length uniformity ratio obtained from the old model
HVI 900. Moreover the manufacturing company provided the HVI Spectrum with a
prediction equation for calculating the spinning consistency index (SCI) instead of
skein strength. Unlikely, the Egyptian cotton breeders are accustomed to deal with
skein strength in the breeding program. Many attempts were done to modify
mathematically the HVI 900 skein strength prediction equation to be used with the
HVI Spectrum data, unfortunately the obtained skein strength predicted values was
not valid and accurate. Therefore, the main objective of this work is to develop new
equations for predicting yarn strength of Egyptian cottons using HVI Spectrum data
and Programming the HVI Spectrum system with this equations to obtain reliable
predicted values of yarn strength in the same table of HVI fiber data.
Materials and Methods
The present study was carried out in Cotton Technology Res. Department
Cotton Res. Institute, Agric. Res. Center at Giza on ten Egyptian cotton varieties and
promising
crosses
namely,
Giza
80,
Giza
90,
(Giza
90×Aust.)
and
[G83×(G75X5844)]×G80 (Upper Egypt long staple cottons), as well as Giza 86 and
(10229×Giza 89) (Delta long staple cottons) in addition to Giza 88, Giza 92, Giza 93
(Giza77×Ps6) and [G84×(G70×51B)]×P62 (extra long staple cottons). The chosen
cottons represent the long and extra long Egyptian cottons. 250 lint cotton samples
were obtained from the commercial crop of 2010 and 2011 seasons as will as from
field trials of cotton Research Institute. The raw cotton samples were of different
grades ranged from FG-1/4 to G-1/4 in most of these cottons. The cotton samples of
the selected cottons were tested using Uster HVI Spectrum system according to
ASTM standard test method (D 5867 – 05) to obtain the following fiber quality
measurements: the tensile strength (g/tex), fiber elongation %, micronaire value
(Mike), maturity ratio (MR), upper half mean length (UHML) in mm., length
uniformity index % (UI %), color reflectance percent (Rd %) and yellowness (+b).
40’s carded yarns at 4.0 T.M. were spun from the different samples of Upper Egypt
long staple cottons, while 60’s carded yarns at 3.6 T.M. were spun from Delta long
and extra long staple cottons. All the spun yarns were produced according to the 60
gram technique used in the experimental spinning mill in Spinning Res. Section,
Cotton Res. Institute, Giza. The spun yarns were tested for skein strength by Goodbrand Lea tester according to ASTM (D- 1598- 93R00). Moreover, the study
included and used the HVI and skein strength data of the cotton varieties and
promising crosses of Upper Egypt, Delta long staple and extra long staple Egyptian
cottons tested in trial B in 2010 and 2011 growing seasons. The obtained data was
computed using SAS* multiple regression analysis to develop prediction equations
for skein strength of Upper Egypt cottons at 40’s carded yarns, as well as to develop
prediction equations for skein strength of Delta long staple and extra long staple
Egyptian cottons at 60’s carded ring spun yarns. Data of SCI and actual skein
strength was subjected to linear regression and simple correlation analysis to study
the relationship between SCI and skein strength as well as to get a predicted value of
skein strength based on SCI data. Predicted values of skein strength were calculated
by applying the prediction equations to HVI spectrum data of 10 samples of each of
the mentioned cottons in 2011 season (another samples not used in the calculation of
the prediction equations). Differences between the determined skein strength and the
predicted ones were calculated for each cotton.
Results and discussion
Subjecting the obtained HVI Spectrum data and skein strength of the spun
yarns to regression analysis led to five prediction equations, for predicting skein
strength of 40’s carded ring yarns spun from Upper Egypt cottons, besides, another
five equations for predicting skein strength of 60’s carded ring yarns spun from Delta
long and extra long staple Egyptian cottons.
Equations for predicting skein strength of 40s yarns of Upper Egypt cottons:
Equation 1 includes length, length uniformity, strength, micronaire, Rd% and +b
Skein strength = -1277.937+ 45.410x Length + 4.402 x Uniformity index + 35.626 x
Strength - 39.355 x Micronaire - 0.919 x Rd +58.472 x +b
Equation 2 includes length, length uniformity strength and micronaire.
Skein strength = -961.717 + 46.954 x Length + 5.211 x uniformity index +
8.994x Strength - 20.442x Micronaire
Equation 3 includes length, length uniformity, strength, micronaire, and maturity ratio and
elongation%
Skein strength =-1683.455 + 50.970 x Length + 5.232 x uniformity index + 38.079 x
Strength - 22.646 x Micronaire + 178.272 x Maturity ratio + 59.850x Elongation
Equation 4 includes length, length uniformity, strength, micronaire, maturity ratio and Rd%.
