Bio-Surfactant for Sustainable Textile Processing

Indian Journal of Fibre and Textile Engineering (IJFTE) ISSN: 2582-936X (Online), Volume-5 Issue-2, November 2025 1 Published By: Lattice Science Publication (LSP) © Copyright: All rights reserved. Retrieval Number:100.1/ijfte.A242205021125 DOI:10.54105/ijfte.A2422.05021125 Journal Website: www.ijfte.latticescipub.com Bio-Surfactant for Sustainable Textile Processing Sangeeta Patil, Ashok Athalye Abstract: Enforcing environmental preservation and addressing climate change for future generations presents various issues for modern society. Microorganisms produce a family of surfaceactive molecules called biosurfactants, which have garnered considerable interest due to their environmentally beneficial properties and potential to replace synthetic surfactants in various industries. Traditional textile wet processing, which includes pretreatment, dyeing, and finishing, often employs synthetic surfactants that raise concerns regarding their biodegradability, toxicity, and potential for water pollution. On the contrary, biosurfactants are biodegradable, non-toxic, and can work in various environmental circumstances, providing a sustainable option. Among the different types of surfactants, glycolipids are particularly highlighted, with sophorolipids (SLs) being lowmolecular-weight compounds that have garnered significant attention. This study compares the effectiveness of SLs with conventional surfactants in various textile wet processing steps, such as pretreatment and dyeing. According to the findings, SLs not only outperform their synthetic counterparts in terms of surfactant qualities (wetting, emulsifying, and foaming) but also lessen the process's total environmental impact. The results suggest that incorporating SLs into textile wet processing can facilitate the industry's transition to more sustainable technologies, leading to greener production methods. Keywords: Bio-based Surfactant, Bio-degradable, Cotton Processing, Environmental Impact, Effluent Treatment Abbreviations: SLs: sophorolipids FTIR: Fourier Transform Infrared HPLC: High-performance Liquid Chromatography ANOVA: Analysis of Variance I. INTRODUCTION The textile industry is one of the largest and most resource-intensive sectors globally, consuming vast amounts of water, energy, and chemicals during various wet processing stages. Conventional textile processing methods heavily rely on synthetic surfactants to achieve desirable fabric properties, such as wettability and detergency [1]. However, the widespread use of artificial surfactants poses significant environmental challenges, as many of these substances are not biodegradable. Manuscript received on 10 June 2025 | First Revised Manuscript received on 21 July 2025 | Second Revised Manuscript received on 16 October 2025 | Manuscript Accepted on 15 November 2025 | Manuscript published on 30 November 2025. *Correspondence Author(s) Sangeeta Patil*, Department of Textile Fibres and Science, Institute of Chemical Technology, Runwal Garden, Tower 12, Flat No 0304, Dombivali (Maharashtra), India. Email ID: [email protected], ORCID ID: 0009-0002-0712-0147 Prof. Dr. Ashok Athalye, Department of Textile Fibres and Science, Institute of Chemical Technology, ITC, Matunga (Maharashtra), India. Email ID: tch21s[email protected]mbai.edu.in © The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) chemicals are non-biodegradable and toxic, contributing to water pollution, soil contamination, and ecological degradation. In response to the increasing demand for sustainable practices in textile manufacturing, biosurfactants have emerged as promising alternatives due to their environmentally friendly characteristics. Biosurfactants, derived from microbial sources, provide eco-friendly alternatives to synthetic surfactants. They exhibit low toxicity and biodegradability and are gaining traction across various industrial applications. The frequent use and subsequent release of this surfactant into the environment have become a growing concern. In recent years, there has been a significant increase in interest in microbial surfactants due to their unique properties and diverse range of applications in various environmental contexts. Biosurfactants are surface-active agents produced by microorganisms, classified into different types based on their molecular weight and structure [2]. High-molecular-weight biosurfactants encompass polymeric and particulate surfactants, often utilised in environmental applications due to their stability and biodegradability.. Glycolipids, lipopeptides, and phospholipids are examples of low molecular weight biosurfactants that are well-known for their capacity to improve emulsification and lower surface tension.SLs are regarded as natural compounds and green glycolipid surfactants. Since about 45 years ago, SLs have been recognized as biosurfactants that some yeast strains can make. They combine green