The technological value determination of some long staple Egyptian cotton varieties by using some mathematical analysis models

 The technological value determination of some long staple Egyptian cotton varieties by using some mathematical analysis models

A. F. Younes ¹, M. S. Salem ^2,*, E. A. Mesbah ², I. A. Ebaido ², and A. A. Hassan ³

¹ Grading Section. Cotton Research Institute, Agriculture Research Center, Giza, Egypt.

² Agronomy Department, Faculty of Agriculture, Al-Azhar University, Cairo, Egypt.

³ Spinning Department Cotton Research. Institute, Agriculture Research Center, Giza, Egypt.

*Corresponding author E-mail: Mohamed sherien 205@azhar.edu.eg (A. Younes)

ABSTRACT

This study was carried out in the Laboratories of Cotton Grade Section, Cotton Research Institute, Agricultural Research center, Giza, Egypt and spinning unit of Industrial Menia EL- Kameh, EL- Sharkia Governorate, Egypt in 2016 and 2017 seasons, to investigate the relationships between fiber properties and lea product, single yarn strengths and unevenness at 40’s and 60’s yarn count at 3.6 twisting index for Giza 86,Giza90 and Giza95 cotton varieties, with using FQI, SCI, PDI and MI_AHP as mathematical models. The technological values were estimated by four models correlated positively with high significant and each of lea product and single yarn strengths, in both seasons. On the contrary, this correlation was negative with the regular percentage for Giza 86 and Giza 95 varieties, SFC, UHML, FS and the Mic value had the highest contribution toward yarn quality properties at 40’s and 60’s Y.C for the studied varieties.

Keywords: Cotton, Long staple Egyptian cotton var., Technological value - Statistical Models analysis.

INTRODUCTION

Cotton is a natural fiber that have great diversity in its properties. Most of these properties play an important role in predicting and determining the spinning characteristics. Spinning cotton fiber is considered one of the most important operations to produce the yarns. Its stage depends on multiple steps that require time-consuming, the efficiency of spinning machines that differed from one factory to another and modern or old fashion for the techniques as well as the used machines. Therefore, several researches are directed to eliminate these obstacles and make the best use of statistical approaches and mathematical prediction equations to short cut the long period for spinning cotton fibers and make the decision for the superiority of multiple model equations under the study that differed between cotton species and production location through creating relationship between fiber properties and yarn quality that are represented by regression and correlation equations which are angle stone for these prediction models. El-Mogazhy et al. (1990). suggested a Premium-Discount index (PDI) through developing statistical approach that depends on model relating fiber to yarn properties. Majumdar. et al. (2005). compared three traditional methods to determine technological value of cotton fiber. These methods were Fiber Quality index (FQI), the Spinning Consistency index (SCI) the Premium-Discount index (PDI) and the new method that has been proposed based on Multiple-Criteria Decision-Making (MCDM) technique. They found that the decision-maker plays a key role in determining the criteria weights in the proposed multiplicative AHP method. They indicated that the Premium-Discount Index method shows maximum rank correlation between the technological value of cotton and yarn tenacity. Also, Ureyen and Kadoglu (2007), indicated that the relationship between yarn properties as dependent variables and fiber traits as indecent variable are nearly linear for each yarn property. Therefore, we choose multiple linear regression. Hager et al. (2011), found that the Fiber strength and Fineness were the most effective fiber properties to predict yarn properties for the category of Extra-long staple under ring spinning system, while, the Upper half mean length, Fiber strength and Maturity had the greatest influence on the studied yarn properties for the long staple cotton category. Fares and Hassan (2015) found that all the supposed models of regression were significant and they reflected large part of variation of the studied yarn properties. Mesbah and Hassan (2016), published that there were positive highly significant correlation between Single yarn strength and each of Upper half mean, Fiber strength and Fiber elongation %. On the same trend, between yarn evenness and most studied fiber traits, at 80’s, 100’s, 120’s and 140’s yarn count for Extra-long staple Egyptian cotton varieties, on the same line, Abdel Daim et al. (2020) indicated that Fiber strength, Upper half mean length, Uniformity index and short fiber index plays an important role in determining technological value of cotton, while, the fiber fineness and fiber elongation had low influence under a Multi Criteria Decision Making (MCDM). Consequently, this study aimed to determine the technological value or to illustrate the relation between fiber properties and yarn quality properties of some long staple Egyptian cotton varieties by using some models of analysis.

