Determination of cellulose, hemicellulose and lignin content using near-infrared spectroscopy in flax fiber

The rapid and accurate determination of flax fiber composition is necessary for its application, but until now it has mainly been tested by the wet chemical method, which is time-consuming and not environmentally friendly. In this paper, near-infrared (NIR) spectroscopy was studied to determinate the main composition of flax, in which 43 flax samples were tested according to the traditional Chinese wet chemical component test standard. Five sets of spectra were generated to show the characteristic of each sample; in total 215 spectra sets were collected using a Fourier transform near-infrared spectrometer. The methods of partial least squares (PLS) and principal component regression (PCR) were used to establish the relationships between the data from the chemical and NIR methods. PLS proved to be a better quantitative method than PCR, based on the value of the coefficient of multiple determination for calibration ( Rc ² ) and prediction ( Rp ² ), the ratio of performance to standard deviate (RPD) and the root mean square error of prediction (RMSEP). With the best pretreatment method, the spectral range of 10,000–4000 cm ^–1 yielded a better predictive result than the full range, with Rc ² of 0.968, Rp ² of 0.955, RMSEP of 1.060%, RPD of 4.641 for cellulose and Rc ² of 0.958, Rp ² of 0.906, RMSEP of 0.678%, RPD of 3.305 for hemicellulose, while the spectral range 6900–5600 cm ^–1 yielded a better predictive result with Rc ² of 0.936, Rp ² of 0.769, RMSEP of 0.455%, and RPD of 2.366 for lignin. The study shows that NIR models can provide a simple and fast way to analyze flax fiber composition, which is also beneficial to evaluate its quality.