Skein strength = -1094.35756 + 49.914 x Length + 5.2371 x uniformity index
+ 37.18052 x Strength - 44.83373 x Micronaire + 325.10457 x Maturity
Ratio – 1.3834 x Rd
Equation 5 includes SCI data
Skein strength = 734.528 + 9.445 x SCI
Equations for predicting skein strength of 60s yarns of Delta LS & ELS cottons
Equation 1: Skein strength = -2058.088+ 43.496x Length + 28.172 x Uniformity index +
36.222 x Strength – 129.105 x Micronaire – 4.906 x Rd +9.360x +b
Equation2: Skein strength = -2500.343 + 47.844 x Length + 28.365 x uniformity index +
37.044x Strength – 138.953x Micronaire
Equation 3: Skein strength = -2852.909 + 41.777 x Length + 21.562 x uniformity index +
25.292 x Strength – 308.535 x Micronaire + 2870.007 x Maturity
ratio + 43.654 x Elongation
Equation 4: Skein strength = -2736.083 + 41.741 x Length + 22.108x uniformity index
+ 25.622x Strength – 301.420 x Micronaire
+ 2744.514 x Maturity ratio – 5.791x Rd
Equation 5: includes SCI data
Skein strength = - 74.881 + 9.445 x SCI
Prediction of yarn strength of Upper Egypt LS Cottons:
HVI Spectrum data, determined skein strength and corresponding predicted ones derived from the
five prediction equations for Upper Egypt cottons as well as the differences between determined
and predicted values of skein strength are shown in Table 1 and Fig. 1 (each cotton was represented
by ten random samples). Comparing the determined and predicted means of skein strength, the
results in Table 1 showed that the determined skein strength of 40s carded yarns spun from Giza 80
averaged 2263 while the predicted values of its skein strength averaged 2352, 2297, 2281, 2298 and
2339 derived from equations 1, 2, 3, 4 and 5 respectively. The determined skein strength of Giza 90
averaged 2193 while the predicted ones averaged 2193, 2191, 2204, 2197 and 2199 derived from
the four equations and SCI data respectively. The determined skein strength of Giza 90 x Australi
yarns averaged 2100, while the predicted ones obtained from the four equations and SCI data
averaged 2140, 2079, 2083, 2085 and 2064 respectively. The determined skein strength of
[G83×(G75X5844)]×G80 yarns averaged 2261 while the predicted ones obtained from the four
equations and SCI data averaged 2217, 2239, 2247, 2233 and 2227 respectively. These results
indicated that the differences between means of the determined skein strength of Upper Egypt
cottons and the corresponding predicted ones ranged from 0 to 40 units which were very low
compared to the testing error in measuring skein strength (100 – 150 units). However the
differences between the individual values of determined skein strength and the corresponding
predicted ones are bigger than noticed between average values, which is logic since the individual
values are characterized by bigger variation than averages. The differences between individual
values ranged from 1 to 118 units in the prediction using equation 1, ranged from 2 to 82 units in
the values obtained from applying equation 2, ranged from 3 to 95 units when applying equation 3
and from 0 to 92 units when applying equation 4, while ranged from 2 to 131 units when based on
SCI data. It is clear from these results that all the prediction equations provide reasonable and
reliable predicted skein strength values, however equations 2, 3 and 4 showed smaller differences
between individual values of determined skein strength and the corresponding ones (< 100 units)
compared to equation 1 and the prediction via SCI data. Furthermore, it is more practical to use
equation 2 since it depends only on the main four fiber properties: length, length uniformity,
strength and mike, while the addition of Rd% and +b in equation 1, maturity ratio and elongation in
equation 2, maturity ratio and Rd% in equation 4 did not improve the accuracy of the prediction
compared to equation 2.
Prediction of yarn strength of Delta LS & ELS Cottons:
HVI Spectrum data, determined skein strength and corresponding predicted ones obtained from the
five prediction equation for Delta LS and ELS cottons as well as the differences between
determined and predicted values of skein strength are shown in table 2 and Fig. 2 (each cotton was
represented by ten random samples. The results in Table 2 showed that the determined skein
strength of 60s carded yarns spun from Giza 86 averaged 2486 while the means of predicted values
of its skein strength were 2540, 2549, 2491, 2503 and 2577 derived from equations 1, 2, 3, 4 and
SCI data respectively. The determined skein strength of 10229 x Giza 86 averaged 2495 while the
predicted ones averaged 2535, 2539, 2498, 2519 and 2576 derived from the four equations and SCI
data respectively. The determined skein strength of Giza 88 yarns averaged 2987 while the
predicted ones obtained from the four equations and SCI data averaged 3023, 2981, 22956, 3005
and 2931 respectively. The determined skein strength of Giza 92 yarns averaged 2978 while the
predicted ones obtained from the four equations and SCI data averaged 2844, 2901, 2957, 2926 and
2976 respectively. The determined skein strength of Giza 77 x Ps6 yarns averaged 3100 while the
predicted ones obtained from the four equations and SCI data averaged 3097, 3054, 3085, 3105 and
2986 respectively The determined skein strength of [G84×(G70×51B)]×P62 yarns averaged 2926
while the predicted ones obtained from the four equations and SCI data averaged 2843, 2859, 2872,
2853 and 2859 respectively These results indicated that the differences between means of the
determined skein strength of Delta LS & ELS cottons and means of the corresponding predicted
ones ranged from 2 in Giza 92 to 114 units in Giza 77 x Ps6 which is lower than the testing error in
measuring skein strength. However the differences between the individual values of determined
skein strength and the corresponding predicted ones are bigger than noticed between average
values. These differences ranged from 0 to 116 units for the prediction using equation 1, ranged
from 1 to 108 units for the values obtained from applying equation 2, ranged from 1 to 110 units
when applying equation 3 and from 0 to 114 units when applying equation 4, while showed bigger
range when using SCI data, being from 5 to 140. It is clear from these results that all the prediction
equations provide reliable predicted skein strength values except the prediction based on SCI data
which showed higher fluctuation than the other equations, however equations 2, 3 and 4 showed
smaller differences between individual values of determined skein strength and the corresponding
ones (< 100 units) compared to equation 1 and the prediction via SCI data. Furthermore, as noticed
in case of Upper Egypt cottons, it is more practical to use equation 2 since it depends only on the
main four fiber properties: length, length uniformity, strength and mike, while the addition of Rd%
and +b in equation 1, maturity and elongation in equation 2, maturity and Rd% in equation 4 did not
improve the accuracy of skein strength prediction compared to equation 2.
Based on the obtained data it is decided to use the equation that include length, uniformity, strength,
and mike (equation 2) in programming HVI spectrum system in Cotton Res. Institute to get reliable
predicted values of skein strength in the same table of HVI fiber quality data.
