Provider: ingentaconnect
Database: ingentaconnect
Content: application/x-research-info-systems
TY - ABST
AU - Delwiche, Stephen R.
AU - Reeves, James B.
TI - A Graphical Method to Evaluate Spectral Preprocessing in Multivariate Regression Calibrations: Example with Savitzky–Golay Filters and Partial Least Squares Regression
JO - Applied Spectroscopy
PY - 2010-01-01T00:00:00///
VL - 64
IS - 1
SP - 73
EP - 82
KW - REGRESSION
KW - PREPROCESSING
KW - NIR SPECTROSCOPY
KW - PARTIAL LEAST SQUARES
KW - SMOOTHING
KW - SAVITZKY-GOLAY
KW - NEAR-INFRARED SPECTROSCOPY
KW - PLS
KW - DERIVATIVE
N2 - In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments,
are commonly smoothing operations or derivatives. While such operations are often useful in reducing the number of latent variables of the actual decomposition and lowering residual error, they also run the risk of misleading the practitioner into accepting calibration equations that are poorly
adapted to samples outside of the calibration. The current study developed a graphical method to examine this effect on partial least squares (PLS) regression calibrations of near-infrared (NIR) reflection spectra of ground wheat meal with two analytes, protein content and sodium dodecyl sulfate
sedimentation (SDS) volume (an indicator of the quantity of the gluten proteins that contribute to strong doughs). These two properties were chosen because of their differing abilities to be modeled by NIR spectroscopy: excellent for protein content, fair for SDS sedimentation volume. To further
demonstrate the potential pitfalls of preprocessing, an artificial component, a randomly generated value, was included in PLS regression trials. Savitzky–Golay (digital filter) smoothing, first-derivative, and second-derivative preprocess functions (5 to 25 centrally symmetric convolution
points, derived from quadratic polynomials) were applied to PLS calibrations of 1 to 15 factors. The results demonstrated the danger of an over reliance on preprocessing when (1) the number of samples used in a multivariate calibration is low (<50), (2) the spectral response of the analyte
is weak, and (3) the goodness of the calibration is based on the coefficient of determination (*R*
^{2}) rather than a term based on residual error. The graphical method has application to the evaluation of other preprocess functions and various types of spectroscopy data.
UR - http://www.ingentaconnect.com/content/sas/sas/2010/00000064/00000001/art00017
M3 - doi:10.1366/000370210790572007
UR - http://dx.doi.org/10.1366/000370210790572007
ER -