Automatic Identification of Ramie and Cotton Fibers Using Characteristics in Longitudinal View, Part II: Fiber Stripes Analysis

Wang, Rong Wu; Wu, Xiong Ying; Wang, Shan Yuan; Xu, Bugao

doi:10.1177/0040517508089755

Cited by 8 publications

(16 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this method has certain limitations that cause a reduction of accuracy: on the one hand, handle vision is easily affected by the subjective judgments of inspectors, and on the other hand, through after-finish techniques, many synthetic fibers are finished to possess similar surface parameters, which confuse the identification process. The microscope [1][2][3][4][5], usually accompanied by image processing [6,7], has been the primary tool for fiber analysis, and its applications range from simple stereomicroscopy through to scanning electron microscopy (SEM), which help in visualizing the longitudinal and cross-sectional characteristics. The longitudinal characteristic of the cotton fiber is its natural twist, the wool fiber has scale, and the surface of the silk fiber is smooth and its section is an…”

Section: Introductionmentioning

confidence: 99%

Textile Fiber Identification Using Near-Infrared Spectroscopy and Pattern Recognition

Zhou

Ding

et al. 2019

Autex Research Journal

View full text Add to dashboard Cite

Fibers are raw materials used for manufacturing yarns and fabrics, and their properties are closely related to the performances of their derivatives. It is indispensable to implement fiber identification in analyzing textile raw materials. In this paper, seven common fibers, including cotton, tencel, wool, cashmere, polyethylene terephthalate (PET), polylactic acid (PLA), and polypropylene (PP), were prepared. After analyzing the merits and demerits of the current methods used to identify fibers, near-infrared (NIR) spectroscopy was used owing to its significant superiorities, the foremost of which is it can capture the tiny information differences in chemical compositions and morphological features to display the characteristic spectral curve of each fiber. First, the fibers’ spectra were collected, and then, the relationships between the vibrations of characteristic chemical groups and the corresponding wavelengths were researched to organize a spectral information library that would be beneficial to achieve quick identification and classification. Finally, to achieve intelligent detection, pattern recognition approaches, including principal component analysis (PCA) (used to extract information of interest), soft independent modeling of class analogy (SIMCA), and linear discrimination analysis (LDA) (defined using two classifiers), assisted in accomplishing fiber identification. The experimental results – obtained by combining PCA and SIMCA – displayed that five of seven target fibers, namely, cotton, tencel, PP, PLA, and PET, were distributed with 100% recognition rate and 100% rejection rate, but wool and cashmere fibers yielded confusing results and led to relatively low recognition rate because of the high proportion of similarities between these two fibers. Therefore, the six spectral bands of interest unique to wool and cashmere fibers were selected, and the absorbance intensities were imported into the classifier LDA, where wool and cashmere were group-distributed in two different regions with 100% recognition rate. Consequently, the seven target fibers were accurately and quickly distinguished by the NIR method to guide the fiber identification of textile materials.

show abstract

Section: Introductionmentioning

confidence: 99%

Textile Fiber Identification Using Near-Infrared Spectroscopy and Pattern Recognition

Zhou

Ding

et al. 2019

Autex Research Journal

View full text Add to dashboard Cite

show abstract

“…These methods are mainly based on the visual characteristics of individual cotton and ramie fibers. [1][2][3] Both ramie and cotton fibers are natural cellulosic fibers. The variations in the appearances of individual ramie and cotton fibers inevitably lower the recognition rate of any method based on appearance characteristics alone.…”

mentioning

confidence: 99%

Automatic identification of ramie and cotton fibers based on iodine blue reaction, Part II: Automatic identification of ramie and cotton using color, texture, shape, and stripes

Jia

2016

Textile Research Journal

View full text Add to dashboard Cite

A new method of automatically identifying ramie and cotton fibers using analysis of shape, color, texture, and surface stripes is here introduced. The shape, color, texture, and stripe features of the fibers were extracted by transformation from red, green, blue images to hue saturation intensity images and then to grayscale and binary images, by segmentation of the fiber from background, by edge detection of the outline of fibers, and by stripes on the surface of the fiber. Eighteen characteristic parameters suitable for identification were selected according to their probability distribution curves. A three-layer multilayer perceptron artificial-neural-network-based prediction system is here presented as a means of distinguishing cotton fibers from ramie fibers. The system training was carried out using a back propagation algorithm. The proposed system was tested on more than 2000 cotton and more than 2000 ramie fibers. The experimental results showed that the overall tolerance for false identification of cotton or ramie fiber was under 5%.

show abstract

“…For example, a series of studies using analysis of the stripes on fiber surfaces in a longitudinal view has been published. 1–3 The problem is that, in some cases, the appearances of ramie and cotton are so similar that it is impossible to identify them correctly by appearance characteristics alone, especially between the top ends of ramie and fine cotton fiber.…”

mentioning

confidence: 99%

Automatic identification of ramie and cotton fibers based on iodine blue reaction, Part I: the optimum conditions for the iodine blue reaction of cellulose

Man

Liu

et al. 2015

Textile Research Journal

View full text Add to dashboard Cite

A new iodine blue staining technique to distinguish ramie fibers from cotton fibers was presented. The influencing factors on staining color, including temperature, concentration of iodide anions in I2-YI solution, ratio of the volumes of added I2-YI solution and saturated LiX solution, time, and concentrations of alkali metal cation Y+ and halide anion X–, were investigated to maximize the difference in color between stained cotton and ramie fibers. The optimum conditions were as follows: temperature: –15℃; concentration of iodide anion in colorant: 1/5; volume ratio: 1/20; colorant: I2-LiI solution; swelling reagent: saturated LiCl solution. The experimental results showed that the new staining technique can be useful for automatic identification of cotton and ramie fibers.

show abstract

Automatic Identification of Ramie and Cotton Fibers Using Characteristics in Longitudinal View, Part II: Fiber Stripes Analysis

Cited by 8 publications

References 8 publications

Textile Fiber Identification Using Near-Infrared Spectroscopy and Pattern Recognition

Textile Fiber Identification Using Near-Infrared Spectroscopy and Pattern Recognition

Automatic identification of ramie and cotton fibers based on iodine blue reaction, Part II: Automatic identification of ramie and cotton using color, texture, shape, and stripes

Automatic identification of ramie and cotton fibers based on iodine blue reaction, Part I: the optimum conditions for the iodine blue reaction of cellulose

Contact Info

Product

Resources

About