The relationship between SCI and yarn strength:
SCI is a calculation for predicting the overall quality and spinability of cotton to be used on bale
management and quality evaluation programs. It depends on fiber data and derived from regression
equation. It is developed by HVI manufacturers to substitute the predicted values of CSP and
breaking load of the tested cotton samples. Therefore it is worthy to study the relationship between
yarn strength and the corresponding SCI values. The results in Table 1, Table 2, Figure 1, Figure 2
and Fig. 3 indicated that the correlation between skein strength and SCI was highly significant
whether calculated from Upper Egypt cottons data or from the Delta LS & ELS fiber cottons data.
The recorded values of simple correlation coefficients were 0.86 for Upper Egypt cottons and 0.95
for Delta LS and ELS cottons. These results indicate that SCI is a good Criterion for expressing the
spinning value of cotton. However the prediction of yarn strength via SCI data showed more
fluctuation in the predicted values compared to the prediction via fiber data.
References
ASTM D1578-93R00. (2005). Standard Test Method for Breaking Strength of Yarn in Skein Form.
Annual Book of ASTM Standards. Vol. 7. 02 Section 7.
ASTM D5867-05. (2005). Standard Test Method for measurement of physical properties of cotton
fibers by High Volume Instruments. Annual Book of ASTM Standards. Vol. 7. 02 Section 7.
Cheng L., and D. L. Adams. Yarn strength prediction using neural networks. I. Fiber properties and
yarn strength relationship. Textile Research Journal, vol. 65, no. 9, pp. 495–500, 1995.
El-Mogazhy, Y., Broughton, R. M. (1992). Regression observation of HVI fiber properties, yarn
quality, and processing performance of medium staple cotton. Part I: HVI fiber parameter, Text.
Res. J., 62 (4), 218-226.
El-Mogazhy, Y., Broughton, R., and Lynch, W. K.(1990). A statistical approach for determining the
technological value of cotton using HVI fiber properties, Text. Res. J., 60 (9), 495-500.
Ethridge, M. D., J. D. Towery, and J. F. Hembree. (1982). Estimating func nal relationships
between fiber properties and the strength of open-end spun yarns. Text. Res. J., vol. 52, no. 1, pp.
35–45.
Frydrych, I., (1992). A new approach for predicting strength properties of yarns. Tex.Res. J., 62,
340-348.
Hequet, E.F. and Abidi, N. (2008). Relationships between fiber and yarn tensile properties
properties. Proceedings of 2008 Beltwide Cotton Conferences, Nashville, Tennessee ,PP. 14681471.
Moon W. Suh, Hyun-Jin Koo and Michael D. Watson. (1998). Estimation of HVI Bundle Modulus
and Toughness as Determinants to Tensile Properties of Spun Yarns. Proceedings of 1998 Beltwide
Cotton Conferences, PP 1530-1537.
Pan, N. (2001). Relationship between fiber and yarn strength, Tex. Res. J., 61, 960-964.
Price, C. Senter, H. J. Foulk, G. Gamble, and W. Meredith. (2009). Relationship of fiber properties
to vortex yarn quality viapartial least squares. Journal of Engineered Fibers and Fabrics, vol. 4, no.
4, pp. 37–46.
Sief, M. G., S. H. M. El-Hariry, and M. B. El- Kadi (1994). Predicted yarn strength of Egyptian
cotton using HVI testing. The 19th Int. Conf.for Stat. & Computer Science, Cairo, Egypt, April
1994:213-227.
Suh M.W., K. Hyun-Jui, and C. Xiaoling. (1998). Prediction of yarn tensile properties based on
HVI testing of 36 U.S. Upland cottons,. in Proceedings of the Beltwide Cotton Conferences,
pp.786–790, San Diego, Calif, USA, January.
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HVI fiber properties. Text. Res. J., vol. 76, no. 5, pp. 360–366.
‫الملخص العربى‬
‫معادالت جديدة للتنبؤ بمتانة الغزل في القطن المصري باستخدام قياسات‬
‫‪HVI Spectrum‬‬
‫تتميز الطرز الحديثة من أجهزة ‪ HVI‬بالسرعة العالية والدقة المطوبة في قياس صفات جودة القطن‬
