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The purpose of this study is to analyse the characteristics of cellulose fibres derived from the pseudo-stems of Curcuma longa and to evaluate the properties of non-woven fabric produced using these fibres.

The fibres were extracted via a decortication method. The acquired intrinsic qualities of the fibres were used to assess the feasibility of using them in textile applications. The thermal bonding approach was used for the development of the non-woven fabric, using a hot press machine with low-melt polyester fibre as a binder.

The mean length of Curcuma longa fibres was determined to be 52.73 cm, with a fineness value of 4.00 tex. The fibres exhibited an uneven cross-sectional morphology, characterized by a diverse range of oval-shaped lumens. The fibre exhibited a tenacity of 1.45 g/denier and an elongation value of 4.30%. The fibres possessed a moisture regain value of 11.30%. The experimental non-woven fabrics had consistent weight and thickness, while exhibiting different properties in terms of tensile strength and air permeability, with Fabric C having the highest tensile strength and the lowest air permeability value.

The features of Curcuma longa fibre, obtained with the decortication process, exhibited suitability for textile applications. Three experimental non-woven fabrics comprising different compositions of Curcuma longa fibre and low-melt polyester fibre were produced. The tensile strength and air permeability properties of these fabrics were influenced by the composition of the fibres.

The textile industry harms the environment at every stage of production, from the acquisition of raw materials to the disposal of finished products. It is very important for the textile industry to adapt to the basic policies on environmental sensitivity and sustainability to keep up with the transformation in production processes and the rapid changes occurring around the world in order to exist in global competition. Within the scope of sustainable development goals, it is of great importance to measure and evaluate indicators of all processes of the sector. This paper aims to present application of multi-criteria decision making (MCDM) methods for the assessment of sustainable development in textile industry.

The data of a multinational clothing company’s four-year sustainability performance between 2018 and 2021 were evaluated under 22 sustainability parameters determined using two new MCDM techniques, namely the combined consensus solution method and multi-attribute ideal real comparative analysis. In determining the criteria, priority key indicators were determined by taking into account the sector’s relationship with the environment, raw material consumption and social adequacy.

According to the application results of both methods, the year 2021 shows the best performance. It has been seen that the sustainability performance of the Inditex group has increased over the years and the results of the applied models support each other. It can be suggested that the proposed approach be applied to evaluate the progress in the textile sector with the relevant data on a particular company or on a macro scale.

This study makes an important contribution to the field in terms of the fact that the methods used are recent and have no application in the field of textiles. It allows the evaluation of different sustainability criteria together using a single method. It is very important to share data on sustainability indicators with customers, employees, suppliers, investors, partner organizations and society and evaluate performance. Analyzing sustainability performance on the basis of annual reports is important in terms of identifying good practices, sharing them with the community and setting an example. In addition, using scientific methods in the evaluation of the sustainability report data published by companies regularly provides significant feedback for policymakers and academics.

Cotton fibre lots are graded and selected for yarn spinning based on their quality value which is a function of certain fibre properties. Cotton grading and selection has created a domain of emerging interest among the researchers. Several researchers have addressed the said issue using a few exponents of multi-criteria decision-making (MCDM) technique. The purpose of this study is to demonstrate a cotton selection problem using a recently developed measurement of alternatives and ranking according to compromise solution (MARCOS) method which can handle almost any decision problem involving a finite number of alternatives and multiple conflicting decision criteria.

The MARCOS method of the MCDM technique was deployed in this study to rank 17 cotton fibre lots based on their quality values. Six apposite fibre properties, namely, fibre bundle strength, elongation, fineness, upper half mean length, uniformity index and short fibre content are considered as the six decision criteria assigning weights previously determined by an earlier researcher using analytic hierarchy process.

Among the 17 alternatives, C9 secured rank 1 (the best lot) with the highest utility function (0.704) and C7 occupied rank 17 (the worst lot) with the lowest utility function (0.596). Ranking given by MARCOS method showed high degree of congruence with the earlier approaches, as evidenced by high rank correlation coefficients (Rs > 0.814). During sensitivity analyses, no occurrence of rank reversal is observed. The correlations between the quality value-based ranking and the yarn tenacity-based rankings are better than many of the traditional methods. The results can be improved further by adopting other efficient method of weighting the criteria.

