SCREAM: a novel method for multi-way regression problems with shifts and shape changes in one mode

TY - JOUR

T1 - SCREAM

T2 - a novel method for multi-way regression problems with shifts and shape changes in one mode

AU - Marini, Federico

AU - Bro, Rasmus

PY - 2013/11/15

Y1 - 2013/11/15

N2 - Some fields where calibration of multi-way data is required, such as hyphenated chromatography, can suffer of high inaccuracy when traditional N-PLS is used, due to the presence of shifts or peak shape changes in one of the modes. To overcome this problem, a new regression method for multi-way data called SCREAM (Shifted Covariates REgression Analysis for Multi-way data), which is based on a combination of PARAFAC2 and principal covariates regression (PCovR), is proposed. In particular, the algorithm combines a PARAFAC2 decomposition of the X array and a PCovR-like way of computing the regression coefficients, analogously to what has been described by Smilde and Kiers (A.K. Smilde and H.A.L. Kiers, 1999) in the case of other multi-way PCovR models. The method is tested on real and simulated datasets providing good results and performing as well or better than other available regression approaches for multi-way data.

AB - Some fields where calibration of multi-way data is required, such as hyphenated chromatography, can suffer of high inaccuracy when traditional N-PLS is used, due to the presence of shifts or peak shape changes in one of the modes. To overcome this problem, a new regression method for multi-way data called SCREAM (Shifted Covariates REgression Analysis for Multi-way data), which is based on a combination of PARAFAC2 and principal covariates regression (PCovR), is proposed. In particular, the algorithm combines a PARAFAC2 decomposition of the X array and a PCovR-like way of computing the regression coefficients, analogously to what has been described by Smilde and Kiers (A.K. Smilde and H.A.L. Kiers, 1999) in the case of other multi-way PCovR models. The method is tested on real and simulated datasets providing good results and performing as well or better than other available regression approaches for multi-way data.

U2 - 10.1016/j.chemolab.2013.09.012

DO - 10.1016/j.chemolab.2013.09.012

M3 - Journal article

SN - 0169-7439

VL - 129

SP - 64

EP - 75

JO - Chemometrics and Intelligent Laboratory Systems

JF - Chemometrics and Intelligent Laboratory Systems

ER -