Rapid Determination of Cotton Content in Textiles by Near-Infrared Spectroscopy and Interval Partial Least Squares

ABSTRACT

Textile products must be marked by fabric type and composition on the label and cotton is by far the most important fiber in the industry and often needs fast quantitative analysis. The corresponding standard methods are very time-consuming and labor-intensive. The work focuses on exploring the feasibility of combining near-infrared (NIR) spectroscopy and interval-based partial least squares (iPLS) for determining cotton content in textiles. Three types of partial least square (PLS)-based algorithms were used for experimental measurements. A total of 91 cloth samples with cotton content ranging from 0 to 100% (w/w) were collected and all compositions are commercially available on the market in China. In all cases, the original spectrum axis was split into 20 subintervals. As a result, three final models, i.e., the iPLS model on a single subinterval, the backward interval partial least squares (biPLS) model on the region remaining six subintervals, and the moving window partial least squares (mwPLS) model with a window of 75 variables, achieved better results than the full-spectrum PLS model. Also, no obvious differences in performance were observed for the three models. Thus, either iPLS or mwPLS was preferred considering their simplicity, which suggested that iPLS and mwPLS combined with NIR technique may have potential for the rapid determination of the cotton content of textile products with comparable accuracy to standard procedures. In addition, this approach may have commercial and regulatory advantages that avoid labor-intensive and time-consuming chemical analysis.

KEYWORDS:

• Cotton
• interval partial least squares (iPLS)
• near-infrared (NIR) spectroscopy
• partial least squares
• textile

Conflicts of interests

The authors declare that there is no conflicts of interest regarding the publication of this paper.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (21375118, J1310041), Scientific Research Foundation of Sichuan Provincial Education Department of China (17TD0048), Opening Fund of Key Lab of Process Analysis and Control of Sichuan Universities of China (2015006, 2016002), and Scientific Research Foundation of Yibin University.