‫عالوة علي حساب معامل للقيمة الغزلية للقطن ‪ SCI‬من خالل معادالت تنبوء تعتمد علي صفات جودة التيلة‬
‫المقاسة مما يمثل فائدة كبيرة لكل المعنيين بصفات جودة القطن‪ .‬ولما كان مربي القطن في مصر يعتمد علي‬
‫متانة الشلة في تقييم صفات جودة التراكيب الوراثية واألصناف التجارية في برامج التربية والمحافظة علي‬
‫النقاوة الوراثية ألصناف القطن المصري لذا لزم إجراء هذه الدراسة للوصول إلي أفضل المعادالت للتنبوء‬
‫بمتانة الغزل ألقطان الوجه القبلي وألقطان الوجه البحري الطويلة وفائقة الطول من قياسات صفات جودة التيلة‬
‫المقدرة بجهاز ‪ HVI spectrum‬وبرمجته بها للحصول علي قيمة تقديرية لمتانة الشلة للعينات المختبرة‬
‫إلستخدامها حين اليكفي وزن العينة للغزل واختبارات الخيوط‪.‬‬
‫إشتملت الدراسة علي عشرة من أصناف القطن المصري والهجن المبشرة هي جيرة ‪ , 80‬حيزة ‪90‬‬
‫جيزة ‪ / 90‬أسترالي ‪,‬ج ‪ // 83‬ج‪ /// 5844 / 75‬ج‪ 80‬من أقطان الوجه القبلي ‪ ,‬جيزة ‪ / 10229, 86‬ج ‪86‬‬
‫من األقطان الطويلة بالوجه البحري وجيزة ‪ , 88‬جيزة ‪ 92‬وجيزة ‪ , 93‬ج‪ //84‬ج‪ / 70‬ج‪51‬ب ‪ ///‬س ‪ 62‬من‬
‫ااألقطان فائقة الطول حيث تم تقدير صفات جودة التيلة بجهاز‪ HVI spectrum‬لعدد ‪ 250‬عينة من رتب‬
‫مختلفة موسم ‪ 2011 , 2010‬وغزل أقطان الوجه القبلي علي عد ‪ 40‬مسرح وبرم ‪ 4‬وأقطان الوجه البحري‬
‫علي عد ‪ 60‬مسرح وبرم ‪ 3,6‬كما هو متبع في برامج التربية والمحافظة بمعهد بحوث القطن بالجيزة‪ .‬تم تقدير‬
‫متانة الشلة للخيوط المغزولة واستخدمت قياسات التيلة وقيم ‪ SCI‬ومتانة الشلة في حساب خمسة معادالت أنحدار‬
‫متعدد للتنبوء بمتانة الشلة من قياسات صفات جودة التيلة المقدرة بجهاز ‪ HVI‬لألقطان المختبرة‪.‬‬
‫تم إختبار مدي دقة معادالت التنبوء المتحصل عليها بتطبيقها علي قياسات ‪ HVI‬لألقطان المذكورة موسم ‪2011‬‬
‫وحساب الفروق بين متانة الشلة المقاسة والمتنبأ بها لكل صنف وهجين مبشر وقد بينت الدراسة دقة المعادالت‬
‫التي تم التوصل إليها وكان أفضلها المعادلة التي إشتملت فقط علي قياسات طول التيلة وإنتظام الطول ومتانة‬
‫التيلة وقراءة الميكرونير حيث كانت الفروق بين متانة الشلة المقاسة بجهاز جودبراند والمحسوبة من هذه‬
‫المعادلة أقل من ‪ 100‬وحدة ‪.‬‬
‫بينت الدراسة أن العالقة بين متانة الشلة ومعامل الغزل ‪ SCI‬موجبة وعالية المعنوية إال أن قيم متانة‬
‫الشلة المحسوبة من بيانات ‪ SCI‬اتصفت بالتباين العالي وارتفاع الفروق بينها وبين متانة الشلة المقاسة إلى ‪140‬‬
‫وحدة في بعض الحاالت‬
‫تم برمجة جهاز ‪ HVI spectrum‬بالمعادلة التي تشتمل علي الطول وانتظامه والمتانة والميكرونير وهي كما‬
‫يلي ‪:‬‬
‫متانة الشلة لخيوط ‪ 40‬مسرح ألقطان الوجه القبلي = ‪ x 46.954 + 961.717-‬الطول بالمم ‪x 5.211 +‬‬
‫انتظام الطول ‪ x 8.994+‬متانة التيلة ‪ x 20.442 -‬قراءة الميكرونير‬
‫متانة الشلة لخيوط ‪ 60‬مسرح ألقطان الوجه البحري وفائق الطول = ‪ x 47.844 + 2500.343-‬الطول بالمم‬
‫‪ x 28.365‬انتظام الطول ‪ x 37.044 +‬متانة التيلة ‪ x 138.953 -‬قراءة الميكرونير‬
Table 1: HVI fiber data, SCI, determined and predicted skein strength of 40’s carded yarns for Upper Egypt LS commercial cotton
varieties and promising crosses.
Genotype
G80
Mean
Max
Min
G90
Mean
Max
Min
UHM UI
Str. Elon.
mm
% g/tex
%
3.7
0.89
31.5 85.0 37.1
8.0
3.9
0.91
31.2 84.2 39.0
8.0
3.5
0.88
30.2 85.5 36.3
7.6
3.5
0.88
31.4 85.7 36.0
8.1
3.5
0.87
31.1 85.0 37.0
8.0
3.3
0.85
30.8 85.4 35.7
7.4
3.5
0.87
30.8 85.8 36.1
7.5
3.5
0.86
31.0 85.3 37.9
8.0
3.6
0.89
31.3 85.2 35.3
7.5
3.5
0.86
31.1 83.9 36.4
7.5
3.6
0.88
31.0 85.1 36.7
7.8
3.9
0.91
31.5 85.8 39.0
8.1
3.3
0.85
30.2 83.9 35.3
7.4
4.1
0.94
30.5 85.3 37.7
8.2
3.8
0.90
30.0 83.3 34.0
8.3
3.7
0.90
31.0 83.1 35.5
8.4
4.1
0.93
30.0 83.0 35.0
7.9
4.2
0.95
30.4 84.0 36.0
7.9
4.3
0.94
30.3 85.0 35.4
7.9
4.3
0.94
30.0 85.6 35.5
8.3
4.0
0.90
29.4 84.0 34.7
8.3
4.2
0.92
30.2 85.0 35.7
8.2
3.8
0.90
30.1 85.3 34.0
8.2
4.1
0.92
30.2 84.4 35.4
8.2
4.3
0.95
31.0 85.6 37.7
8.4
3.7
0.90
29.4 83.0 34.0
7.9
P1: Predicted skein strength using equation 1
P2: Predicted skein strength using equation 2
P5: Predicted skein strength using SCI data
Mike
Mat.
Rd
dtr.P1
P2
.+b SCI Detr.
