Abstract

The quality of flax fiber in the textile industry is closely related to the wax content remaining on the fiber after the cleaning process. Extraction by organic solvents, which is currently used for determining wax content, is very time consuming and produces chemical waste. In this study, near-infrared (NIR) spectroscopy was used as a rapid analytical technique to develop models for wax content associated with flax fiber. Calibration samples (<i>n</i> = 11) were prepared by manually mixing dewaxed fiber and isolated wax to provide a range of wax content from 0 to 5%. A total of fourteen flax fiber samples obtained after a cleaning process were used for prediction. Principal component analysis demonstrated that one principal component is enough to separate the flax fibers by their wax content. The most highly correlated wavelengths were 2312, 2352, 1732, and 1766 nm, in order of significance. Partial least squares models were developed with various chemometric preprocessing approaches to obtain the best model performance. Two models, one using the entire region (1100–2498 nm) and the other using the selected wavelengths, were developed and the accuracies compared. For the model using the entire region, the correlation coefficient (<i>R</i>²) between actual and predicted values was 0.996 and the standard error of prediction (RMSEP) was 0.289%. For the selected-wavelengths model, the <i>R</i>² was 0.997 and RMSEP was 0.272%. The results suggested that NIR spectroscopy can be used to determine wax content in very clean flax fiber and that development of a low-cost device, using few wavelengths, should be possible.

Pattern recognition of visible and near-infrared spectroscopy from bayberry juice by use of partial least squares and a backpropagation neural network

Haiyan Cen, Yidan Bao, and Yong He
Appl. Opt. 45(29) 7679-7683 (2006)

Acidity measurement of iron ore powders using laser-induced breakdown spectroscopy with partial least squares regression

Z.Q. Hao, C.M. Li, M. Shen, X.Y. Yang, K.H. Li, L.B. Guo, X.Y. Li, Y.F. Lu, and X.Y. Zeng
Opt. Express 23(6) 7795-7801 (2015)

High accuracy determination of copper in copper concentrate with double genetic algorithm and partial least square in laser-induced breakdown spectroscopy

Haochen Li, Meizhen Huang, and Huidi Xu
Opt. Express 28(2) 2142-2155 (2020)

Partial least squares regression calculation for quantitative analysis of metals submerged in water measured using laser-induced breakdown spectroscopy

Tomoko Takahashi, Blair Thornton, Takumi Sato, Toshihiko Ohki, Koichi Ohki, and Tetsuo Sakka
Appl. Opt. 57(20) 5872-5883 (2018)

Nondestructive determination of SSC in an apple by using a portable near-infrared spectroscopy system

Yizhe Zhang, Jipeng Huang, Qiulei Zhang, Jinwei Liu, Yanli Meng, and Yan Yu
Appl. Opt. 61(12) 3419-3428 (2022)