Investigation of the stripping process of the reactive dyes using organic sulphur reducing agents in alkali condition

This paper focuses on the stripping application of the reactive dyes using different recipes set up with different reducing agents and working parameters. We tried to develop alternative stripping methods using two kinds of thioureadioxide because of several disadvantages of sodium dithionite. The pretreated woven fabrics were dyed at 0.25%, 1%, 4% owf% using five commercial reactive dyes by exhaust method. Totally, 180 specimens were dyed. Then, these specimens were stripped using alternative recipes. In these recipes, we investigated the effects of types of the reducing agents, the concentration of the reducing agents and caustic, process temperature and the presence of leveling agent on the stripping efficiency. In order to examine the efficiency of the stripping processes, we measured the colorimetric values (L*, ΔL, ΔE) of pretreated, dyed and the stripped samples using commercial spectrophotometer and international measurement method. The experimental results were statistically evaluated using analysis of variance (ANOVA) with significance level ofα=0.05. According to the measured and calculated spectral results, we achieved to develop alternative methods using thioureadioxide instead of sodium dithionite. In terms of ANOVA, we found out that only concentration of caustic itself did not have significant effects on the efficiency of stripping.     

Dyeing and DP treatment of sol-gel pre-treated cotton fabrics

Cotton samples were pre-treated with various sol solutions containing different alkoxysilanes (TEOS, GPTMS, APTES, and TESP-SA). The as-prepared cotton samples were dyed with 2 % owf Red and 4 % owf Blue. Furthermore, dyed cotton samples were after-treated with the alkoxysilanes. The alkoxysilane were also incorporated into the dyeing bath. The as- prepared cotton samples were subjected to a treatment with the non-formaldehyde durable press finishing agent BTCA in conjunction with the catalyst SHP. The textile materials were evaluated with respect to the colorimetric data (L*, a*, b*, ΔE*) and the color strength expressed in terms of K/S values. Tensile strength and dry crease recovery angles of the durable pressfinished samples were measured. The findings indicate that APTES and TESP-SA exert a significant influence on the color properties.     

Artificial neural network approach for predicting colour properties of laser-treated denim fabrics

In this study, artificial neural network (ANN) and linear regression (LR) approaches are proposed for predicting colour properties of laser-treated denim fabrics. Denim fabrics were treated under different combinations of laser processing parameters, including pixel time (μs), resolution (dot per inch) and grayscale (lightness percentage) as inputs. Colour properties, including colour yield (K/S sum value), CIE L*, a* and b* values and yellowness index were predicted as outputs in these approaches. Later, the prediction performances of two approaches were compared and the statistical findings revealed that ANN approach was able to provide more accurate prediction than LR approach, especially for L value. Moreover, among the three input variables, grayscale (lightness percentage) was found to be the most important factor affecting colour properties of laser-treated denim fabrics.     

Prediction of CIELab data and wash fastness of nylon 6,6 using artificial neural network and linear regression model

We tried to predict the CIELab data and wash fastness values of scoured nylon 6.6 knitted fabric dyed with 1:2 metal-complex acid dyes and aftertreated using three different methods named as syntan, syntan/cation and full backtan by artificial neural network (ANN) with Levenberg-Marquardt algorithm and regression models. Afterward, the predicting performance of these models was tested and compared with each other using unseen data sets. We were able to achieve to predict the all colorimetric data satisfactorily such as L*, a*, b*, C, h ^o and wash fastness performance using both models. The statistical findings indicated that the regression models provide more accurate prediction for all colour data with an average error of 1% contrast to previous study. In terms of prediction of fastness, artificial neural network is a bit more useful than regression models for prediction of staining value on the nylon part of adjacent multifiber.     

Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties. I. Prediction of yarn tensile properties

In this study artificial neural network (ANN) models have been designed to predict the ring cotton yarn properties from the fiber properties measured on HVI (high volume instrument) system and the performance of ANN models have been compared with our previous statistical models based on regression analysis. Yarn count, twist and roving properties were selected as input variables as they give significant influence on yarn properties. In experimental part, a total of 180 cotton ring spun yarns were produced using 15 different blends. The four yarn counts and three twist multipliers were chosen within the range of Ne 20–35 and α _e 3.8–4.6 respectively. After measuring yarn tenacity and breaking elongation, evaluations of data were performed by using ANN. Afterwards, sensitivity analysis results and coefficient of multiple determination (R²) values of ANN and regression models were compared. Our results show that ANN is more powerful tool than the regression models.     

Using artificial neural network to predict colour properties of laser-treated 100% cotton fabric

In this paper, artificial neural network (ANN) model was used for predicting colour properties of 100 % cotton fabrics, including colour yield (in terms of K/S value) and CIE L, a, and b values, under the influence of laser engraving process with various combination of laser processing parameters. Variables examined in the ANN model included fibre composition, fabric density (warp and weft direction), mass of fabric, fabric thickness and linear density of yarn (warp and weft direction). The ANN model was compared with a linear regression model where the ANN model produced superior results in prediction of colour properties of laser engraved 100 % cotton fabrics. The relative importance of the examined factors influencing colour properties was also investigated. The analysis revealed that laser processing parameters played an important role in affecting the colour properties of the treated 100 % cotton fabrics.     

Dyeing properties and colour fastness of cotton and silk fabrics dyed with<Emphasis Type="Italic">Cassia tora L.</Emphasis> extract

A natural colorant was extracted fromCassia tora L. using buffer solutions (pH: 2–11) as extractants. The dyeing solution (Cassia tora L. extract) extracted using pH 9 buffer solution was found to give the highest K/S values of dyed fabrics. Cotton and silk fabrics were dyed withCassia tora L. extract at 60°C for 60 min with pre-treatment of various metal salts as mordants. It was found thatCassia tora L. extract was polygenetic dyestuffs and its major components were anthraquinones. Studies have been made on the effects of the kind of mordant on dyeing properties and colour fastnesses of cotton and silk fabrics. The K/S of cotton fabrics increased in the order of the dyeing using FeSO₄>CuSO₄>ZnSO₄>MnSO₄≅Al₂(SO₄)₃>NiSO₄>none, however, the K/S of silk fabrics increased in the order of the dyeing using FeSO₄>CuSO₄>ZnSO₄≅Al₂(SO₄)₃>MnSO₄≅NiSO₄>none. It was found that the K/S values of dyed fabrics were largely affected by the colour difference (ΔE) between mordanted fabric and control fabric. However, they were not depended on the content of mordanted metal ion of the fabrics. Mordants FeSO₄ and CuSO₄ for cotton fabric, FeSO₄, CuSO₄, and Al₂(SO₄)₃ for silk fabric were found to give good light fastness (rating 4).     

Effect of color changes of naturally colored organic cotton fibers on human sensory perception

This study aims to investigate the color changes of Naturally Colored Organic Cotton (NaCOC) fibers after scouring, and to evaluate the human sensory perception for the fibers. Furthermore, it tries to observe the relationship between the color coordinates and the sensory perception. Three colors (ivory, coyote-brown, green) of NaCOC fibers were scoured under four different treatments (boiling water, enzyme, sodium carbonate, sodium hydroxide). The color coordinates (L, a, b) were measured in CIELAB using spectrophotometer (SP62, X-Rite), and color differences (ΔL, Δa, Δb, ΔE) were calculated. Human sensory perception for the NaCOCs was evaluated by 27 female participants. The questionnaire consisted of nine pairs of bipolar visual sensory adjectives using the SDS. The values of L and b fell, while the value of a arose after scouring in general. The value of ΔE was the highest when treated with alkali solutions among all treatments. Human sensory perception such as brightness, clearness, lightness and freshness generally decreased, while vividness and strength increased. The meaningful color factors to predict brightness, lightness were L and ΔL, and those to predict vividness and strength sensory were ΔL.     