chemistry with a reduced carbon footprint, making them environmentally beneficial. They exhibit low ecotoxicity and are biodegradable, making them commercially appealing [3]. This study demonstrates the application of SLs in textile wet processing applications. Among the notable characteristics that the biosurfactants display are surface tension lowering, emulsification, wetting, and foaming [4]. Surface tension reduction, emulsification, wetting, and foaming are some of the prominent properties exhibited by biosurfactants. A biologically generated chemical known as SLs-biosurfactant is produced when microorganisms (bacteria, fungi, and yeast) are cultivated in aqueous environments and provided with a carbon source as a feedstock, such as blends of carbohydrates, fats, oils, and hydrocarbons [5]. SLs, a glycolipid biosurfactant produced by yeast species such as Starmerella bombicola, have garnered interest due to their potential applications in various industries, including cosmetics, pharmaceuticals, and food processing. In recent years, their use in textile applications has been explored due to their unique surface-active properties, low toxicity, and biodegradability. SLs can be produced from renewable resources, making them an attractive alternative to petrochemical-based surfactants in textile wet processing [6]. SLs are known for their rapid breakdown in the environment, which is a crucial factor in reducing Bio-Surfactant for Sustainable Textile Processing 2 Published By: Lattice Science Publication (LSP) © Copyright: All rights reserved. Retrieval Number:100.1/ijfte.A242205021125 DOI:10.54105/ijfte.A2422.05021125 Journal Website: www.ijfte.latticescipub.com pollution. Studies indicate that SLs exhibit minimal cytotoxicity, comparable to that of conventional solvents, thereby enhancing their safety profile [7]. This study aims to investigate the application of biosurfactants from SLs in various stages of textile wet processing, including scouring, bleaching, and dyeing, with a focus on their effectiveness compared to traditional synthetic surfactants. The research will examine the impact of SLs on fabric quality, dye uptake, and colour fastness. By reviewing the performance and environmental benefits of SLs in textile applications, this study aims to contribute to the growing body of knowledge on sustainable practices in the textile industry. It provides insights into the viability of biosurfactant SLs as a greener alternative to conventional surfactants. II. EXPERIMENTAL A. Material SLs Received from Rossari Biotech Limited. Caustic flakes, sodium chloride, and soda ash are sourced from SD Fine Chem. Reactive dyes are sourced from Rossari Biotech Limited. Single jersey 30s cotton was sourced from Vijay and Sons, Tirupur. The greige cotton fabric, with a quality of 40 ComX 40 Cw / 132 x 72 - 1/1, was sourced from Gimatex. B. Wetting Test (Drave’s Test -AATCC 17) A surfactant's effectiveness is evaluated by measuring how long it takes for a standard cotton yarn skein with a standard weight to sink in a solution of a wetting agent in water. The more quickly the skein wets out, the more effective the surfactant is at doing so. C. Foaming Test The method involves manually shaking the cylinder with the Product under study and measuring the column of the resulting foam. The graduated cylinder is 2-2-250 as per GOST 1770-74, and the stopwatch is as per GOST 5072-72. Prepare a solution of 1.0 g/L of the product under study and shake the graduated cylinder with horizontal swinging motions, using an amplitude of 70 cm, for 1 minute. Then, the cylinder is put on a tabletop immediately after shaking, and the difference between the foam's surface and the emulsion's meniscus is determined, which numerically expresses the foaming tendency. Allow it to settle for 1 minute and measure the foam’s volume again to assess its settling. D. Surface Tension (Pendant Drop Method) Adding surfactant to water reduces the surface tension of water from 72 dynes/cm to approximately 30 dynes/cm. The pendant drop method, which involves finding a drop of water at the tip of a needle, is used to measure surface tension. A camera captures a picture of the drop, which is then imported into the drop shape analysis program. The first step in contour recognition is to analyze a greyscale image. Optical contact angle measuring and contour analysis systems (Model No. OCA 25 15EC) of Data Physics are used for the evaluation. E. Cloud Point (DIN EN 1890) The cloud point is the temperature at which the surfactant's solubility is significantly decreased, resulting in phase separation and making the solution cloudy. At this temperature, the surfactant separates the hydrophobic part from its soluble phase and forms micelles. These micelles aggregate, making the clear solution turbid. A higher cloud point indicates the stability of the surfactant at high temperatures, allowing it to exhibit properties under those