Effect of Hydrogen Peroxide Concentration on 100% Cotton Knit Fabric Bleaching

Ferdush, Jannatul; Nahar, Kamrun; Akter, Tarifun; Ferdoush, Most. Jannatul; Jahan, Nusrat; Iqbal, Shakeel

doi:10.19044/esj.2019.v15n33p254

Cited by 4 publications

(7 citation statements)

References 3 publications

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“…Many studies reported that WI of cotton fabric can be increased by increasing H 2 O 2 concentrations and T (Abdul and Narendra 2013;Ferdush et al 2019). Nevertheless, high concentrations of H 2 O 2 could break up the unsaturated bonds (e.g., C=C) and decreases the bursting strength of cotton fabric (Ferdush et al 2019;Haque and Islam 2015;Tang et al 2016). Hence, optimum bleaching process parameters are required to determine the effectiveness of the bleaching process to produce a targeted cotton fabric whiteness.…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

“…The higher the WI, the greater the whiteness degree of the measured cotton and vice versa (Topalovic et al 2007). If the preferred white fabric has a high reflectance, then the ideal reflectance for textile materials should approach 100 (Ferdush et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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

Yeo

Lau

2021

Cellulose

View full text Add to dashboard Cite

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“…Whiter cotton fabric is desired before it is dyed, printed or other wet-treatments (Ferreira et al 2019;Kabir et al 2014). Many studies have reported that WI of cotton fabric can be increased by H2O2 concentrations and T (Abdul and Narendra 2013;Ferdush et al). Nevertheless, the high concentrations of H2O2 could break up the unsaturated bonds, like C=C, and then decreases the bursting strength of cotton fabric (Ferdush et al ;Haque and Islam 2015;Tang et al 2016).…”

Section: Resultsmentioning

confidence: 99%

“…Many studies have reported that WI of cotton fabric can be increased by H2O2 concentrations and T (Abdul and Narendra 2013;Ferdush et al). Nevertheless, the high concentrations of H2O2 could break up the unsaturated bonds, like C=C, and then decreases the bursting strength of cotton fabric (Ferdush et al ;Haque and Islam 2015;Tang et al 2016). Hence, the optimum bleaching process parameters are required to determine the effectiveness of the bleaching process to produce a targeted cotton fabric whiteness.…”

Section: Resultsmentioning

confidence: 99%

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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“…The antimicrobial activities of hydrogen peroxide can be used to inhibit bacteria, viruses, fungi, including moulds, and it can also be found in honey, acting as a preservative [79]. Additionally, certain concentrations of H 2 O 2 are often used to remove staining on textiles, odour and improve textile deterioration [120]. Such finishes can accomplish antimicrobial effects in various textile products, which include apparel, upholstery and automotive industries [79,121].…”

Section: Hydrogen Peroxidementioning

confidence: 99%

The Future of Functional Clothing for an Improved Skin and Textile Microbiome Relationship

2021

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The skin microbiome has become a hot field of research in the last few years. The emergence of next-generation sequencing has given unprecedented insights into the impact and involvement of microbiota in skin conditions. More and more cosmetics contain probiotics or bacteria as an active ingredient, with or without scientific data. This research is also acknowledged by the textile industry. There has been a more holistic approach on how the skin and textile microbiome interacts and how they influence the pH, moisture content and odour generation. To date, most of the ingredients have a broad-spectrum antibacterial action. This manuscript covers the current research and industry developments in the field of skin and textiles. It explores the nature of antimicrobial finishing in textiles which can disrupt the skin microbiome, and the benefits of more natural and microbiome friendly therapies to combat skin conditions, malodour and skin infection.

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Effect of Hydrogen Peroxide Concentration on 100% Cotton Knit Fabric Bleaching

Cited by 4 publications

References 3 publications

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

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

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

The Future of Functional Clothing for an Improved Skin and Textile Microbiome Relationship

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