MATERIAL AND METHODS

This investigation was conducted during 2016 and 2017 seasons at cotton Grade Section, cotton Research Institute, Giza, Egypt to study the relationship between fiber properties and yarn quality traits (Lea product, Single yarn strength “cN/tex” and Unevenness “cV%”) at 40’s and 60’s yarn count for three long Egyptian Cotton varieties (Giza 86, Giza 90 and 95), in the presence of 3.6 twisting index through the ring spinning system. These treatments were arranged in completely randomized design with factorial analysis in the presence of three repetitions samples for each grade ( FG, G, FGF and GF), also variety under the study to analyze the effects of spinning variables as well as their interactions on yarn  quality properties. The obtained data were subjected to statistical analysis according to the procedure outlined by Snedecore and Chocran (1981). The different data means of each variety were analyzed separately. Least significant difference at 5% probability level was used for comparing the different means. Eventually, it could be compared between the resulted cotton yarn properties from traditional spinning method and the value estimated by prediction equations as well as the correlation or approaches between them.

The studied traits:

These traits were determined by weighting 10 g from each sample, grade and variety.

Fiber quality properties:

were determined by using Cotton Classification System (CCS) and High-Volume Instrument (HVI)as obtainedEbaido et al. (2017).

All fiber tests were carried out at the Grading Section. at cotton Res. Inst., Agric. Res. Center, under controlled conditions of 65% ± 2° relative humidity and temperature of 20 ± 2°, where cotton spinning process as ring system (5 kg for each sample) on two yarn counts (40’s and 60’s) was detected in spinning unit of Industrial Menia El-Kameh El-Sharkia Governorate, Egypt.

Yarn quality properties:

To study the yarn quality traits, carded yarns of 20 texliner density at twist factor (3.6) were spun from long staple Egyptian cotton varieties (Giza 86, Giza 90 and Giza 95) to determine the following yarn properties:

Lea product, (Y₁). It was measured by using Good Brand Lea tests according to ASTM (D-1598-93Roo).

Single yarn strength, (Y₂). It was estimated by using Uster Automatic, where 120 breaks were taken from the tested samples, according to ASTM (D-2256-67).

Yarn Unevenness (cV%), (Y₃). It was calculated by using Uster tester III, according to ASTM (D-2256-67).

Determination of yarn quality by using the prediction equations for different varieties under study: This investigation concluded four models as follow:- 

Fiber quality index (FQI):-

This model had been chosen for its newless and simplicity.

FQI = UHM x UI x STRf x (1+EL) x (1-SF)/ MIC

El-Messiry and Abd- Ellatif (2013).

Spinning Consistency Index (SCI):-

It is a linear regression equation that included the most HVI measurements for calculating the prediction of the quality and spin ability of the cotton fiber  

SCI = - 414.67 + 2.9 STR+ 49.17 UHML+ 4.74 UI - 9.32 Mic + 0.95

Rd% + 0.36 (+b) (Uster, 1999).

Premium Discount Index (PDI):-

It is a linear equation that contains the difference factor (D) for the fiber properties (UHML, F.S, F.FL, UI, SFC and MIC) and the standardized   ⁽⁽β⁾⁾ Coefficient value (22.15, - 4.75, - 4.37, 11.19, - 20.68, - 7.8) for each fiber properly.

PDI= 22.15 x STR - 4.75 x EL - 4.37 x UHML+ 11.19 x UI - 20.68 xSFC-7.8 x MIC. (Majumdar et al. 2005)

Multiplicative Analytic Hierarchy Process MCDM or (MI_AHP)

It consists of fractional exponential equation included fiber properties (F.STR, F. EL, UHM, UI, MIC. And SFC.). The exponent for each fiber property defines its importance in quality of cotton fiber as follow:-

MIAHP=

RESULTS AND DISCUSSION

Table (1): Exhibited the fiber properties through the four studied grades for (Giza 86, Giza 90 and Giza 95) varieties during 2016 and 2017 seasons that were considered as inputs for the results of the prediction equations.

Coefficient of correlation between the four studied models equation and yarn quality traits under the two yarn counts for some long staple Egyptian cottons varieties manufactured by ring spinning system in 2016 and 2017 seasons are presented in Table (2).

Considering Giza86 cultivar results in Table (2) indicated that there was positive and highly significant rank correlation (0.977** and 0.955**) between the application of MI_AHP model equation and Lea product in both seasons., respectively.

As for Single Yarn Strength (SYS) its value was positive and highly significant correlated (0.990** & 0.973**) with MI_AHP models in 2016 and 2017 season. On the other hand, there was a negative highly significant correlation between Giza 86 variety (-0.945**and-0.979**) and MI_AHP model equation due to Unevenness property in the two seasons.

With respect to Giza 90 var., the values estimated by PDI, SCI and FQI models equation gave the highest significant correlation (0.990**and 966**) with Lea product in the first and second seasons, respectively. On the same trend, MI_AHP model equation gave the highest positively correlation (0.971** and 0.950**) with Single Yarn Strength (SYS) in both seasons. On the other hand, there was a negative highly significant (- 0.944**and- 0.944**) between MI_AHP model equation and Unevenness property.