The properties of raw cotton have significant impact on the quality of final yarn. Compared to the traditional methods, MCDM is reported as the most viable solution in which fibre parameters are given their due importance while formulating a single index known as quality value. The present study demonstrates the application of a recently developed exponent of MCDM in the name of MARCOS for the first time to address a cotton fibre selection problem for textile spinning mills. The same approach can also be extended to solve other decision problems of the textile industry, in general.

Novelty of the present study lies in the fact that the MARCOS is a very recently developed MCDM method, and this is a maiden application of the MARCOS method in the domain of textile, in general, and cotton industry, in particular. The approach is very simple, highly effective and quite flexible in terms of number of alternatives and decision criteria, although highly robust and stable.

This paper aims to investigate how different knitted structures affect the illuminative effect of polymeric optical fibres (POFs).

Knit prototypes were constructed using a 7-gauge industrial hand flat knitting machine. The textile prototype swatches developed in this study tested POF illumination in three types of knitting structures: intervallic knit and float stitch structures; POF inlaid into double plain and full cardigan structures; and double plain and partial knitting structures. The illuminative effects of the POFs in seven prototype swatches were analysed and compared.

It is possible to use an industrial hand flat knitting machine to knit POFs. Longer floats expose more POFs, which boosts illumination but limits the textile’s horizontal stretchability. The openness of the full cardigan structure maximises POF exposure and contributes to even illumination. The partial knitting in different sections achieves the most complete physical integration of POFs into the knitted textiles but constrains the horizontal stretchability of the textiles.

The integration of POFs into knitted textiles provides a functional illuminative effect. Applications include but are not limited to fashion, architecture and interior design.

This study is novel, as it investigates new POF knitted textiles with different loop structures. This study examines how knit stitches affect POFs in intervallic knit and float stitch, inlaid POF double knit, double plain and partial knit and the illuminative effects of the knitted textile.

This paper aims to examine and compare the export performance and competitiveness of Indian and Chinese textile and clothing industry in post-multifibre arrangement (MFA) era.

Balassa’s revealed comparative advantage Index is used to assess the competitiveness of Indian and Chinese textile and clothing exports.

The results indicate that China’s textiles and garments sector holds a greater proportion of the global market compared with India. India has a robust comparative advantage in silk, carpets and cotton post-MFA. Vegetable textile fibers, paper yarn and woven fabrics of paper yarn are also competitive. China had a strong comparative advantage in silk and fabrics; special woven fabrics, tafted textile fabrics, lace, tapestries, trimmings and embroidery in 2005. China also recorded comparative advantage in silk, man-made filaments: strip and the like of man-made textile materials, fabrics; special woven fabrics, tafted textile fabrics, lace, tapestries, trimmings and embroidery and fabrics; knitted or crocheted in 2021.

This study’s results and recommendations could assist the Indian and Chinese Governments develop policies to upgrade their garment industries.

Though vast literature reviews are available for textile and apparel export performance in India and China separately, there are few studies on comparisons. This study is a significant attempt to evaluate India and China’s competitiveness in the global market.

The purpose of this study is to use liposome technology in the treatment of fabrics textiles because of its efficient energy saving, reducing time and temperature.

The newly prepared lecithin liposome was used to encapsulate dyes for the purpose of increasing dyeing affinity. Different ratios of commercially available lecithin liposomes (1%, 3%, 5% and 7%) were used simultaneously in the dyeing of cotton and wool fabrics. The treated fabrics (cotton and wool fabrics) were confirmed using different analytical procedures such as scanning electron microscope (SEM), Fourier-transition infrared spectroscopy, ultraviolet protection factor, colour strength (K|S) measurements and fastness measurements.