Predicting properties of single jersey fabrics using regression and artificial neural network models

In our previous works, we had predicted cotton ring yarn properties from the fiber properties successfully by regression and ANN models. In this study both regression and artificial neural network has been applied for the prediction of the bursting strength and air permeability of single jersey knitted fabrics. Fiber properties measured by HVI instrument and yarn properties were selected as independent variables together with wales’ and courses’ number per square centimeter. Firstly conventional ring yarns were produced from six different types of cotton in four different yarn counts (Ne 20, Ne 25, Ne 30, and Ne 35) and three different twist multipliers (α _e 3.8, α _e 4.2, and α _e 4.6). All the yarns were knitted by laboratory circular knitting machine. Regression and ANN models were developed to predict the fabric properties. It was found that all models can be used to predict the single jersey fabric properties successfully. However, ANN models exhibit higher predictive power than the regression models.     

Psychoacoustic characteristics of fibers

In order to investigate psychoacoustic characteristics of fibers, and to compare them with sound physical parameters, each sound of 25 different fabrics consisted of a single fiber such as wool, cotton, silk, polyester, and nylon was recorded. Sounds of specimens were transformed into critical band diagram and psychoacoustic characteristics including loudness and sharpness for each sound were calculated based on Zwicker’s models. Physical parameters such as the level pressure of total sound (LPT), level ranges (ΔL), frequency differences (Δf), AR coefficients (ARC, ARF, ARE) were obtained in fast fourier transform (FFT) spectrum. Nylon taffeta showed higher values for loudness than 2.5 sone corresponding to human low conversation, while most silk fibers generated less louder showing lower values for loudness than 1.0 sone. Wool fibers had higher loudness mean value than that of cotton, while the two fibers didn’t differ in LPT. Loudness showed high positive correlation coefficients with both LPT and ARC. Sharpness values were higher for wool fiber group than other fibers. Sharpness was not concerned with loudness, LPT, and ARC, but the fabrics with higher values for sharpness tended to show higherΔL.     

Artificial Neural Network Modelling of Leaf Water Potential for Potatoes Using RGB Digital Images: A Greenhouse Study

Plant water status information of potato (Solanum tuberosum L. cv. Russet Burbank) is needed at the farm level for irrigation scheduling. This research investigated the feasibility of using a 5-megapixel digital camera to determine the leaf water potential (Ψ_L) of potato plants by capturing red, green, blue (RGB) digital images in the visible region of the electromagnetic spectrum. A greenhouse experiment was conducted in containerized cv. Russet Burbank potato plants subjected to five soil nitrate-nitrogen (N) levels and four soil water content levels. An artificial neural network (ANN) model, built with RGB images, RGB image transformations, RGB vegetation indices, and principal components analysis, found that for the validation data set, the measured Ψ_L and predicted Ψ_L results were from common populations. Other results showed: (1) a linear trend between soil nitrate-N levels and leaf reflectance in the G image band, (2) that the RG image bands were more suitable than the B image band for classifying leaf pigment from leaf shadow and leaf damage, (3) soil nitrate-N interacted with leaf greenness, affecting Ψ_L prediction, and (4) some image variables were more important than others in the ANN model. Although this greenhouse research shows promise, further field-based research is required to validate the selection of input neurons used and also validate the use of ANN modelling to determine Ψ_L at the plant canopy level with cv. Russet Burbank and other cultivars. In addition, an image acquisition method needs to be developed to obtain periodic representative sample coverage over a field.     

Prediction of cotton yarn properties using support vector machine

This paper presents a support vector machine (SVM) regression approach to forecast the properties of cotton yarns produced on ring and rotor spinning technologies from the fibre properties measured by HVI and AFIS. Prediction performance of SVM models have been compared against those of the artificial neural network (ANN) models. A k-fold cross validation technique is applied to assess the expected generalization accuracies of both SVM and ANN models. The investigation indicates that the yarn properties can be predicted with a very high degree of accuracy using SVM models and the prediction performance of SVM models are better than that of ANN models.     