%
P1
66.3 12.2 170 2260 2355 -95 2331
66.6 12.9 170 2370 2438 -68 2383
66.1 12.8 169 2295 2313 -18 2246
66.9 12.5 172 2225 2339 -114 2291
67.9 12.7 172 2315 2369 -54 2313
66.9 12.5 171 2190 2308 -118 2254
67.0 12.9 172 2225 2339 -114 2268
67.5 12.5 175 2270 2387 -117 2345
65.1 12.7 165 2225 2317 -92 2255
65.5 12.8 163 2255 2351 -96 2284
66.6 12.7 170 2259 2352
89
2297
67.9 12.9 175 2370 2438 118 2383
65.1 12.2 163 2185 2308
18
2246
65.5 11.8 167 2245 2294 -49 2301
63.9 12.1 146 2080 2162 -82 2129
66.1 11.9 155 2205 2250 -45 2235
62.7 11.7 145 2185 2162
23
2160
64.9 11.8 154 2210 2220 -10 2221
65.8 11.8 156 2230 2194
36
2196
65.5 11.2 159 2250 2151
99
2189
66.1 11.9 151 2125 2141 -16 2128
65.8 11.7 158 2250 2198
52
2205
66.7 11.9 159 2150 2161 -11 2144
65.3 11.8 155 2193 2193
42
2191
66.7 12.1 167 2250 2294
99
2301
62.7 11.2 145 2080 2141
10
2128
P3: Predicted skein strength using equation 3
P4: Predicted skein strength using equation 4
dtr.P2
-71
-13
49
-66
2
-64
-43
-75
-30
-29
44
75
2
-56
-49
-30
25
-11
34
61
-3
45
6
32
61
3
P3
2333
2385
2218
2299
2310
2212
2235
2339
2228
2250
2281
2385
2212
2319
2147
2263
2158
2223
2197
2212
2143
2220
2157
2204
2319
2143
dtr.P3
-73
-15
77
-74
5
-22
-10
-69
-3
5
39
77
5
-74
-67
-58
27
-13
33
38
-18
30
-7
34
74
3
P4
2334
2383
2248
2297
2311
2253
2268
2338
2265
2283
2298
2383
2248
2308
2137
2243
2171
2232
2201
2193
2125
2205
2149
2197
2308
2125
dtr.P4
-74
-13
47
-72
4
-63
-43
-68
-40
-28
45
74
4
-63
-57
-38
14
-22
29
57
0
45
1
33
63
0
P5
Via SCI
2340
2340
2331
2359
2359
2350
2359
2387
2293
2274
2339
2387
2274
2312
2114
2199
2104
2189
2208
2236
2161
2227
2236
2199
2312
2104
dtr. P5
-80
30
-36
-134
-44
-130
-134
-117
-108
-19
86
140
19
-67
-34
6
81
21
22
14
-36
23
-86
39
86
6
Table 1: continue.
G90XAUS
Genotype
{G83(G75×8544)}G80
Mean
Max
Min
Mean
Max
Min
UHM UI
Str. Elon.
mm
% g/tex
%
0.93
4.5
30.3 85.1 32.5
7.6
0.93
4.5
29.9 82.5 33.9
8.5
0.93
4.5
29.3 83.0 34.9
7.6
0.94
4.6
29.1 83.6 33.9
8.1
0.95
4.6
28.2 83.5 32.1
7.8
0.93
4.5
28.9 84.0 33.3
8.0
0.94
4.5
29.1 84.3 33.5
8.2
0.95
4.6
30.1 85.8 33.8
8.3
0.96
4.5
29.3 83.8 35.0
7.6
0.95
4.6
30.1 85.8 33.9
8.3
0.94
4.5
29.4 84.1 33.7
8.0
1.0
4.6
30.3 85.8 35.0
8.5
0.9
4.5
28.2 82.5 32.1
7.6
0.94
4.5
29.2 84.5 38.5
8.3
0.93
4.4
31.9 86.7 38.7
7.8
0.92
4.3
29.6 83.6 37.3
8.2
0.94
4.2
30.3 84.3 38.2
8.1
0.92
4.2
29.4 84.4 34.6
8.3
0.93
4.4
29.4 84.9 35.4
8.2
0.92
4.4
29.4 84.5 36.0
8.0
0.93
4.4
29.4 83.6 38.2
8.3
0.92
4.3
30.5 84.3 37.5
8.3
0.93
4.4
29.5 83.0 36.9
7.8
0.93
4.4
29.9 84.4 37.1
8.1
0.94
4.5
31.9 86.7 38.7
8.3
0.92
4.2
29.2 83.0 34.6
7.8
P1: Predicted skein strength using equation 1
P2: Predicted skein strength using equation 2
P5: Predicted skein strength using SCI data
Mike
Mat.
Rd
dtr.P1
P2
.+b SCI Detr.