conditions. F. Emulsifying Index The emulsification index test determines the surfactant’s emulsifying properties. The emulsification index test calculates the ratio of the height of the stable emulsion layer to the total height of the liquid generated after vortexing and allowing it to stand for 24 hours. This helps determine whether the surfactant possesses emulsifying properties. The activity of emulsification was computed by applying the formula. E24 (%) = height of the liquid layer/height of the emulsified layer. G. Specific Gravity (ASTM D792) Specific gravity is a measure of the ratio of the mass of a given volume of material at 30°C to the same volume of deionised water. H. Ionic Nature Ionic nature refers to the characteristics of a chemical compound, which determines whether a chemical is nonionic, anionic, or cationic. The ionic nature of surfactants is crucial, as it determines their performance in various applications and compliance with environmental standards. I. pH Value A Universal digital pH meter was used to check the pH of the products. Prepared a solution with 10 g of product in 100 ml of distilled water and tested for the pH value of the surfactant under study. J. Solid Content The solid content is a measure of the amount of active ingredients or non-volatile components in the liquid. It is determined by measuring the weight loss of the liquid when the volatile components evaporate. It is an essential parameter for understanding the strength of the product. K. Absorbency by Drop Test The absorbency of scoured fabric is measured using the drop test, as specified in AATCC 79. Wetting time refers to the amount of time required for a water drop to disappear from the surface of the fabric, and it is also used to determine the shape of the drop on treated fabric. L. Sinking Test The sinking test is checked by JIS 1907. The sinking time test determines how long it takes for the scoured test specimen to be completely wetted by water when placed on its surface. M. Wicking Test Standard vertical wicking test techniques, as outlined in AATCC 197, are used. The specimen's bottom comes into contact with water, and then the wicking distance is measured after 15 minutes. N. Weight Loss: The weight loss of the scoured fabric is calculated Indian Journal of Fibre and Textile Engineering (IJFTE) ISSN: 2582-936X (Online), Volume-5 Issue-2, November 2025 3 Published By: Lattice Science Publication (LSP) © Copyright: All rights reserved. Retrieval Number:100.1/ijfte.A242205021125 DOI:10.54105/ijfte.A2422.05021125 Journal Website: www.ijfte.latticescipub.com according to ISO 1383:1977 (BIS 1982). Wa and Wb are the weights of the unscoured and the scoured fabric, respectively Weight loss % = (Wa - Wb)/Wa X 100 O. TEGEWA Rating The TEGEWA scale is used to measure desizing efficiency. A tiny piece of resized cloth was cut out and submerged in an iodine solution in a beaker. 0.6358 g of iodine and 10 g of potassium iodide (KI) are combined in 100 ml of water, and the mixture is thoroughly shaken to dissolve. Crystals of iodine. After that, 800 mL of distilled water is added, followed by 1000 mL of ethanol. Following a thorough water rinse and a dab with filter paper, the fabric is instantly compared to the TEGEWA scale, which has a rating range of 1 to 9. Nearly no size removal is indicated by a grade of 1, while total size removal from the fabric is indicated by a value of 9. P. Desizing Efficiency W1 is the initial weight of the greige fabric, and note down the weight of the fabric after desizing. This is W2. This desized fabric is further desized with a Concentrated amylase enzyme at a high dosage, ensuring complete size removal. The TEGEWA rating can assess this. The TEGEW rating is 8-9, which denotes complete size removal from the fabric. Weigh the fabric after completely removing the size. This reading is W3. Total size content = W1-W3 Residual size = W2-W3 Desizing Efficiency = (Total size -Residual size)/Total size X100 Q. Whiteness Index and Colour Strength Measurement The spectrophotometer X-Rite Colour Ci7450 Model Spectrophotometer was used to measure the whiteness of scoured and colour intensity (K/S), colour indices (L*, a*, b*) values of dyed cotton fabric at the maximum wavelength. The CIE scale was used to calculate whiteness. D65 was set as illuminated at 10 standard observers for determining K/S using the Kubelka–Munk equation, K/S = (1-R)2/2R Additionally, the colored samples were assessed using L*, a*, b*, C, h, and dE. The distance between the standard or reference point and the sample points shown in the L*a*b* colour space is known as the colour difference (dE). L* values vary from 0 to 100, where 0 denotes darkness or black and 100 denotes lightness or white. Green is indicated by negative values of "a*," whereas positive values indicate red; blue is indicated by negative values of "b*," and yellow by positive values. The most widely used system today