Prediction of the changes on the CIELab values of fabric after chemical finishing using artificial neural network and linear regression models

Changes on the CIELab values of the dyed materials after the different chemical finishing treatments using artificial neural network (ANN) and linear regression (LR) models have been predicted. The whole structural properties of fabrics and some process data which were from fiber to the finishing parameters were accepted as inputs in these models. The networks having different structures were established, and it was also focus on the parameters which could affect the performance of the established networks. It was determined that we could successfully predict the color differences values occurring on the material after the finishing applications. In addition, we realized that some ANN parameters affected the prediction performance while establishing the models. After training ANN models, the prediction of the color difference values was also tried by linear regression models. Then, extra ANN models were established for all outputs using the parameters as inputs in the LR equations, and the prediction performances of both established models were compared. According to the results, the neural network model gives a more accurate prediction performance than the LR models.     

Equilibrium and kinetic modeling of the adsorption of indigo carmine onto silk

Quantitative adsorption kinetic and equilibrium parameters for indigo carmine dyeing of silk were studied using UV-visible absorption spectroscopy. The effect of initial dye concentration, contact time, pH, material to liquor ratio (MLR), and temperature were determined to find the optimal conditions for adsorption. The mechanism of adsorption of indigo carmine dyeing onto silk was investigated using the pseudo first-order and pseudo second-order kinetic models. The adsorption kinetics was found to follow a pseudo-second-order kinetic model with an activation energy (E _a) of 51.06 kJ/mol. The equilibrium adsorption data of indigo carmine dye on silk were analyzed by the Langmuir and Freundlich models. The results indicate that the Langmuir model provides the best correlation. Adsorption isotherms were also used to obtain thermodynamic parameters such as free energy (ΔG ^o), enthalpy (ΔH ^o), and entropy (ΔS ^o) of adsorption. The negative values of ΔG ^o and Δ^{H o} indicate the overall adsorption process is a spontaneous and exothermic one.     

Modelling of ring yarn unevenness by soft computing approach

This paper demonstrates the application of two soft computing approaches namely artificial neural network (ANN) and neural-fuzzy system to forecast the unevenness of ring spun yarns. The cotton fiber properties measured by advanced fiber information system (AFIS) and yarn count have been used as inputs. The prediction accuracy of the ANN and neural-fuzzy models was compared with that of linear regression model. It was found that the prediction performance was very good for all the three models although ANN and neural-fuzzy models seem to have some edge over the linear regression model. The linguistic rules developed by the neural-fuzzy system unearth the role of input variables on the yarn unevenness.     

Effects of fabric surface energy on human thermophysiological responses during exercise and recovery

The present paper reports a study on influences of fabric surface energy of cotton and polyester garments on clothing microclimates and human thermophysiological responses during intermittent exercise and recovery. Eight healthy males wearing the garments prepared performed exercises and rest according to the following protocol: rest for 30 min, run on treadmill for total 60 min of three sessions with different intensity and duration, and then sit quietly for 30 min for recovery, all at 30 °C and relative humidity of 30 %, while the microclimate humidity (H _mc ) and temperature (T _mc ), the clothing outside surface humidity (H _co ) and temperature (T _co ), the skin temperatures and ear canal temperature (T _{ear_canal} ) were measured. The garments are made of: (a) hydrophilic and hydrophobic cotton knitted fabrics, and (b) hydrophilic and hydrophobic polyester knitted fabrics. During and after exercise, for cotton, hydrophilic garment resulted in significant lowerΔH _mc , ΔH _co , ΔT _mc during recovery, higher , lowerΔT _{ear_canal} andΔT _forehead . For polyester, hydrophilic garment resulted in significantly lowerΔH _co , ΔT _co , higher , higherΔT _forehead during E1, E2 and recovery session but lower during E3. In summary, surface energy of cotton garments had significant influences on human thermophysiological responses during exercise and recovery, and hydrophilic cotton garment was better than hydrophobic one to reduce heat stress. Surface energy of polyester garments had influences of lower significance, and hydrophilic garment appeared better than hydrophobic garment.     