%
P1
61.8 12.9 144 2100 2151 -51 2080
63.3 12.9 136 2250 2170 -20 2102
60.1 12.4 138 2105 2154 -49 2115
58.5 12.9 136 2140 2139
1
2068
62.8 12.9 131 2000 2029 -29 1955
62.1 13.1 139 2050 2122 -72 2039
59.8 13.1 140 2080 2142 -62 2058
65.9 13.1 153 2085 2195 -110 2123
58.8 13.2 142 2200 2100 100 2123
58.7 12.9 149 2090 2194 -104 2127
61.2 12.9 141 2110 2140
60
2079
65.9 13.2 153 2250 2195 110 2127
58.5 12.4 131 2000 2029
1
1955
66.5 11.5 159 2265 2226
39
2259
66.8 11.6 186 2385 2375
10
2407
67.3 11.5 155 2300 2204
96
2230
67.4 11.6 163 2330 2281
49
2304
67.7 11.6 152 2145 2112
33
2122
66.3 11.6 154 2205 2136
69
2152
67.1 11.5 154 2255 2149 106 2173
67.0 11.7 156 2210 2235 -25 2254
66.0 11.5 160 2270 2257
13
2284
66.8 11.8 150 2240 2197
43
2205
66.9 11.6 159 2261 2217
46
2239
67.7 11.8 186 2385 2375 106 2407
66.0 11.5 150 2145 2112
10
2122
P3: Predicted skein strength using equation 3
P4: Predicted skein strength using equation 4
dtr.P2
20
48
-10
72
45
11
22
-38
77
-37
38
77
10
6
-22
70
26
23
53
82
-44
-14
35
38
82
6
P3
2063
2136
2092
2076
1945
2040
2073
2149
2105
2152
2083
2152
1945
2275
2403
2240
2314
2140
2164
2171
2270
2304
2193
2247
2403
2140
dtr.P3
37
14
13
64
55
10
7
-64
95
-62
42
95
7
-10
-18
60
16
5
41
84
-60
-34
47
38
84
5
P4
2087
2104
2118
2075
1960
2041
2066
2126
2137
2139
2085
2139
1960
2249
2404
2221
2304
2119
2147
2163
2244
2279
2197
2233
2404
2119
Act.P4
13
46
-13
65
40
9
14
-41
63
-49
35
65
9
16
-19
79
26
26
58
92
-34
-9
43
40
92
9
P5
Via SCI
2095
2019
2038
2019
1972
2047
2057
2180
2076
2142
2064
2180
1972
2236
2406
2199
2274
2170
2189
2189
2208
2246
2151
2227
2406
2151
dtr. P5
5
131
67
121
28
3
23
-95
124
-52
65
131
3
29
-21
101
56
-25
16
66
2
24
89
43
101
2
Table 2: HVI fiber data, SCI, determined and predicted skein strength of 60’s carded yarns for Delta LS and ELS Egyptian commercial cotton
varieties and promising crosses.
UHM UI
Str.
mm
% g/tex
4.7
0.96 33.4 86.6 44.3
4.8
0.96 34.0 86.9 44.3
4.8
0.98 33.7 86.2 44.4
4.6
0.95 34.1 86.2 45.7
4.8
0.96 33.6 86.4 44.6
Giza 86
4.5
0.95 33.4 85.6 45.4
4.5
0.98 34.2 86.0 43.0
4.6
0.95 33.7 85.8 45.1
4.6
0.98 33.5 86.8 43.3
4.7
0.96 33.5 86.6 42.2
Mean
4.7
0.96 33.7 86.3 44.2
Max
4.8
0.98 34.2 86.9 45.7
Min
4.5
0.95 33.4 85.6 42.2
4.3
0.94 33.5 87.6 40.0
4.2
0.95 33.8 87.6 43.5
4.3
0.94 34.4 87.2 42.0
4.3
0.94 34.7 87.9 41.7
4.3
0.92 33.5 87.8 40.1
10229XG86
4.3
0.95 34.3 87.0 41.7
4.3
0.94 34.3 87.4 40.1
4.3
0.93 34.1 87.8 40.0
4.3
0.94 33.4 88.5 40.0
4.4
0.94 34.5 87.7 41.7
Mean
4.3
0.94 34.1 87.7 41.1
Max
4.4
0.95 34.7 88.5 43.5
Min
4.2
0.92 33.4
87
40
P1: Predicted skein strength using equation 1
P2: Predicted skein strength using equation 2
P5: Predicted skein strength using SCI data
Genotype
Mike
Mat
Elon. Rd
dtr.P1
.+b SCI Deter.
%
%
P1
7.6
75.7 8.2 197 2430 2538 -108
7.8
77.4 8.8 200 2450 2557 -107
7.8
77.9 8.5 197 2470 2522 -52
7.8
78.4 8.0 203 2550 2605 -55
7.7
78.7 8.5 199 2530 2527
3
7.5
77.3 8.3 199 2590 2568
22
7.9
78.3 8.6 196 2460 2525 -65
7.4
78.2 8.6 199 2555 2562
-7
7.3
75.0 8.4 196 2470 2530 -60
7.1
76.1 8.3 192 2355 2465 -110
7.6
77.3 8.4 198 2486 2540
59
7.9
78.7 8.8 203 2590 2605 110
7.1
75.0 8.0 192 2355 2465
3
7.8
79.6 8.8 196 2515 2453
62
7.8
77.7 8.8 201 2530 2615 -85
7.2
78.0 8.2 201 2595 2555
40
7.4
75.6 8.4 202 2570 2591 -21
7.6
75.6 8.6 195 2480 2480
0
7.6
75.1 8.6 197 2460 2552 -92
7.6
74.8 8.6 194 2450 2507 -57
7.1
70.8 8.1 192 2410 2521 -111
7.7
74.5 8.7 197 2420 2498 -78
7.7
74.3 8.7 199 2515 2573 -58
7.6
75.6 8.6 198 2495 2535
60
7.8
79.6 8.8 202 2595 2615 111
7.1
70.8 8.1 192 2410 2453
0
P3: Predicted skein strength using equation 3
P4: Predicted skein strength using equation 4
P2
2532
2555
2535
2630
2543
2582
2543
2577
2529
2459
2549
2630
2459
2471
2629
2577
2600
2481
2556
2508
2506
2492
2571
2539
2629
2471
dtr.P2
-102
-105
-65
-80
-13
8
-83
-22
-59
-104
64
105
8
44
-99
18
-30
-1
-96
-58
-96
-72
-56
57
99
1
P3
2465
2455
2488
2513
2440
2509
2557
2492
2551
2440
2491
2557
2440
2438
2598
2547
2557
2397
2540
2479
2473
2458
2499
2498
2598
2397
dtr.P3
-35
-5
-18
37
90
81
-97
63
-81
-85
59
97
5
77
-68
48
13
83
-80
-29
-63
-38
16
52
83
13
P4
2487
2479
2506
2531
2451
2517
2574
2491
2559
2435
2503
2574
2435
2447
2617
2536
2570
2422
2564
2506
2500
2492
2535
2519
2617
2422
dtr.P4
-57
-29
-36
19
79
73
-114
64
-89
-80
64
114
19
68
-87
59
0
58
-104
-56
-90
-72
-20
61
104
0
P5
via SCI
2568
2588
2568
2648
2595
2595
2554
2595
2554
2491
2577
2648
2491
2554
2662
2622
2635
2541
2568
2528
2501
2558
2595
2576
2762
2501
dtr. P5
-138
-138
-98
-98
-65
-5
-94
-40
-84
-136
91
138
5
-39
-132
-27
-65
-61
-108
-78
-91
-138
-80
82
138
27
Table 2: continue.