For Giza 95 var., PDI and MI_AHP models equation gave positive highly significant correlation (0.947**, 0.981**, 0.954** and 0.966**) with Lea product and Single Yarn Strength (SYS) traits, respectively. On the other hand, there was a negative highly significant correlation (- 0.933** and - 0.934) between the application of MI_AHP model and yarn Unevenness trait in the two seasons.

From Table (2) it could be indicated that (FQI) model equation gave the lowest correlation values for Lea product and Single Yarn Strength traits in both seasons. These results were in agreement with those obtained by El-Mogazhy et al. (1990) who found that the maker plays role indicator mining the criteria weights in the proposed multiplicative MI_AHP model equation. They also indicated that (PDI) model equation shows the maximum rank correlation between technological value of cotton and yarn tenacity. Ureyen and Kadoglu (2007) also found that the correlation between yarn properties as independent variable and fiber traits as indecent variable are nearly linear for each yarn property.

The results of multiple linear regression analysis between Lea product, Single Yarn Strength as well as Unevenness (cV%) under two yearn counts (40’s and 60’s) (depended variable) and long Egyptian cotton fiber properties under study (explanatory variables) are presented in Tables (3 and 4) in 2016 and 2017 seasons.

The results indicated that the supposed multiple regression models were significantly contributed the most variability of the three above mentioned yarn properties. Statistically, goodness of fit was satisfied for the three multiple regression models for each yarn property, each cotton variety under the study and for each yarn count, where more than 80% of Lea product, Single Yarn Strength and Unevenness (cV%) explained as R²% was attributed to the fiber properties for the two yarn count. Also, it was interesting to note the negative relation between above studied yarn properties and some of fiber ones that differed between the varieties in the first season, the contribution of the most fiber properties in yarn quality traits was significant, with the exception of

 Y₁M₁ = 58083.354 - 851.799 UHM - 413.319 UI - 443.856 SFC+ 6483. 121Mic - 25821.81MR - 98.611FS + 476.717 El.,

That was insignificant contribution (0.168) at 40’s yarn count for Giza95 var., same trend had been detected at 60’s yarn count with the exception of

Y₃M₃=122.428-0.076UHM-1.707UI-0.016SFC+ 10.920 Mic -15.096MR - 257 FS + 2.376 El.,

Which was insignificant contribution (0.129) for Giza 95 var. during 2016 season.

As for 2017 season, at 40’s yarn count, all models equations, that contained some of fiber properties are significant contribution, where R²% ranged between 0.912 up to 0.996% with the exception of Y₃M₃, which ,the level of significance was (0.052) for Giza 95 var.

On the other hand, at 60’s yarn count, all the studied models are characterized by significant contribution, for example due to lea product with its equation Y₁M₁ had the highest value (0.987%) for Giza 90 var., while, the model equation Y₂M₂ for SYS property was the highest value (0.977%) for Giza 86 var., with addition to, cV% for Giza 95 var. its R²% was (0.964) for Y₃M₃ model equation. The residuals content (1-R²%) may be due to some errors during measuring the fiber and yarn properties, that some fiber properties were not into account under the current investigation and or unknown variation (random error). These results are in agreement with Hager et al. (2011), Fares and Hassan (2015) and Abdel Daim et al. (2020). They indicated that Upper Half Mean Length (UHML), Fiber Strength (FS), Uniformity index and Short Fiber index play an important role in determining technological value of cotton under Multi Criteria Decision Making (MCDM) model.

Stepwise multiple regression parameters of Lea product, Single yarn strength and Unevenness (cV%) using seven fiber properties for some long staple Egyptian cotton varieties (Giza 86, Giza 90 and Giza 95) manufactured by ring spinning system are presented in Tables (5 up to 8).

From these Tables, we can get the available or suitable equation to determine (R²%) and rank of contribution of the studied fiber traits to Lea product, Single yearn strength and Unevenness (cV%) within 40s yarn count for long staple varieties.

as for the stepwise multiple linear regression, results in Tables (5 and 6) showed that the accepted limiting properties of cotton fibers were significantly accounted for most variation of Lea product (Y₁) at 40’s count of Giza 86 var. they were Short fiber content (SFC) (X₃), Fiber strength (FS) (X₆) and Fiber elongation (FEL) (X₇), that the value of coefficient of determination R² for these trait was 0.983, as well as, same the traits rank of contribution due to Lea product (Y₁) for Giza 90 var., the highest value for coefficient of determination (R²%) was (0.986), while, most of the traits that contributed in lea product of Giza 95var. were X₁, X₂ and X₄, that the maximum value of (R²%) was (0.932), during 2016 season with model 1 equation.

