Prediction of whiteness index of cotton using bleaching process variables by fuzzy inference system

Haque, Abu Naser Md Ahsanul; Smriti, Shamima Akter; Hussain, Manwar; Farzana, Nawshin; Siddiqa, Fahmida; Islam, M. T.

doi:10.1186/s40691-017-0118-9

Cited by 12 publications

(22 citation statements)

References 21 publications

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“…As evidenced by visual perception, it is noted that curaua fibers WI showed to be satisfactory and was meaningly higher than red pineapple fibers (≈ 50% superior). Comparing with hydrogen peroxide bleached cotton whiteness index values found in literature [33][34][35], ranging from 47 to 72, the red pineapple fibers whiteness index was lower. Figure 10 shows the visual aspect of dyed PALF samples.…”

Section: Whiteness Index (Wi)mentioning

confidence: 57%

New Brazilian pineapple leaf fibers for textile application: cottonization and dyeing performance

et al. 2019

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This work evaluates the dyeing performance associated with a cottonization and bleaching pretreatment of pineapple leaf fibers from two different species from Brazil: curaua (A. lucidus syn. A. erectifolius) and red pineapple (A. bracteatus (Lindl.) Schult. & Schult.f ), aiming its application in textile materials for garments. Treated and untreated fibers were compared for their morphological, chemical, physical and color properties. The fibers underwent changes in their chemical and physical structural characteristics after pretreatment. The results of SEM, FTIR and whiteness index analysis indicated that the cottonization process improved the color and surface uniformity of fibers, reducing non-cellulosic matters, such as lignin and pectin. Treated fibers also presented low impurities content, i.e., oils, wax and fats (between 1.1 and 2.1%). However, compared to untreated fibers, treated curaua show an increase in impurities, supposedly caused by the removal of epidermal tissue from the fiber surface, which became the fiber bundle structure more accessible. Cottonization process also produced thinner, individualized and more resistant fiber bundles, which was evidenced by a decrease in linear density by 30 and 42% and an increase in tenacity by 12 and 23%. The dyeing behavior of fibers was investigated using blue, red and yellow reactive dyes. Fibers of both species demonstrated good dyeing behavior, with K/S values compatible with textile application standards (between 4 and 11) and an excellent color fastness to laundering.

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Section: Whiteness Index (Wi)mentioning

confidence: 57%

New Brazilian pineapple leaf fibers for textile application: cottonization and dyeing performance

et al. 2019

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“…In this study, the collected experimental data was taken from Haque et al (2018), and Haque and Islam (2015). In their studies, single jersey cotton knitted fabric of 130 grams per cubic centimeter was used as the fabric samples and a 12.5 g of fabric sample with a 1:10 material liquor ratio was treated in each bleaching time.…”

Section: Bleaching Process Of Cotton Fabric and Whiteness Indexmentioning

confidence: 99%

“…The CIE WI under CIE 1964 10 o standard observer can be represented by the Eq. 1 (Haque et al 2018;Jafari and Amirshahi 2008).…”

Section: Bleaching Process Of Cotton Fabric and Whiteness Indexmentioning

confidence: 99%

“…A total of 56 bleaching data sets which consists of H2O2 concentration, temperature, time of bleaching, and the WI of the cotton fabric were adopted from Haque et al (2018). In this study, NT, N1, N2, and latent variable (LV) represent the total numbers of data sets, numbers of training data sets, numbers of testing data sets, and number of LV, respectively.…”

Section: Regression Models Parameters Settingmentioning

confidence: 99%

“…However, these models require many data for model parameters optimisation, and they have computational time burdens. Later, a fuzzy predictive model had been developed and studied by Haque et al (2018) using a fuzzy logic designer app in MATLAB to predict the WI of cotton using the bleaching process parameters that are nonlinear. Nevertheless, this fuzzy model is unable to predict the WI for the bleaching process parameters that are not within the ranges of the input data.…”

Section: Introductionmentioning

confidence: 99%

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Least Square Support Vector Regression-Based Model for Whiteness Index of Cotton Fabric Prediction

Yeo

2021

Preprint

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The textile bleaching process uses a hydrogen peroxide (H2O2) solution in alkali pH associated with high temperature is the commonly used bleaching procedure in cotton fabric manufacture. The purpose of the bleaching process is to remove the natural colour from cotton to obtain a permanent white colour before dyeing or shape matching. Normally, the visual ratings of whiteness on the cotton are measured by the whiteness index (WI). Notice that lesser research study is focusing on chemical predictive modelling of the WI of cotton fabric than its experimental study. Predictive analytics using predictive modelling can forecast the outcomes that can lead to better-informed cotton quality assurance and control decisions. Up to date, limited study applying least square support vector regression (LSSVR) based model in the textile domain. Hence, the present study was aimed to develop the LSSVR-based model, namely multi-output LSSVR (MLSSVR) using bleaching process variables to predict the WI of cotton. The predictive accuracy of the MLSSVR model is measured by root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2), and its results are compared with other regression models including partial least square regression, predictive fuzzy model, locally weighted partial least square regression and locally weighted kernel partial least square regression. The results indicate that the MLSSVR model performed better than other models in predicting the WI as it has 60–1209% lower values of RMSE and MAE as well as it provided the highest R2 values which are up to 0.9985.

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Predicting the whiteness index of cotton fabric with a least squares model

Yeo

Lau

2021

Cellulose

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The textile bleaching process that involves hot hydrogen peroxide (H 2 O 2 ) solution is commonly practised in cotton fabric manufacture. The purpose of the bleaching process is to remove color from the cotton, achieving a permanent white before proceeding to dyeing or shape matching. Normally, the visual ratings of whiteness on the cotton are measured based on whiteness index (WI). However, it is found that there is little research on chemical predictive modelling of the cotton fabric's WI compared to experimental study. Analytics using predictive modelling can forecast the outcomes, leading to better-informed cotton quality assurance and control decisions. Up to date, there is limited study applying least square support vector regression (LSSVR) model in the textile domain. Hence, the present study aims to develop a multi-output LSSVR (MLSSVR) model using bleaching process variables and results obtained from two different case studies to predict the WI of cotton. The predictive accuracy of the MLSSVR model was measured by root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R 2 ). The obtained results were compared with other regression models including partial least square regression, predictive fuzzy model, locally weighted partial least square regression, and locally weighted kernel partial least square regression. Our findings indicate that the proposed MLSSVR model performed better than other models in predicting the WI as it showed significantly lower values of RMSE and MAE. Furthermore, it provided the highest R 2 values which are up to 0.9999.

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Prediction of whiteness index of cotton using bleaching process variables by fuzzy inference system

Cited by 12 publications

References 21 publications

New Brazilian pineapple leaf fibers for textile application: cottonization and dyeing performance

New Brazilian pineapple leaf fibers for textile application: cottonization and dyeing performance

Least Square Support Vector Regression-Based Model for Whiteness Index of Cotton Fabric Prediction

Predicting the whiteness index of cotton fabric with a least squares model

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