UHM UI
Str.
mm
% g/tex
4.0
0.96 36.8 87.0 48.1
3.9
0.95 36.0 88.9 46.0
3.9
0.95 37.4 88.0 48.1
4.0
0.95 37.5 87.0 47.6
3.9
0.94 36.4 88.9 49.4
Giza 88
3.9
0.95 37.4 88.2 47.9
3.9
0.94 36.4 88.0 46.0
4.0
0.96 37.7 87.0 49.6
3.9
0.95 37.4 88.6 46.2
4.0
0.96 36.3 88.6 48.5
Mean
3.9
0.95 36.9 88.0 47.6
Max
4.0
0.96 37.7 88.9 49.6
Min
3.9
0.94 36.0 87.0 46.0
3.9
0.97 34.3 88.2 48.7
3.7
0.96 34.1 87.2 47.8
3.7
0.95 34.3 88.7 47.1
3.8
0.96 34.0 88.8 48.1
3.6
0.95 33.9 88.1 46.7
Giza 92
3.7
0.95 33.8 87.6 48.6
3.6
0.94 34.4 87.9 48.0
3.6
0.94 35.1 88.1 49.0
3.7
0.96 34.4 88.8 47.9
3.8
0.96 33.4 88.7 48.5
Mean
3.7
0.95 34.2 88.2 48.0
Max
3.9
0.97 35.1 88.8 49.0
Min
3.6
0.94 33.4 87.2 46.7
P1: Predicted skein strength using equation 1
P2: Predicted skein strength using equation 2
P5: Predicted skein strength using SCI data
Genotype
Mike
Mat
Elon
%
7.3
7.1
7.4
7.5
7.3
7.0
7.2
7.6
7.0
7.0
7.2
7.6
7.0
7.3
6.9
6.9
6.7
6.6
6.6
6.4
7.2
6.6
6.0
6.7
7.3
6.0
Rd
Act.P1
.+b SCI Deter.
%
P1
65.7 11.9 218 2935 3008 -73
66.5 11.9 221 2930 2960 -30
66.4 11.9 225 3070 3072
-2
64.4 11.6 219 3025 2988
37
67.8 11.1 232 2985 3087 -102
68.1 11.2 227 2995 3056 -61
69.2 11.2 219 2995 2932
63
68.4 11.8 226 2990 3088 -98
68.5 11.7 224 2955 3008 -53
67.3 11.4 227 2990 3034 -44
67.2 11.6 223 2987 3023
56
69.2 11.9 232 3070 3088 102
64.4 11.1 214 2930 2932
2
78.0 8.9 229 3005 2900 105
74.5 8.8 220 2900 2852
48
77.0 8.8 227 2975 2866 109
77.9 8.8 230 2980 2874 106
78.5 9.2 225 2900 2826
74
76.7 8.6 225 2995 2887 108
78.3 8.8 228 2975 2887
88
77.7 8.8 233 3065 2962 103
73.6 8.8 228 2990 2919
71
76.4 8.9 229 2990 2868 122
76.9 8.8 227 2978 2884
93
4
78.5 9.2 233 3065 2962 108
73.6 8.6 220 2900 2826
48
P3: Predicted skein strength using equation 3
P4: Predicted skein strength using equation 4
P2
2954
2906
3025
2932
3051
3023
2899
3053
2972
2990
2981
3053
2899
2905
2861
2887
2899
2850
2888
2917
2993
2925
2890
2901
2993
2850
dtr.P2
-19
24
45
93
-66
-28
96
-63
-17
0
45
96
0
100
39
88
81
50
107
58
72
65
100
76
108
39
P3
2942
2908
2986
2915
2973
3005
2892
3003
2970
2980
2956
3005
2892
2938
2938
2932
2955
2938
2940
2968
2988
3002
2972
2957
3002
2932
dtr.P3
-7
22
84
110
12
-10
103
-13
-15
10
39
110
7
67
-38
43
25
-38
55
7
77
-12
18
38
77
7
P4
3004
2957
3050
2975
3026
3040
2911
3065
3003
3020
3005
3065
2911
2928
2928
2910
2917
2891
2905
2914
2977
2984
2909
2926
2984
2891
Act.P4
-69
-27
20
50
-41
-45
84
-75
-48
-30
49
84
20
77
-28
65
63
9
90
61
88
6
81
57
90
6
P5
via SCI
2850
2890
2943
2896
3037
2970
2863
2957
2930
2970
2931
3037
2796
2997
2876
2970
3011
2943
2943
2984
3051
2984
2997
2976
3051
2876
dtr. P5
85
40
127
129
-52
25
132
33
25
20
67
129
20
8
24
5
-31
-43
52
-9
14
6
-7
20
52
5
Table 2: continue.