is the L*a*b* system, which was derived from the CIE in 1976. Where DL: L (standard) − L (sample), Da: a (standard) − a (sample), Db: b (standard) − b (sample). From the CIELAB coordinates, one may compute C (chroma) and h (hue). The distance between the achromatic point and the colour is known as the chroma or saturation (C), and it is computed from "a*" and "b*" using the following formula: The chromaticity diagram's centre is located at a = 0 and b = 0. Higher achromaticity and poorer purity are indicated by lower values of "a*" and "b*." Conversely, the purer, more saturated, or brighter the colour, the higher the values of "a*" and "b*" (disregard the negative signs). More saturated colour, or brighter, is indicated by positive chroma (or saturation) numbers, whereas less saturated colour, or duller, is indicated by negative values. Hue, also known as colour purity, is an angle measured in degrees [8]. R. Experimental Design The scouring process was optimised using the response surface methodology to investigate the effects of three selected variables on absorbency, sinking, and the drop test. Box–Behnken statistical experimental design contained three factors, Factor 1 A – Temperature (Degree Celsius) Factor 2 B – SLs Concentration (% On weight of fabric) Factor 3 C –Caustic Soda Concentration (% On weight of fabric) Each component was put on three levels, regularly spaced out according to the scheme shown in Table 1. Table-I: Process Variables with Range for SLs Scouring of Cotton Variable Code Variable level TemperatureDegree Celsius A 85 92.5 100 SLs - % B 0.5 1.75 3 Caustic soda -% C 1 3 5 The preliminary scouring experiment was used to determine the variable levels for SL concentration (%) and Caustic concentration (%). In contrast, data from a prior study on alkaline scouring of cotton were used to determine the third parameter. A total of 17 combinations were found. Using Design-Expert software, analysis of variance (ANOVA) statistics, response surface plots, and model analysis were conducted at a 95% confidence interval (p < 0.05). During the optimization process, "minimize" was the stated goal for each variable's answer and objective [9]. III. CHARACTERIZATION A. Fourier Transform Infrared (FTIR) Several functional groups found in the SLs are confirmed by IR spectrometry using a Thermo Nicolet FTIR spectrometer with a diamond ATR. B. High-performance Liquid Chromatography (HPLC) The Shimadzu LC-2030C NT Model is used to identify and quantify the components of SLs C. Statistical Data Analysis Analysis of variance (ANOVA) was used to estimate the statistical variance of the experimental data. D. Surface Activity Optical contact angle measuring and contour analysis systems (Model No. OCA 25 15EC) of Data Physics are used for the evaluation. E. Rota Dyer Rota Dyer Machine Model 12 x 100 CC: Qty-12nos.J.K. Electronics manufactured Bio-Surfactant for Sustainable Textile Processing 4 Published By: Lattice Science Publication (LSP) © Copyright: All rights reserved. Retrieval Number:100.1/ijfte.A242205021125 DOI:10.54105/ijfte.A2422.05021125 Journal Website: www.ijfte.latticescipub.com the device used in this study. This instrument enables the simultaneous conduct of 12 trials while maintaining a consistent temperature and rate of temperature rise for all experiments. They are embedded with a special Temperature Control Mechanism and an indicator. This was used for desizing, scouring, and dyeing applications. F. Spectrophotometer The X-Rite Colour Ci7450 Model was used to measure the whiteness of the scoured material and to measure colour intensity. G. Laundrometer (Wash Fastness) The R.B. Electronic wash fastness tester is used to determine the resistance of textiles to colour fading and staining during repeated washing cycles. IV. TESTING AND ANALYSIS A. Desizing Gimatex's greige cotton fabric, of quality 40 ComX 40 Cw / 132 x 72 - 1/1, is utilised for this application. The fabric is desized using a commercially available and widely used Amylase enzyme, which breaks down the 1-4 glycosidic bonds between the amylose and amylopectin molecules in the starch. The alpha-amylase enzyme Rexsize NCG liquid then transforms it into water-soluble fructose, glucose, or maltose, which is easily removed from the fabric. The bacterial reaction, a biological process, produces the amylase enzyme, a bio-catalyst. Enzymes work on specific substances in a particular way [10]. A small amount of an enzyme can break down a significant amount of the chemical it acts on because enzymes have a specialised mechanism of action. Acetic acid maintains the pH at 6.5, and the enzyme concentration is 0.5% of the fabric's weight. The temperature is increased to 850 °C and held for 45 minutes. Following the process, the bath is drained, and a 10-minute hot wash at 950°C is administered. The item is then allowed to air dry and examined for characteristics, including weight reduction, TEGEWA rating, and