Calibration Transfer Based on Affine Invariance for NIR without Transfer Standards

被引:14
作者
Zhao, Yuhui [1 ]
Zhao, Ziheng [1 ]
Shan, Peng [2 ]
Peng, Silong [3 ]
Yu, Jinlong [1 ]
Gao, Shuli [1 ]
机构
[1] Northeastern Univ, Sch Comp Sci & Engn, Shenyang 110819, Liaoning, Peoples R China
[2] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China
[3] Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
来源
MOLECULES | 2019年 / 24卷 / 09期
基金
中国国家自然科学基金;
关键词
near-infrared (NIR) spectroscopy; calibration transfer; affine invariance; multivariate calibration; partial least squares (PLS); ORTHOGONAL SIGNAL CORRECTION; NEAR-INFRARED SPECTROSCOPY; IMPROVEMENT; REGRESSION; SPECTRA;
D O I
10.3390/molecules24091802
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Calibration transfer is an important field for near-infrared (NIR) spectroscopy in practical applications. However, most transfer methods are constructed with standard samples, which are expensive and difficult to obtain. Taking this problem into account, this paper proposes a calibration transfer method based on affine invariance without transfer standards (CTAI). Our method can be utilized to adjust the difference between two instruments by affine transformation. CTAI firstly establishes a partial least squares (PLS) model of the master instrument to obtain score matrices and predicted values of the two instruments, and then the regression coefficients between each of the score vectors and predicted values are computed for the master instrument and the slave instrument, respectively. Next, angles and biases are calculated between the regression coefficients of the master instrument and the corresponding regression coefficients of the slave instrument, respectively. Finally, by introducing affine transformation, new samples are predicted based on the obtained angles and biases. A comparative study between CTAI and the other five methods was conducted, and the performances of these algorithms were tested with two NIR spectral datasets. The obtained experimental results show clearly that, in general CTAI is more robust and can also achieve the best Root Mean Square Error of test sets (RMSEPs). In addition, the results of statistical difference with the Wilcoxon signed rank test show that CTAI is generally better than the others, and at least statistically the same.
引用
收藏
页数:19
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