The results show that increasing liposome ratios in dyeing baths leads to increased dyeing affinity for cotton and wool fabrics compared with conventional dyeing without using liposomes. In addition to that, the colour strength values, infrared spectra, SEM and fastness properties of non-liposome-dyed fabrics and liposome-dyed fabrics were investigated.

The research paper provides broad spectrum of green encapsulation fabrics using liposome technology to perform the dye stability, dye strength and fastness.

With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

This research conducts a thorough analysis of artificial neural network applications in the textile sector.

This study aims to characterize the properties of natural cellulose fiber from the pseudo-stems of the curcuma zedoaria plant.

The fiber was extracted using the biological retting process (cold-water retting). The intrinsic fiber properties obtained were used to evaluate the possibility of using fiber for textile applications.

The average length of a curcuma zedoaria fiber was 34.77 cm with a fineness value of 6.72 Tex. A bundle of curcuma zedoaria fibers was comprised of many elementary fibers. Curcuma zedoaria had an irregular cross-section, with the lumen having a varied oval shape. Curcuma zedoaria fibers had tenacity and elongation value of 3.32 gf/denier and 6.95%, respectively. Curcuma zedoaria fibers had a coefficient of friction value of 0.46. Curcuma zedoaria fibers belong to a hygroscopic fiber type with a moisture regain value of 10.29%.

Extraction and Characterization of Curcuma zedoaria Pseudo-stems Fibers for Textile Application.

The application of indigo dyes in the denim industries has been criticised due to the introduction of non-renewable oxidation products into the environment. Previous studies have investigated that reducing sugars can be used as green alternatives to sodium dithionite in the indigo dyeing of cotton fabric owing to their reduced and stable redox potential in the dye bath. The purpose of this study was to dye denim cotton fabric with indigo dye using various reducing sugars and alkalis. The use of sucrose and potassium hydroxide (KOH) for indigo dyeing has been explored for the first time.

A mixed factorial design with four variables including alkali, pH, number of dips and type of reducing sugar at different levels was studied to identify a significant correlation between the effect of these variables on the colour strength and fastness properties of the dyeings.

Investigations were made to examine the significant factors and interactions of the selected responses in the eco-friendly dyeing method. This process has the potential to reduce the load of sulphite and sulphate generated in the dyebath due to the use of a conventional reducing agent, sodium dithionite. The colour strength of the dyeing reduced with fructose was found to be better than other reducing sugars and significantly influenced by the number of dips, pH levels and the interaction between pH and reducing sugars. Using fructose for indigo dyeing with two dips at a pH of 11.5, using KOH as an alkali, results in higher colour strength values. The fastness properties of the indigo-dyed sample with reducing sugars ranging from fair to good or good to excellent. Specifically, colour change receives a rating of grey scale 3–4, staining 4–5, dry rubbing 4 and light fastness 3–4. These assessments hold true across various factors such as the type of reducing sugar, alkali, pH and the number of dips. The optimised parameters leading to improved colour strength and fastness properties are also discussed.

This dyeing technique is novel and a green alternative to dithionite denim dyeing. This process is found to be useful for indigo dyeing of denim fabric leading to reduced and stable redox potential in the dyebath and acceptable colour strength of the dyed fabric.

This study aims to identify leather type and authenticity through optical coherence tomography.

Optical coherence tomography images taken from genuine and faux leather samples were used to create an image dataset, and automated machine learning algorithms were also used to distinguish leather types.

The optical coherence tomography scan results in a different image based on leather type. This information was used to determine the leather type correctly by optical coherence tomography and automatic machine learning algorithms. Please note that this system also recognized whether the leather was genuine or synthetic. Hence, this demonstrates that optical coherence tomography and automatic machine learning can be used to distinguish leather type and determine whether it is genuine.

For the first time to the best of the authors' knowledge, spectral-domain optical coherence tomography and automated machine learning algorithms were applied to identify leather authenticity in a noncontact and non-invasive manner. Since this model runs online, it can readily be employed in automated quality monitoring systems in the leather industry. With recent technological progress, optical coherence tomography combined with automated machine learning algorithms will be used more frequently in automatic authentication and identification systems.