desizing efficiency. Additionally, surfactants help lower the water's surface tension, making it easier to remove sizing agents from textiles. Conventional surfactant (Keenox CBR) was tested against SLs in desizing applications. Desizing results are compiled in Section 6.1, Table 7. B. Scouring and Bleaching Greig hosiery fabric was treated with scouring and bleaching to remove impurities and enhance its functionality. Traditional scouring and bleaching methods involve alkali treatment with surfactant and bleaching agent. The Scouring bath is prepared with Caustic at 2.0%, H2O2 at 3.0%, Surfactant at 0.6%, and Stabiliser for peroxide at 0.3%. The chemicals mentioned below are based on the weight of the fabric. The MLR was maintained at a 1:10 ratio, and the temperature was maintained at 98°C. The run time is 45 minutes. After the process, the bath is drained, and the fabric under study is washed at 85 °C for 10 minutes, followed by drying. Treated fabrics are tested for properties such as absorbency, capillary rise, sinking test, and whiteness index. Swatches are then further taken for reactive dyeing to assess the effect of the surfactant on dyeing performance. C. Reactive Dyeing: The Scoured and Bleached Fabric is Dyed with Reactive Dyes The fabric, 5.0 g, is scoured and bleached with Kleenox CBR and SLs, and then dyed with reactive dyes. The study was done on three reactive dyes. Rosareact G Yellow HER, Rosareact N Blue HE2G, Rosareact Red HE4B H/C are used for this study. The concentration of the dye used is 2.0%. [Fig.1: Graphical Representation of the Reactive Dyeing Process] A time-temperature graph illustrates the traditional reactive HE dyeing procedure. Where A: Fabric, the necessary quantity of dye, and room-temperature salt (40 g/L), A–B: Increase the temperature by 1.5°C each minute to 80°C. B– C: Run the bath at 80°C for 30 minutes. C: Adding 20 g/l of sodium carbonate, C–D: 45 minutes of dying, E: Rinsing and washing [11]. V. RESULTS AND DISCUSSION A. Fourier-Transform Infrared Spectroscopy (FTIR) FTIR spectra in Figure 3 typically show characteristic peaks corresponding to hydroxyl (-OH), carbonyl (C=O), and ester (C-O) groups, which are essential for the surfactant activity of SLs [12]. [Fig.2: FTIR Spectra of SLs] ▪ Hydroxyl Groups: FTIR spectra typically show broad absorption bands around 3200-3450 cm⁻¹, indicating the presence of hydroxyl (-OH) groups, which are essential for the surfactant properties of SLs [13]. Indian Journal of Fibre and Textile Engineering (IJFTE) ISSN: 2582-936X (Online), Volume-5 Issue-2, November 2025 5 Published By: Lattice Science Publication (LSP) © Copyright: All rights reserved. Retrieval Number:100.1/ijfte.A242205021125 DOI:10.54105/ijfte.A2422.05021125 Journal Website: www.ijfte.latticescipub.com ▪ C-H Stretching: Peaks in the range of 2800-3000 cm⁻¹ correspond to C-H stretching vibrations from the fatty acid chains, confirming the glycolipid nature of SLs ▪ C=O Stretching: A peak around 1740 cm⁻¹ is indicative of carbonyl (C=O) stretching, which is characteristic of the ester bonds formed between the fatty acids and the sophorose moiety. ▪ Alkyl Groups: A deformation vibration at 1367 cm⁻¹ indicates alkyl groups. ▪ FTIR spectra reveal characteristic peaks corresponding to hydroxyl (–OH), carbonyl (C=O), and ether (C–O–C) groups, essential for understanding the molecular structure of SLs. B. High-Pressure High-Performance Chromatography SLs were examined using an HPLC analytical symmetric C18 column (250 x 4.6 mm²). The following gradient solvent elution profile was employed: a 95:5 v/v water/acetonitrile mixture was held for 10 minutes, resulting in a final composition of 50 minutes of linear gradient water/acetonitrile (5:95, v/v), followed by a 10-minute holding period. The flow rate was 0.5 ml/min. At 220 nm, the peaks were found. In numerous runs, fractions from various peaks were gathered and pooled independently. [Fig.3: HPLC Chromatogram of SLs] The analysis of SLs using high-performance liquid chromatography (HPLC) reveals their complex composition and diverse derivatives. SLs are categorized into acidic and lactonic types, each exhibiting unique properties. (i) Peaks: Peak 1, at a retention time of 27.714 minutes, has an area percentage of 53.1% and a height of 1,193,317. Peak 2, at a retention time of 46.986 minutes, has an area percentage of 46.9% and a higher peak height of 2,027,223. This indicates that there are two primary components in the SLs sample, contributing almost equally to the total area. (ii) Retention Times: The retention times of around 27.7 and 47 minutes suggest that the sample likely contains different SL variants, such as lactonic and acidic forms. SLs often show varied retention based on structural differences (e.g., acetylation and chain length of fatty acids). Peak 1 (Retention time: 27.714 min) represents lactonic SLs, which elute earlier due to their more hydrophobic nature, allowing