Plant dye is a promising dyestuff to be used in textiles due to its unique environmental compatibility. However, currently there is no effective method for the identification of plant-dyed and chemical-dyed textiles. In this study, near-infrared (NIR) spectroscopy combined with three kinds of pattern recognition methods, namely soft independent modeling of class analogy (SIMCA), partial least squares (PLS) regression and principal component regression (PCR), were applied to identify cotton fabrics dyed with plant and chemical dyes. A total of 336 plant dye and chemical dye dyed cotton fabrics were prepared and the NIR spectra were collected; 267 samples were used as the calibration set, while the remaining 69 samples were used as the validation set. After pretreatment with the Savitzky–Golay first derivative, the calibration model was constructed. In the SIMCA model, the correct recognition rate values of the calibration and prediction sets were 100% and 98.55%, respectively. The PLS model showed that the number of principal components (PCs) and the correlation coefficient (R2) were 8 and 0.9978, respectively, and the results of PCR were PC = 10, R2 = 0.9937. Both methods were satisfactory for the predicted results. The overall results indicated that NIR spectroscopy could be used for rapid and nondestructive identification of plant-dyed cotton fabrics and chemical-dyed cotton fabrics.
BhuiyanMARIslamAAliA, et al.Color and chemical constitution of natural dye henna (Lawsonia inermis L) and its application in the coloration of textiles. J Clean Prod2017; 167: 14–22.
2.
BechtoldTMahmudaliAMussakR. Natural dyes for textile dyeing: a comparison of methods to assess the quality of Canadian golden rod plant material. Dyes Pigm2007; 75: 287–293.
3.
VelhoSRKBrumLFWPetterCO, et al.Development of structured natural dyes for use into plastics. Dyes Pigm2017; 136: 248–254.
4.
Oktav BulutMAkarE. Ecological dyeing with some plant pulps on woolen yarn and cationized cotton fabric. J Clean Prod2012; 32: 1–9.
5.
Mohammad ShahidS-u-IFaqeerM. Recent advancements in natural dye applications: a review. J Clean Prod2013; 53: 310–331.
6.
KhanAAIqbalNAdeelS, et al.Extraction of natural dye from red calico leaves: gamma ray assisted improvements in colour strength and fastness properties. Dyes Pigm2014; 103: 50–54.
7.
SilvaPMdSFiaschitelloTRQueirozRSd, et al.Natural dye from Croton urucurana Baill. bark: extraction, physicochemical characterization, textile dyeing and color fastness properties. Dyes Pigm2020; 173: 107953.
8.
BenliHBahtiyariMİ. Use of ultrasound in biopreparation and natural dyeing of cotton fabric in a single bath. Cellulose2014; 22: 867–877.
9.
LiHDuZ. Preparation of a highly sensitive and stretchable strain sensor of MXene/silver nanocomposite-based yarn and wearable applications. ACS Appl Mater Inter2019; 11: 45930–45938.
10.
KamelMMEl-ShishtawyRMYoussefBM, et al.Ultrasonic assisted dyeing III. Dyeing of wool with lac as a natural dye. Dyes Pigm2005; 65: 103–110.
11.
SouissiMGuesmiAMoussaA. Valorization of natural dye extracted from date palm pits (Phoenix dactylifera) for dyeing of cotton fabric. Part 1: optimization of extraction process using Taguchi design. J Clean Prod2018; 202: 1045–1055.
12.
KamelMMEl-ShishtawyRMYoussefBM, et al.Ultrasonic assisted dyeing. IV. Dyeing of cationised cotton with lac natural dye. Dyes Pigm2007; 73: 279–284.
13.
SunXYuanHSongC, et al.A novel drying-free identification method of cashmere textiles by NIR spectroscopy combined with an adaptive representation learning classification method. Microchem J2019; 149: 104018.
14.
ShiD. Identification of woollen fabrics dyed by yellow vegetable dyestuffs. Wool Text J2018; 46: 33–37.
15.
Liu W. Study on discrimination of several natural dye on silk fabric. Master Thesis, Zhejiang Sci- University, CHN, 2016.
16.
JiangWHanGViaB, et al.Rapid assessment of coniferous biomass lignin–carbohydrates with near-infrared spectroscopy. Wood Sci Technol2014; 48: 109–122.
17.
ViaBKJiangW. Nonlinear multivariate modeling of strand mechanical properties with near infrared spectroscopy. Forest Chron2013; 89: 621–630.
18.
HaffnerFBMitchellVDArundaleRA, et al.Compositional analysis of Miscanthus giganteus by near infrared spectroscopy. Cellulose2013; 20: 1629–1637.
19.
TongJXiangWLeiP, et al.Prediction of tree species composition in fine root mixed samples using near-infrared reflectance spectroscopy. Plant Biosyst 2016; 150, pp. 412–419.
20.
Hans G, Kitamura R, Inagaki T, et al. Assessment of variations in air-dry wood density using time-of-flight near-infrared spectroscopy. Wood Material Science & Engineering 2015; 10: 57–68.
21.
MayrGHintenausPZeppetzauerF, et al.A fast and accurate near infrared spectroscopy method for the determination of cellulose content of alkali cellulose applicable for process control. J Near Infrared Spectr2015; 23: 369–379.
22.
XuFZhouLZhangK, et al.Rapid determination of both structural polysaccharides and soluble sugars in sorghum biomass using near-infrared spectroscopy. BioEnerg Res2014; 8: 130–136.
23.
JiangWZhouCHanG, et al.Classification and identification of plant fibrous material with different species using near infrared technique—a new way to approach determining biomass properties accurately within different species. Front Plant Sci2017; 7: 2000.
24.
ZhouCHanGViaBK, et al.Rapid identification of fibers from different waste fabrics using the near-infrared spectroscopy technique. Text Res J2018; 89: 3610–3616.
25.
ZhouCHanGGaoS, et al.Rapid determination of cellulose content in pulp using near infrared modeling technique. BioResources2018; 13: 6122–6132.
26.
JiangWViaBKHanG, et al.Near infrared monitoring of untreated and chemically delignified wood. J Near Infrared Spectr2014; 21: 485–493.
27.
ViaBKZhouCAcquahG, et al.Near infrared spectroscopy calibration for wood chemistry: which chemometric technique is best for prediction and interpretation?. Sensors2014; 14: 13532–13547.
28.
KongWZhangCLiuF, et al.Rice seed cultivar identification using near-infrared hyperspectral imaging and multivariate data analysis. Sensors (Basel)2013; 13: 8916–8927.
29.
PieszczekLCzarnik-MatusewiczHDaszykowskiM. Identification of ground meat species using near-infrared spectroscopy and class modeling techniques - aspects of optimization and validation using a one-class classification model. Meat Sci2018; 139: 15–24.
30.
HorikawaY. Assessment of cellulose structural variety from different origins using near infrared spectroscopy. Cellulose2017; 24: 5313–5325.
31.
ŞahinSDemirCGüçerŞ. Simultaneous UV–vis spectrophotometric determination of disperse dyes in textile wastewater by partial least squares and principal component regression. Dyes Pigm2007; 73: 368–376.
32.
YangZXiaoHZhangL, et al.Fast determination of oxides content in cement raw meal using NIR-spectroscopy and backward interval PLS with genetic algorithm. Spectro Acta A2019; 223: 117327.
33.
Ju-sunZXTT-mF. Online control of dyeing rate for cotton fabric with reactive dyes. Dye Finish2015; 41: 1–5.
