journal article·2018-01

Measurement of short fiber contents in raw cotton using dual-beard images

Jingye Jin, Bugao Xu, Fumei Wang

Textile Research Journal· Volume 88· Issue 1
Publisher page

Abstract

The short fiber content (SFC) in raw cotton is an extremely important attribute in the modern cotton classification system, because of its impact on the quality of yarn manufacturing. However, the current SFC measuring methods are costly, time-consuming and tedious for accurate and quick testing. This study aimed to develop a new way to calculate a weight-based short fiber content ( SFC w ) from a dual-beard specimen via image-processing and bell-shaped fibrogram modeling techniques. A sample from a cotton sliver was clamped and combed in the opposite directions to form two tapered fringes, and then scanned on a desktop scanner to generate a dual-beard image (DBI). The grayscale values in each column of the DBI were accumulated to derive a bell-shaped fibrogram representing the fiber quantity distributions over the distance from the fibrogram center. Based on the fibrogram, a special hierarchical model was proposed to calculate SFC w in two different length limits—12.7 and 16 mm—defined respectively in the USA and China’s specifications. Fifteen samples were tested to compare the SFC w results from the DBIs to those from the Advanced Fiber Information System (AFIS PRO2). Significant correlations were found between the SFC w values from DBIs and AFIS. The regressive equations for SFC w prediction were validated with six additional samples. As shown in the Bland–Altman plots, a good agreement existed between the two sets of SFC w values, demonstrating the DBI’s potential to be an accurate, rapid and portable approach for measuring SFC w , and to transform the SFC w testing from a dedicated machine to an off-the-shelf scanner.
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Research context

OpenAlex topics: Textile materials and evaluations · Industrial Vision Systems and Defect Detection · Research in Cotton Cultivation

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

College of Textiles, Donghua University, China

Department of Merchandising and Digital Retailing, University of North Texas, USA

Key Laboratory of Textile Science & Technology, Ministry of Education, China

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