them to interact less with the stationary phase in reverse-phase chromatography. Peak 2 (Retention time 46.986 min) represents acidic SLs. Acidic SLs are more hydrophilic due to a free carboxylic acid group, making them elute later as they interact more with the stationary phase [12]. C. Physical Properties of SLs The SLs have a surface tension value of 30.56, a wetting time of 23 seconds by Drave's test, and an emulsification index of approximately 86. Wetting fabric and emulsifying wax are crucial steps in the scouring process. Since SLs have both emulsification and wettability qualities, they will be a superior green surfactant for scouring cotton. Table-II: Physical Properties of SLs Properties SLs Appearance Clear Liquid pH of 10 % 5.36 Solid Content at 110 ° for 2 hours 50 % Wetting by Drave’s test-1.0 ml in water 23 sec Foaming by the Cylinder method 150 ml Collapsed to 120 ml in 1 min Specific gravity 1.023 Ionic Nature Anionic Emulsifying index 86 Surface Tension of 0.1 % Solution 34.56 D. Response Surface Methodology Statistical Optimization The experimental data were analysed using the 'Design Expert 12' statistical program, with an ANOVA used to estimate statistical parameters. Table 3 provides the experimental range, coded level of variables, and outcomes. The quadratic model was proposed for the drop test (Equation 1) and the sinking test (Equation 2), while the linear model was suggested for capillary rise (Equation 3). Equations (1 to 3) display the final empirical model as a coded factor for the response of the drop test (in seconds), sinking test, and wicking test. The process is run for 45 minutes. Factor 1 A – Temperature (Degree Celsius) Factor 2 B – SLS Concentration (% On weight of fabric) Factor 3 C -Caustic Concentration (% On weight of fabric) Drop Test = +4-2.06A-5.56B - 1.75C+1.38AB+0.25AC+1.25BC+0.1875A²+4.19B²+1.56C Sinking Test= +40.08-9.59A-32.13B-1.84C-5.1AB-2.98AC5.15BC+1.67A²+21.6B²+1.67C Capillary Rise= +2.42+0.2875A+0.625B+0.3625C Bio-Surfactant for Sustainable Textile Processing 6 Published By: Lattice Science Publication (LSP) © Copyright: All rights reserved. Retrieval Number:100.1/ijfte.A242205021125 DOI:10.54105/ijfte.A2422.05021125 Journal Website: www.ijfte.latticescipub.com Table-III: Experimental Variables for Scouring of Cotton Std Run Factor 1 Temp Factor 2 SL conc in % owf* Factor 3 Caustic Conc in %owf Response 1 Sinking in sec Response 2 Drop test in sec Responses 3 Capillary rise in cm for 5 mins 4 1 100 3 3 21.5 2.0.0 3.1 12 2 92.5 3 5 20.7 3 sec 3.4 1 3 85 0.5 3 95.0 17.5 1.3 6 4 100 1.75 1 37.5 5.0 2.1 8 5 100 1.75 5 27.6 3.0 3.4 17 6 92.5 1.75 3 37.5 5.0 2.4 5 7 85 1.75 1 53.3 9.0 1.6 10 8 92.5 3 1 34.4 5.0 2.8 16 9 92.5 1.75 3 29.3 2. 2.5 13 10 92.5 1.75 3 32.3 2.0 2.7 3 11 85 3 3 48.3 4.0 2.7 11 12 92.5 0.5 5 102.6 12.0 1.8 15 13 92.5 1.75 3 51.3 6.0 2.4 14 14 92.5 1.75 3 50.0 5.0 2.3 2 15 100 0.5 3 88.6 sec 10.0 2.0 7 16 85 1.75 5 55.3 sec 6.0 2.7 9 17 92.5 0.5 1 95.7sec 19.0 1.9 *On the weight of the fabric Table-IV: ANOVA for the Response Surface Quadratic Model for the Drop Test Source Sum of Squares df Mean Square F-value p-value Response 408.47 9 45.39 18.62 0.0004 significant A-Temperature 34.03 1 34.03 13.96 0.0073 B-concentration 247.53 1 247.53 101.55 < 0.0001 C-Caustic conc 24.5 1 24.5 10.05 0.0157 AB 7.56 1 7.56 3.1 0.1216 AC 0.25 1 0.25 0.1026 0.7581 BC 6.25 1 6.25 2.56 0.1533 A² 0.148 1 0.148 0.0457 0.8124 B² 73.83 1 73.83 30.29 0.0009 C² 10.28 1 10.28 4.22 0.0791 Residual 17.06 7 2.44 Lack of Fit 3.06 3 1.02 0.2917 0.8304 not significant Pure Error 14 4 3.5 Cor Total 425.53 16 Source Sum of Squares df Mean Square F-value p-value The factor is coded. Type III: Partial sum of squares. The significance of the model is indicated by its F-value of 18.62. This large F-value could only result from noise in 0.04% of cases. Model terms are considered significant when the Pvalue is less than 0.0500. A, B, C, and B2 are essential model terms. The model terms are not important if the values are higher than 0.1000. Model reduction can enhance your model if it contains many unnecessary terms (apart from those necessary to maintain the hierarchy). Compared to pure mistakes, the lack of fit is not substantial, as indicated by the lack of fit F-value of 0.29. A significant Lack of Fit F-value has an 83.04% probability of being caused by noise. Good is a non-significant lack of fit. E. Response to Sinking Test Table-V: ANOVA for the Response Surface Quadratic Model for the Sinking Test Source Sum of Squares df Mean Square F-value p-value Model 11298.6 9 1255.4 16.61 0.0006 significant A-Temperature 735.36 1 735.36 9.73 0.0169 B-concentration 8256.13 1 8256.13 109.22 < 0.0001 C-Caustic conc 27.01 1 27.01 0.3573 0.5688 AB 104.04 1 104.04 1.38 0.2791 AC 35.4 1 35.4 0.4683 0.5158 BC 106.09 1 106.09 1.4 0.2748 A² 11.78 1 11.78 0.1558 0.7048 B² 1964.01 1 1964.01 25.98 0.0014 C² 11.78 1 11.78 0.1558 0.7048 Residual 529.15 