Near-infrared (NIR) spectroscopy and characteristic variables selection methods were used to develop a quick method for the determination of cellulose, hemicellulose, and lignin contents in Sargassum horneri. Calibration models for cellulose, hemicellulose, and lignin in Sargassum horneri were established using partial least square regression methods with full variables (full-PLSR). The PLSR calibration models were established by four characteristic variables selection methods, including interval partial least square (iPLS), competitive adaptive reweighted sampling (CARS), correlation coefficient (CC), and genetic algorithm (GA). The results showed that the performance of the four calibration models, namely iPLS-PLSR, CARS-PLSR, CC-PLSR, and GA-PLSR, was better than the full-PLSR calibration model. The iPLS method was best in the performance of the models. For iPLS-PLSR, the determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of the prediction set were as follows: 0.8955, 0.8232%, and 3.0934 for cellulose, 0.8669, 0.4697%, and 2.7406 for hemicellulose, and 0.7307, 0.7533%, and 1.9272 for lignin, respectively. These findings indicate that the NIR calibration models can be used to predict cellulose, hemicellulose, and lignin contents in Sargassum horneri quickly and accurately.
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- Near-infrared spectroscopy
- Rapid measurement
- Variables selection