Linear model correction: A method for transferring a near-infrared multivariate calibration model without standard samples
Linear model correction: A method for transferring a near-infrared multivariate calibration model without standard samples
复制标题
线性模型校正：一种无需标准样品即可传输近红外多元校准模型的方法
DOI：
10.1016/j.saa.2016.06.041
复制
发表时间：
2016-12-05
影响因子：
4.4
通讯作者：
Shao, Xueguang
中科院分区：
文献类型：
--
作者：
Liu, Yan;Cai, Wensheng;Shao, Xueguang
校准传递对于近红外（NIR）光谱的实际应用是必不可少的，因为光谱的测量可以在不同的仪器上进行，并且必须校正仪器之间的差异。对于大多数的校准传递方法，标准样品是必要的，以建立传递模型使用的两个仪器，分别命名为主，从仪器上测量的样品的光谱。本文提出了一种无标样的线性模型校正方法。该方法是基于这样一个事实，即对于具有相似物理和化学性质的样品，在不同仪器上测量的光谱是线性相关的。这使得由不同仪器测得的光谱所建立的线性模型的系数在剖面上是相似的。因此，采用约束优化方法，只需在从机上测量少量光谱，就可以将主模型的系数转换为从机模型的系数。用两个不同仪器测量的玉米和植物叶片样品的近红外数据集对该方法进行了验证。结果表明，对于这两个数据集，光谱可以正确地预测使用转移偏最小二乘（PLS）模型。由于该方法不需要标准样品，因此在实际应用中可能更有用。(C)2016爱思唯尔B.V.保留所有权利。
Calibration 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. For most of calibration transfer methods, standard samples are necessary to construct the transfer model using the spectra of the samples measured on two instruments, named as master and slave instrument, respectively. In this work, a method named as linear model correction (LMC) is proposed for calibration transfer without standard samples. The method is based on the fact that, for the samples with similar physical and chemical properties, the spectra measured on different instruments are linearly correlated. The fact makes the coefficients of the linear models constructed by the spectra measured on different instruments are similar in profile. Therefore, by using the constrained optimization method, the coefficients of the master model can be transferred into that of the slave model with a few spectra measured on slave instrument Two NIR datasets of corn and plant leaf samples measured with different instruments are used to test the performance of the method. The results show that, for both the datasets, the spectra can be correctly predicted using the transferred partial least squares (PLS) models. Because standard samples are not necessary in the method, it may be more useful in practical uses. (C) 2016 Elsevier B.V. All rights reserved.