7 75.59 Lack of Fit 121.46 3 40.49 0.3972 0.7629 not significant Pure Error 407.69 4 101.92 Cor Total 11827.74 16 The sum of squares factor coding is Type III-Partial. The significance of the model is indicated by its F-value of 16.61. An F-value of this level could only result from noise in 0.06% of cases. Model terms are considered significant when the Pvalue is less than 0.0500. A, B, and B2 are essential model terms in this instance. Regarding the pure error, the Indian Journal of Fibre and Textile Engineering (IJFTE) ISSN: 2582-936X (Online), Volume-5 Issue-2, November 2025 7 Published By: Lattice Science Publication (LSP) © Copyright: All rights reserved. Retrieval Number:100.1/ijfte.A242205021125 DOI:10.54105/ijfte.A2422.05021125 Journal Website: www.ijfte.latticescipub.com lack of fit is not considerable, as indicated by the F-value of 0.40 for the lack of fit. A significant Lack of Fit F-value has a 76.29% probability of being caused by noise. Inconsequential absence of fit is good. F. Response for Wicking Test Table-VI: ANOVA for a Linear Model for Wicking Source Sum of Squares df Mean Square F-value p-value Model 4.84 3 1.61 25.97 < 0.0001 significant A-Temperature 0.6612 1 0.6612 10.65 0.0062 B-concentration 3.13 1 3.13 50.33 < 0.0001 C-Caustic conc 1.05 1 1.05 16.93 0.0012 Residual 0.8072 13 0.0621 Lack of Fit 0.7152 9 0.0795 3.46 0.1224 not significant Pure Error 0.092 4 0.023 Cor Total 5.64 16 The significance of the model is indicated by its F-value of 25.97. An F-value this enormous could only result from noise in 0.01% of cases. Model terms are considered significant when the P-value is less than 0.0500. A, B, and C are essential model terms in this instance. Values above 0.1000 indicate that the model terms are not significant. Model reduction can enhance your model if it contains many unnecessary terms (apart from those necessary to maintain the hierarchy). Regarding the pure error, the Lack of Fit is not substantial, as indicated by the Lack of Fit F-value of 3.46. A significant Lack of Fit F-value has a 12.24% probability of being caused by noise. A negligible loss of fit is desirable; we want the model to fit. G. Combined Effect of Temp, SLs, and Caustic Concentration on Absorbency The response surface approach was employed to investigate the individual and combined effects of the three factors on the absorbency of scoured fabric. ANOVA was used to analyse the results, and three-dimensional response surface plots illustrated the impact of experimental conditions. The basic processes of alkaline scouring in cotton include the saponification of oils and fats, the emulsification of waxes, and the breakdown of proteins and pectins into sodium salts of smaller molecular fragments. According to the response surface analysis approach in Table 3, the SL concentration, caustic concentration, and temperature have a substantial impact on the absorbency of the scoured fabric. As indicated in Table 2, the response surface analysis approach demonstrated that the scouring parameters have a considerable impact on the fabric's absorbency. With absolute values on the axes, Figures 5-7 display the relationships between variables in three-dimensional response surface plots. Figure 4(a) illustrates how the drop test of scoured cloth is affected by the concentration of SLs, caustic, and temperature at a fixed 45-minute interval. According to the study, the absorbency of the scoured fabric increases linearly with the concentration of SLs (SL), and it significantly increases as the temperature and SL concentration rise. Temperature enhances wettability, as shown in Figure 4(b), presumably due to an increased saponification rate and wax melting. Figure 4(c) shows that saponification increases absorbency, as measured by the drop test, with rising temperature and caustic concentration. (a) (b) (c) [Fig. 4: (a, b, and c) – Impact of SLs Conc by Drop Test of the Scoured Fabric] Figures 5 (a, b and c) show the impact of variables on the sinking test of scoured cotton fabric. The study reveals that the sinking of scoured fabric showed a linear improvement with an increase in SL concentration, caustic concentration, and temperature at a fixed time of 45 minutes. Bio-Surfactant for Sustainable Textile Processing 8 Published By: Lattice Science Publication (LSP) © Copyright: All rights reserved. Retrieval Number:100.1/ijfte.A242205021125 DOI:10.54105/ijfte.A2422.05021125 Journal Website: www.ijfte.latticescipub.com (a) (b) (c) [Fig. 5: (a, b, and c) – Impact of SLs Conc on the Sinking Test of Scoured Fabric] Figures 6A, B, and C disclose the impact of variables on the wicking of scoured cotton fabric. The Wicking of fabric increases with rising temperature, SLs concentration, caustic concentration, and temperature at a fixed time of 45 mins. (a) (b) (c) [Fig.6: (a, Band c) – Impact of Temperature and SLs Concentration on the Capillary Test] According to the study, an optimal recipe