Maintaining the predictive abilities of multivariate calibration models by spectral space transformation

Du, Wei; Chen, Zengping; Zhong, Li-Jing; Wang, Shuxia; Yu, Ru‐Qin; Nordon, Alison; Littlejohn, David; Holden, Megan

doi:10.1016/j.aca.2011.02.014

Cited by 122 publications

(74 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Spectral space transformation [26,27] has employed the strategy for eliminating the spectral differences induced by the changes in instruments or measurement conditions. Spectral space transformation [26,27] has employed the strategy for eliminating the spectral differences induced by the changes in instruments or measurement conditions.…”

Section: Theory and Calculationsmentioning

confidence: 99%

“…where C is the chromatographic matrix of all components in the sample and can be considered to be the common matrix of X mix and X s ; S T mix and S T s are the spectral matrices of all components in the sample and the analyte in the standard, respectively; T is the score matrix; P T mix and P T s are the sub-matrices of loadings, corresponding to the sample and standard. Spectral space transformation [26,27] has employed the strategy for eliminating the spectral differences induced by the changes in instruments or measurement conditions. Because the same T is used, and the differences in concentration of the target analyte in the sample and standard can be measured by the loadings P T mix and P T s .…”

Section: Theory and Calculationsmentioning

confidence: 99%

See 1 more Smart Citation

Standard signal extraction for analyzing target analytes in real samples with complex matrices

Cai

Shao

2015

Journal of Chemometrics

View full text Add to dashboard Cite

A second-order method, standard signal extraction was proposed for quantitative analysis of target analytes in the samples with complex matrices by gas chromatography-mass spectrometry. In the method, standard addition was adopted. The data of the pure standard, sample, and spiked sample were used in the calculation. By performing principal component analysis on the data aligned with the measured signals of the sample and standard, the standardized signal that is orthogonal to the signals of interferences in the sample can be extracted. Then, the signal of spiked sample was projected to the standardized signal. The concentration ratio of the target analyte in the sample and spiked sample can be obtained. Finally, the concentration of the target analyte in the sample can be calculated with the added concentration in the spiked sample. Both simulated and experimental data were investigated with the proposed approach, compared with rank annihilation factor analysis. The recoveries of standard addition were found in a range of 99-105%, and the relative standard deviations obtained in three repeated measurements were less than 3%. Results show that the method can accurately estimate the quantitative information of analytes of interest in the presence of interferents and can be applied more widely compared with rank annihilation factor analysis.

show abstract

Section: Theory and Calculationsmentioning

confidence: 99%

Section: Theory and Calculationsmentioning

confidence: 99%

Standard signal extraction for analyzing target analytes in real samples with complex matrices

Cai

Shao

2015

Journal of Chemometrics

View full text Add to dashboard Cite

show abstract

“…More advanced alternative is wavelet transform‐based standardization (WT), in which WT coefficients of selected standardization sample spectra from both instruments are univariately regressed to achieve correspondence . Another technique is finite impulse response method that does not require spectra to be measured on both instruments . Spectral space transformation, which applies wavelength selection and spectral pre‐processing, has been used to address calibration transfer .…”

Section: Introductionmentioning

confidence: 99%

Transfer of multivariate regression models between high‐resolution NMR instruments: application to authenticity control of sunflower lecithin

Monakhova

Diehl²

2016

Magnetic Reson in Chemistry

View full text Add to dashboard Cite

In recent years the number of spectroscopic studies utilizing multivariate techniques and involving different laboratories has been dramatically increased. In this paper the protocol for calibration transfer of partial least square regression model between high-resolution nuclear magnetic resonance (NMR) spectrometers of different frequencies and equipped with different probes was established. As the test system previously published quantitative model to predict the concentration of blended soy species in sunflower lecithin was used. For multivariate modelling piecewise direct standardization (PDS), direct standardization, and hybrid calibration were employed. PDS showed the best performance for estimating lecithin falsification regarding its vegetable origin resulting in a significant decrease in root mean square error of prediction from 5.0 to 7.3% without standardization to 2.9-3.2% for PDS. Acceptable calibration transfer model was obtained by direct standardization, but this standardization approach introduces unfavourable noise to the spectral data. Hybrid calibration is least recommended for high-resolution NMR data. The sensitivity of instrument transfer methods with respect to the type of spectrometer, the number of samples and the subset selection was also discussed. The study showed the necessity of applying a proper standardization procedure in cases when multivariate model has to be applied to the spectra recorded on a secondary NMR spectrometer even with the same magnetic field strength. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

“…The theory and application of PLS in spectrometry have been discussed by several researchers [7]. Recently, a PLS model has been developed including interval partial least square (iPLS) [8], genetic algorithm-interval partial least square (GA-iPLS) [9], forward interval partial least square (FiPLS) [10], backward interval partial least-square (BiPLS) [10], genetic algorithm-partial least square (GA-PLS) [11], and moving windowpartial least square (MWPLS) [12].…”

Section: Introductionmentioning

confidence: 99%

A novel model selection strategy using total error concept

Lin

et al. 2013

Talanta

View full text Add to dashboard Cite

Maintaining the predictive abilities of multivariate calibration models by spectral space transformation

Cited by 122 publications

References 34 publications

Standard signal extraction for analyzing target analytes in real samples with complex matrices

Standard signal extraction for analyzing target analytes in real samples with complex matrices

Transfer of multivariate regression models between high‐resolution NMR instruments: application to authenticity control of sunflower lecithin

A novel model selection strategy using total error concept

Contact Info

Product

Resources

About