期刊
BIORESOURCES
卷 17, 期 4, 页码 6476-6489出版社
NORTH CAROLINA STATE UNIV DEPT WOOD & PAPER SCI
DOI: 10.15376/biores.17.4.6476-6489
关键词
Near infrared spectroscopy; Holocellulose; Lignin; Stable and consistent wavelength; Slope/Bias algorithm; Model transfer
资金
- Fundamental Research Funds of Research Institute of Forest New Technology, CAF [CAFYBB2019SY039]
Model transfer techniques in near infrared spectroscopy are important for avoiding duplicate modeling and reducing resources consumption. The use of the SWCSS-S/B algorithm, based on stable and consistent signals, improves the universality and accuracy of model transfer. This method reduces the predicted standard deviation of holocellulose and lignin contents, showing a significant improvement in transfer effect.
Model transfer techniques in near infrared spectroscopy are important for avoiding duplicate modeling, sharing samples and data resources, and reducing the human and material consumption required for modeling. Use of the slope/bias correction algorithm (S/B) based on screening wavelengths with consistent and stable signals (SWCSS) for model transfer is a new strategy. To enable sharing of near infrared analysis models of pulp holocellulose and lignin content in two different types of spectroscopic instruments, a combined SWCSS-S/B algorithm was proposed. The stable and consistent wavelengths between the spectroscopic instruments screened by the SWCSS method reduced the differences between the instruments, thereby improving the universality and transmission accuracy of the S/B method. The SWCSS-S/B based model transfer method reduced the predicted standard deviation RMSEP of holocellulose and lignin contents of the samples measured on the target spectrometer of the from 5.4686 and 7.6823 to 1.2133 and 1.3494, respectively. This result showed a significant improvement in the transfer effect compared to the SWCSS and S/B correction results alone, and the prediction of holocellulose was better than that of the prediction effect of lignin. The method has fewer wavelength variables involved in model transfer, fast transfer speed, and high prediction accuracy, which provides a new solution for the wide application of NIR analytical models.
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