title: An augmented classical least squares method for quantitative Raman spectral analysis against component information loss.
abstract: We propose an augmented classical least squares (ACLS) calibration method for quantitative Raman spectral analysis against component information loss. The Raman spectral signals with low analyte concentration correlations were selected and used as the substitutes for unknown quantitative component information during the CLS calibration procedure. The number of selected signals was determined by using the leave-one-out root-mean-square error of cross-validation (RMSECV) curve. An ACLS model was built based on the augmented concentration matrix and the reference spectral signal matrix. The proposed method was compared with partial least squares (PLS) and principal component regression (PCR) using one example: a data set recorded from an experiment of analyte concentration determination using Raman spectroscopy. A 2-fold cross-validation with Venetian blinds strategy was exploited to evaluate the predictive power of the proposed method. The one-way variance analysis (ANOVA) was used to access the predictive power difference between the proposed method and existing methods. Results indicated that the proposed method is effective at increasing the robust predictive power of traditional CLS model against component information loss and its predictive power is comparable to that of PLS or PCR.
authors: School of Energy & Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
authors: Yan
authors: Y
authors: Zhou
authors: Hui
authors: H
authors: Cao
mesh_terms: D016018:Least-Squares Analysis; D015233:Models, Statistical; D025341:Principal Component Analysis; D013059:Spectrum Analysis, Raman / Q000379:methods*
references: Appl Spectrosc. 2012 Dec;66(12):1468-74
references: 23231910
references: Trends Analyt Chem. 2009 Oct 1;28(9):1127-1136
references: 20160977
references: Appl Spectrosc. 2005 Jun;59(6):816-25
references: 16053548
references: Food Chem. 2013 Jun 1;138(2-3):1829-36
references: 23411315
references: Spectrochim Acta A Mol Biomol Spectrosc. 2013 Feb;102:403-7
references: 23237846
references: Anal Chem. 2008 Jul 1;80(13):4951-9
references: 18529019
references: Appl Spectrosc. 2004 Sep;58(9):1065-73
references: 15479523
references: Anal Chem. 2010 Jun 15;82(12):4699-711
references: 20481510
references: Appl Opt. 2007 Apr 1;46(10):1726-34
references: 17356615