Improving the accuracy of models to map alpine grassland above-ground biomass using Google earth engine

Accurate modelling and mapping of alpine grassland aboveground biomass (AGB) are crucial for pastoral agriculture planning and management on the Qinghai Tibet Plateau (QTP). This study assessed the effectiveness of four popular models (traditional multiple linear regression (MLR), support vector machine (SVM), artificial neural network (ANN), and deep neural network (DNN)) with various input combinations (geospatial variables [GV], vegetation types [VT], field measurements [FM], meteorological variables [MV] and observation time [OT]) for AGB estimation based on a new framework for AGB modelling and mapping using Google Earth Engine. The results showed that the input feature of GV had a poor performance in AGB estimation (0.121 < R² < 0.591). FM improved the accuracy the most when incorporated with GV (0.815 < R² < 0.833). Although MV, VT and OT improved the accuracy (R²) only by 0.112–0.216 with an importance rank order of MV > VT > OT for machine learning models, their outputs could be used to map AGB. Grass AGB was less accurately predicted than shrub AGB, but the pooling of both VTs improved estimation accuracy (R²) by 0.171–0.269. The performance of the models followed the ranked order of DNN > ANN > SVM > MLR. DNN had the highest accuracy (R² = 0.818) using all non-field measured variables (excluding FM) as the inputs, and it was successfully applied to a new dataset (not associated with the data used in the training and testing) with a R² of 0.676. This study presents an effective and operational framework for modelling and mapping grassland AGB. Accordingly, it provides the scientific foundations to determine of sustainable grazing carrying capacity in alpine grasslands.     

A comparative study in five laboratories on detection ofClavibacter michiganensis subsp.sepedonicus in potato stems and tubers

Summary Potato stems and tubers grown in the field from seed tubers inoculated withClavibacter michiganensis subsp.sepedonicus, which causes bacterial ring rot, were tested by indirect, sandwich enzyme-linked immunosorbent assay (ELISA) in five laboratories. Correlation between values for each experimental treatment from the five laboratories was greater (r=0.86) than correlation between values for individual samples (r=0.71). When three or more laboratories obtained ELISA values of ≥0.200 for a sample, that sample was presumed to be positive. Conversely, when three or more laboratories obtained ELISA values <0.200, the consensus determination was regarded as negative. The percentage of stem and tuber samples that were in agreement with the consensus ELISA determination varied from 65.5 to 96.7%. Indirect immunofluorescence tests, conducted on the same samples in two laboratories, were consistent with 83.4–91.9% of the consensus ELISA determinations. Presence or absence ofC.m. sepedonicus was confirmed in some samples by an eggplant bioassay and direct isolatiion of the bacterium. The ELISA procedure was well suited for screening large numbers of samples and this study confirms it to be a promising procedure in routine indexing of seed potatoes forC.m. sepedonicus.     

Antioxidant Activities of Four Edible Seaweeds from the Southern Coast of Thailand

The aqueous extracts of four marine algae, Caulerpa racemosa var. macrophysa, Gracilaria tenuistipitata var. tenuistipitata, Sargassum sp., and Ulva lactuca, from the coastal areas in Southern Thailand, were prepared by boiling dried seaweed powder in water for 3 h, and by autoclaving each sample at 120 °C for 3 h. They were then freeze-dried and evaluated for their antioxidant activities using DPPH (1, 1-diphenyl-2-picrylhydrazyl), hydroxyl radical (OH^•) and superoxide anion (O₂^•−) scavenging assays. Boiling extracts of the seaweeds, except C. racemosa, were found to have higher total phenolic contents (TPC) than those obtained from the autoclave method. The antioxidant results also showed that O₂^•− scavenging activity existed only in the boiling extracts of C. racemosa, G. tenuistipitata, and U. lactuca. In DPPH and OH^• assays, however, almost all the boiling extracts were less active than the autoclave ones. Among the four alga species, Sargassum sp. was the most active. Both extracts of this seaweed had the highest TPC and also displayed the strongest DPPH^• and OH^• inhibitory activities. A strong positive-correlation between the antioxidant potency and TPC of the autoclave extracts was found, while for the boiling extracts such relation was very weak. This result thus reflected that in addition to the phenolic compounds, there might be some other active components present in these extracts involved in the antioxidant activity.