Textile Fiber Identification Using Near-Infrared Spectroscopy and Pattern Recognition

• Autorzy: Zhou Jinfeng , Yu Lingjie , Ding Qian , Wang Rongwu

Identyfikatory

DOI

10.1515/aut-2018-0055

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

textile fibers; fibers identification; NIR; spectroscopy method; pattern recognition

PL

włókna tekstylne; identyfikacja włókien; metoda spektroskopii; NIR; rozpoznawanie wzorców

• Wydawca: Lodz University of Technology, Faculty of Material Technologies and Textile Design
• Czasopismo: Autex Research Journal
• Rocznik: 2019
• Tom: Vol. 19, no. 2
• Strony: 201--209
• Opis fizyczny: Bibliogr. 33 poz.

Twórcy

autor

Zhou Jinfeng

• Key Laboratory of Textile Science & Technology, Ministry of Education, College of Textiles, Donghua University, Shanghai, China

autor

Yu Lingjie

• School of Textile Science&Engineering, Xi’an Polytechnic University, Shaanxi Province, China

autor

Ding Qian

• Key Laboratory of Textile Science & Technology, Ministry of Education, College of Textiles, Donghua University, Shanghai, China

autor

Wang Rongwu

• Key Laboratory of Textile Science & Technology, Ministry of Education, College of Textiles, Donghua University, Shanghai, China

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).