UHM
UI
Str.
mm
%
g/tex
3.3
0.92
37.2
88.8 46.4
3.2
0.89
36.2
87.7 46.6
3.1
0.89
37.1
87.4 45.7
3.3
0.93
36.5
88.8 46.8
3.2
0.89
36.8
87.1 46.2
3.2
0.92
38.0
88.8 45.3
3.2
0.90
38.1
88.8 45.5
3.1
0.92
37.0
88.3 49.2
3.1
0.91
36.9
88.0 46.7
3.1
0.91
36.5
87.5 47.2
Mean
3.2
0.91
37.0
88.1 46.6
Max
3.3
0.93
38.1
88.8 49.2
Min
3.1
0.89
36.2
87.1 45.3
3.9
0.93
35.8
87.5 47.6
3.9
0.96
36.5
86.5 46.7
4.0
0.94
36.5
88.8 45.3
4.1
0.95
35.8
87.2 46.2
4.0
0.96
36.5
87.1 46.4
4.0
0.94
35.5
87.7 45.9
4.1
0.95
35.9
87.2 47.5
4.1
0.94
36.2
86.8 44.5
4.0
0.94
36.1
88.4 44.8
4.1
0.94
35.8
88.7 46.0
Mean
4.0
0.95
36.1
87.6 46.1
Max
4.1 0.96
36.5 88.8 47.6
Min
3.9 0.93
35.5 86.5 44.5
P1: Predicted skein strength using equation 1
P2: Predicted skein strength using equation 2
P5: Predicted skein strength using SCI data
{G84×(G70×G51B)}×P62
G77XPs6
Genotype
Mike
Mat.
Elon Rd
dtr.P1
.+b SCI Deter.
%
%
P1
6.8
65.9 11.3 229 3100 3099
1
6.4
64.7 11.6 228 3100 3053
47
6.3
64.0 11.7 225 3100 3069
31
6.6
66.7 11.7 229 3060 3083 -23
6.3
65.5 11.1 220 3070 3039
31
6.0
65.7 11.7 228 3100 3111 -11
6.2
66.9 11.5 230 3100 3115 -15
6.3
64.4 11.6 235 3150 3213 -63
6.7
66.0 11.5 227 3100 3101
-1
6.4
65.7 11.6 226 3120 3090
30
6.4
65.6 11.5 227 3100 3097
25
6.8
66.9 11.7 235 3150 3213
63
6.0
64.0 11.1 220 3060 3039
1
6.2
76.4 8.8 224 2980 2892
88
6.4
76.4 8.8 218 2950 2862
88
6.5
74.5 8.7 223 2965 2872
93
6.8
75.3 8.8 216 2925 2813 112
6.0
76.4 9.0 219 2910 2857
53
6.0
75.1 8.8 218 2915 2817
98
6.2
74.3 9.3 219 2990 2874 116
6.7
73.6 8.4 209 2815 2762
53
6.3
76.2 8.8 220 2850 2817
33
6.2
73.0 9.0 221 2960 2861
99
6.3
75.1 8.8 219 2926 2843
83
6.8 76.4 9.3 224
2990 2892 116
6
73 8.4 209
2815 2762
33
P3: Predicted skein strength using equation 3
P4: Predicted skein strength using equation 4
P2
3059
3001
3016
3040
2998
3070
3082
3166
3060
3046
3054
3166
2998
2916
2888
2887
2828
2880
2830
2890
2772
2838
2863
2859
2916
2772
dtr.P2
41
99
84
20
72
30
18
-16
40
74
49
99
16
64
62
78
97
30
85
100
43
12
97
67
100
12
P3
3080
2994
3028
3099
2991
3156
3098
3218
3096
3096
3085
3218
2984
2897
2958
2879
2821
2952
2853
2888
2763
2851
2859
2872
2958
2763
dtrP3
20
106
72
-39
79
-56
2
-68
4
24
47
106
2
83
-8
86
104
-42
62
102
52
-1
101
64
104
1
P4
3117
3011
3053
3121
3008
3154
3101
3239
3127
3114
3105
3239
3008
2877
2913
2854
2815
2889
2820
2878
2752
2806
2849
2853
2913
2752
dtr.P4
-17
89
47
-61
62
-54
-1
-89
-27
6
45
89
1
103
37
111
110
21
95
112
63
44
111
83
112
21
P5
via SCI
2997
2977
2960
2997
2966
2984
3011
3078
2970
2987
2986
3078
2960
2930
2850
2917
2823
2863
2850
2863
2729
2876
2890
2859
2930
2729
dtr. P5
103
123
140
63
134
116
89
72
130
133
119
140
63
50
100
48
102
47
65
127
86
-26
70
72
127
26
skein strength
Actual
P1
P2
P3
P4
P5
2500
2400
2300
2200
2100
2000
1900
1800
1700
1600
1500
1400
1300
1200
1100
1000
G 80
G 90
G 90×Aus.
{G 83(G 75×8544)}G 80
Fig.1 Determined and predicted skein strength for 40’s carded yarns spun from Upper Egypt
cottons.
Skein strength
Actual
P1
P2
P3
P4
P5
3200
3000
2800
2600
2400
2200
2000
1800
1600
1400
1200
1000
G 86
10229XG 86
G 88
G 92
G 77XPs6
{G 84×(G 70×G 51B)}×P62
Fig.2 Determined and predicted skein strength for 60’s carded yarns spun from Delta LS & ELS
Egyptian cottons
y= 1340.6+5.5319x r = 0.86
y = 1040.8-17.969x r = 0.95
3300
Determined skein strength
Determined skein strength
2400
2350
2300
2250
2200
2150
2100
2050
2000
3100
3000
2900
2800
2700
2600
2500
2400
2300
1950
1900
100
3200
110
120 130
140
150
SCI
160 170
180
190
2200
190 195 200 205 210 215 220 225 230 235 240
SCI
Upper Egypt cottons data
Delta LS & ELS cottons data
Fig.3 Regression equations and correlation coefficients for SCI and determined skein strength of
Upper Egypt and Delta LS & ELS cottons.