On the same trend, contributors’ fiber traits are important due to Single yearn strength (Y₂) at 40’s yarn count for Giza 86 var. were X₃, X₅ and X₆, had the highest value of (R²) being (0.993) while fiber traits are important for Single yarn strength for Giza 90 var. were X₁, X₄ and X₇, that the maximum value of (R²) that was (0.973), as well as, contribution traits for Giza 95 var. that was the same traits for Giza 90 var. That (R²) value was (0.982) in 2016 season with model 2 equation.

With regard to the important contributor fiber traits due to Unevenness (cV%), (Y3) at 40’s count for Giza 86, Giza 90 and Giza 95 varieties they were Upper Half Mean Length (UHML) (X₁), short fiber content (SFC) (X₃), Micronair value (Mic) (X₄), length uniformity  % (U%) (X₂), Fiber strength (FS) (X6) and Fiber elongation % (FE) (X₇), that the maximum values for (R²) were (0.981, 0.905 and 0.857) respectively with model 3 equation through 2016 season.

Concerning the stepwise multiple linear regression in 2017 season, results in Table (8) indicated that limiting properties of cotton fiber is characterized by a significant contribution for the studied yarn quality traits.

The important fiber traits contribution due to Lea product (Y₁), at 60’s count for Giza 86, Giza 90 and Giza 95 varieties were Short Fiber Content (SFC) (X₃), Upper Half Mean Length (UHM) (X₁), Micronair value (MIC) (X₄), Maturity ratio (MR) (X₅) and Fiber elongation (FE%) (X₇), that the highest values for (R²%) were 0.931, 0.981 and 0.966, respectively with the application of model I equation.

On the same trend, contribution fiber traits for Single yarn Strength (SYS), (Y₂) at 60’s yarn count for the three tested varieties were Short Fiber Strength (X₃), Upper Half Mean Length (X₁), Micronair value (X₄), Uniformity% (X₂) and Fiber elongation % (X₇), respectively, that the maximum values for (R²%) of the studied varieties were 0.961, 0.941 and 0.970, respectively with model 2 equation in the second season.

 With respect to the important contributors’ fiber traits for Unevenness (cV%), (Y3) at 60’s yarn count for Giza 86, Giza 90 and Giza 95 var. they were Short Fiber Strength (X₃), Upper Half Mean Length (X₁), Micronair value (X₄), Uniformity % (X₂) and Maturity ratio (X₅), that the maximum values for (R²%) of the studied varieties were 0.914, 0.901 and 0.944, respectively, with model 3 equation in 2017 season. These results were in agreement with Fares et al. (2010), Hager et al. (2011), Fares and Hassan (2015) and Mesbah and Hassan (2016). They indicated that Upper Half Mean, Fiber Strength, Fiber elongation and Maturity percent were the most effective fiber traits to predict yarn quality properties and technological value for long staple on different yarn counts. Results showed that all the supposed models of regression were significant and reflected large part of the variation of studied yarn traits expressed as high values of R² and near values of the corresponding adjusted R² indicating the validity and goodness of fit for these models.

CONCLUSION

Most of the fiber properties i.e. Upper Half Mean Length, Fiber strength, Maturity percentage and Short fiber content (%) contribute significantly towards cotton yarn quality under study, by using four mathematical models i.e. FQI, PDI, SCI and MI_AHP. Previous fiber properties were estimated for the long Egyptian cotton varieties and were considered as inputs to calculate their equations and detected the comparison between the prediction equation results and the obtained ones from ring spinning at 40’s and 60’s yarn count. The results of prediction equations rely on rank correlation matrix, multiple liner regression analysis and stepwisemultiple linear regression analysis that cleared the main fiber properties contribution and the rate of contribution (R²%) that differed with varietal difference. for example, it was found that the main fiber property towards Lea product was UHML for Giza86 var. whereas, it was SFC for Giza 90 var. R²% also varied from one variety to another, being 0.9810% for Giza 86 var. and 0.9549% for Giza 90 var.

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Table 2: Rank correlation matrix between the four models’ equations and lea product (LP), Single yarn strength (SYS) and yarn unevenness (cV%) for Giza86, Giza90and Giza95 (long Egyptian cotton varieties) during 2016 and 2017 seasons.

Table 3: Multiple linear regression analysis of lea product, Single yarn strength (cN/tex) and Unevenness (cV%) at 40’s and 60’s yarn count for Giza 86,Giza90 and Giza 95 varieties during 2016.

Table 4: Multiple linear regression analysis of lea product, Single yarn strength (cN/tex) and Unevenness (cV%) at 40’s and 60’s yarn count for Giza 86, Giza90 and Giza 95 varieties during 2017.