Liu Yan, Cai Wensheng, Shao Xueguang
Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), State Key Laboratory of Medicinal Chemical Biology, and Research Center for Analytical Sciences, College of Chemistry, Nankai University, Tianjin 300071, China.
Anal Chim Acta. 2014 Jul 11;836:18-23. doi: 10.1016/j.aca.2014.05.036. Epub 2014 May 28.
Calibration model transfer is essential for practical applications of near infrared (NIR) spectroscopy because the measurements of the spectra may be performed on different instruments and the difference between the instruments must be corrected. An approach for calibration transfer based on alternating trilinear decomposition (ATLD) algorithm is proposed in this work. From the three-way spectral matrix measured on different instruments, the relative intensity of concentration, spectrum and instrument is obtained using trilinear decomposition. Because the relative intensity of instrument is a reflection of the spectral difference between instruments, the spectra measured on different instruments can be standardized by a correction of the coefficients in the relative intensity. Two NIR datasets of corn and tobacco leaf samples measured with three instruments are used to test the performance of the method. The results show that, for both the datasets, the spectra measured on one instrument can be correctly predicted using the partial least squares (PLS) models built with the spectra measured on the other instruments.
校准模型转移对于近红外（NIR）光谱的实际应用至关重要，因为光谱测量可能在不同仪器上进行，且仪器之间的差异必须得到校正。本文提出了一种基于交替三线性分解（ATLD）算法的校准转移方法。从在不同仪器上测量得到的三维光谱矩阵中，利用三线性分解获得浓度、光谱和仪器的相对强度。由于仪器的相对强度反映了仪器之间的光谱差异，通过校正相对强度中的系数，可以对在不同仪器上测量的光谱进行标准化。使用三个仪器测量的玉米和烟叶样本的两个近红外数据集来测试该方法的性能。结果表明，对于这两个数据集，使用在其他仪器上测量的光谱建立的偏最小二乘法（PLS）模型能够正确预测在一个仪器上测量的光谱。
