A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples

作者：Li Yankun; Shao Xueguang; Cai Wensheng^*

来源：Talanta, 2007, 72(1): 217-222.

DOI：10.1016/j.talanta.2006.10.022

• consensus modeling
• least squares support vector regression (LS-SVR)
• near-infrared spectroscopy
• quantitative analysis

Consensus modeling of combining the results of multiple independent models to produce a single prediction avoids the instability of single model. Based on the principle of consensus modeling, a consensus least squares support vector regression (LS-SVR) method for calibrating the near-infrared (NIR) spectra was proposed. In the proposed approach, NIR spectra of plant samples were firstly preprocessed using discrete wavelet transform (DWT) for filtering the spectral background and noise, then, consensus LS-SVR technique was used for building the calibration model. With an optimization of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of reducing sugar in plant samples. The predicted results show that consensus LS-SVR model is more robust and reliable than the conventional partial least squares (PLS) and LS-SVR methods.

• 出版日期2007-4-15
• 单位南开大学