Related Experiment Video
Updated: Aug 29, 2026
Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall
Published on: June 10, 2017
A method for near-infrared spectral calibration of complex plant samples with wavelet transform and elimination of
Xueguang Shao1, Fang Wang, Da Chen
1Department of Chemistry, University of Science and Technology of China, 230026, Hefei, Anhui, People's Republic of China. xshao@ustc.edu.cn
Abstract:
An algorithm is proposed for extracting relevant information from near-infrared (NIR) spectra for multivariate calibration of routine components in complex plant samples. The algorithm is a combination of wavelet transform (WT) data compression and a procedure for uninformative variable elimination (UVE). After compression of the NIR spectra by WT, the UVE approach is used to eliminate the irrelevant wavelet coefficients. Finally, a calibration model is built from the retained wavelet coefficients to enable prediction. Because irrelevant information can be removed from the spectra used for multivariate calibration, the model based on the extracted relevant features is better than those obtained with full-spectrum data. Both prediction precision and calculation speed are improved.
More Related Videos
11:37RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols
Published on: August 8, 2017
06:28High Throughput Image-Based Phenotyping for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato
Published on: June 7, 2024