School of Chemistry and Chemical Engineering, Central South University, Changsha, Hunan 410083, PR China.
Anal Chim Acta. 2010 Feb 28;661(2):133-42. doi: 10.1016/j.aca.2009.12.026. Epub 2009 Dec 24.
We report the use of stacked partial least-squares regression and stacked dual-domain regression analysis with four commonly used techniques for calibration transfer to improve predictive performance from transferred multivariate calibration models. The predictive performance from three conventional calibration transfer methods, piecewise direct standardization (PDS), orthogonal signal correction (OSC) and model updating (MUP), requiring standards measured on both instruments, was significantly improved from data fusion either by stacking of wavelet scales or by stacking of spectral intervals, as demonstrated by transfer of calibrations developed on near-infrared spectra of synthetic gasoline. Stacking did not produce as significant an improvement for calibration transfer using a finite impulse response (FIR) filter, but application of SPLS regression to FIR-transferred spectra improves predictive performance of the transferred model.
我们报告了使用堆叠偏最小二乘回归和堆叠双域回归分析，以及四种常用技术进行校准转移，以提高从转移多元校准模型获得的预测性能。通过数据融合，使用堆叠小波尺度或堆叠光谱区间，对三种传统校准转移方法（分段直接标准化（PDS）、正交信号校正（OSC）和模型更新（MUP））的校准进行转移，从在合成汽油的近红外光谱上开发的校准中显著提高了预测性能，这需要在两种仪器上测量标准品。对于使用有限脉冲响应（FIR）滤波器的校准转移，堆叠并没有产生如此显著的改进，但将 SPLS 回归应用于 FIR 转移的光谱可以提高转移模型的预测性能。