can result in high capillary, low sinking time, and the lowest absorbency. Section 5.4.3's numerical optimization provides a highly desirable formula with precise scouring parameter values. H. Optimization and Validation of the Model Table 7 presents the optimisation constraints for a Box-Behnken design, detailing the goals, limits, weights, and importance of each parameter. The chart defines optimization goals for process parameters. Table-VII: Common Constraints An optimised recipe is achieved by optimising the scouring process parameter to minimise absorbency, as measured by the drop test, sinking, and the higher wicking-capillary test, which is a crucial component of the study. A numerical optimisation method was employed to optimise the scouring variable parameter. The numerical optimization suggested the optimal scouring conditions for good wettability were temperature 88°C, SLs concentration 2.0 %, and caustic 2.0 % As shown in Table 8, the predicted and experimental values for drop test, sinking test, and wicking loss were very close, indicating that the developed model accurately predicted the result. Table-VIII: Predicted and Experimental Response of Numerically Optimized Recipe Temperature SLs concentration Caustic conc Absorbency (Drop Test) Sinking Capillary rise Pred Exp Pred Exp Pred Exp 88 2.00% 2.00% 5.1 5 41 40 2.2 2.1 Abbreviations: PredPredicted and ExpExperimental Name Goal Lower Limit Upper Limit Lower Weight Upper Weight Importance A: Temprature minimize 85 100 1 1 3 B: SLConcentration minimize 0.5 3 1 1 3 C: Caustic conc minimize 1 5 1 1 3 Absorbancy minimize 2 19 1 1 3 Sinking minimize 20.7 102.6 1 1 3 Capillary rise maximize 1.3 3.4 1 1 3 Indian Journal of Fibre and Textile Engineering (IJFTE) ISSN: 2582-936X (Online), Volume-5 Issue-2, November 2025 9 Published By: Lattice Science Publication (LSP) © Copyright: All rights reserved. Retrieval Number:100.1/ijfte.A242205021125 DOI:10.54105/ijfte.A2422.05021125 Journal Website: www.ijfte.latticescipub.com VI. PRETREATMENT AND DYEING PERFORMANCE A. Desizing The Desizing application, performed with conventional and SL surfactants, is compiled in Table 9. The lesser absorbency, desizing efficiency, and TEGEWA rating might be due to the low wetting properties of the SLs. Table-IX: Comparative Results after Desizing Application of Conventional and SLs TEGEWA Rating Desizing Efficiency (%) Absorbency Whiteness Index-CIE Weight Loss (Seconds) Conventional Surfactant 4-5 88 12-15 sec 15.63 11.23% SLs 4 86 20-25 sec 15.67 10.56% B. Scouring and Bleaching The scouring and bleaching performance of SLs against conventional scoured fabric is shown in Table 10. The capillary rise, drop test, sinking test, and weight loss of traditional surfactants are better due to their higher wetting action and detergency. Table-X: Comparative Pretreatment Performance of Conventional and SLs Recipe Capillary rise Drop test Sinking test Weight loss Whiteness index Convention surfactant4.4 cm Less than 1 sec 13 sec 2.66% 64.32 SLs 3 cm 1 sec 35.6 sec 2.58% 63.18 C. Performance of Scoured and Bleached Fabric in Dyeing by Spectrophotometer The preparation of fabric for dyeing is a crucial responsibility for textile processors; uneven pre-processing can result in uneven colouration. Considering the importance of dyeing performance, the fabric was scoured with SLs and dyed with reactive dye, and the dyeing results L*, a*, b*, and K/S were investigated, as shown in Tables 11, 12 and 13. The conventional surfactant-pretreated and dyed fabric is considered the standard, and the comparison of strength and K/S is made with the SLpretreated, dyed sample. i. Yellow Shade Rosareact G Yellow HERAs shown in Table 11, the colour strength of SL-treated fabric is similar to that of conventional wetting agent-treated fabric. DE 0.28 indicates that the tonal variation falls within an acceptable range of 0.5. No structural changes were observed in either fabric on FTIR scanning. Table-XI: Spectro Reading for Rosareact G Yellow HER 2.0% Shade Standard L* a* b* C* h G. Yellow (CBR) 44.55 59.96 0.99 59.97 0.94 SLs surfactant – pretreated and dyed DL* Da* Db* DC* DH* %STR WSUM DEcmc -0.36 D 0.36 R 0.64 Y 0.73 B -0.8 R 102.84 0.28 [Fig.7: FTIR of Conventional and SLs Pretreated and Dyed with Rosareact G Yellow HER] ii. Red Shade Table-XII: Spectro Reading for Rosareact Red HE4B H/C-2.0% Shade Standard L* a* b* C* h Conventional Surfactant – pretreated and dyed 44.55 59.96 0.99 59.97 0.94 SLs surfactant – pretreated and dyed DL* Da* Db* DC* DH* %STR WSUM DE CMC -0.43D 0.28R 0.39 Y 0.29 B 0.39 Y 101.46 0.31 According to the data in Table 9, the colour strength of the conventional and SL samples is comparable. The tonal variation is within the acceptable range of DE -0.5. The treated fabrics' FTIR was performed to check for any changes in the functional properties of the fabric after treatment. Figure 7 shows identical peaks, indicating no structural change.