Influence of data pre-processing on the quantitative determination of the ash content and lipids in roasted coffee by near infrared spectroscopy

Abstract In near-infrared (NIR) measurements, some physical features of the sample can be responsible for effects like light scattering, which lead to systematic variations unrelated to the studied responses. These errors can disturb the robustness and reliability of multivariate calibration models. Several mathematical treatments are usually applied to remove systematic noise in data, being the most common derivation, standard normal variate (SNV) and multiplicative scatter correction (MSC). New mathematical treatments, such as orthogonal signal correction (OSC) and direct orthogonal signal correction (DOSC), have been developed to minimize the variability unrelated to the response in spectral data. In this work, these two new pre-processing methods were applied to a set of roasted coffee NIR spectra. A separate calibration model was developed to quantify the ash content and lipids in roasted coffee samples by PLS regression. The results provided by these correction methods were compared to those obtained with the original data and the data corrected by derivation, SNV and MSC. For both responses, OSC and DOSC treatments gave PLS calibration models with improved prediction abilities (4.9 and 3.3% RMSEP with corrected data versus 7.1 and 8.3% RMSEP with original data, respectively).

[1]  Steven D. Brown,et al.  Piecewise orthogonal signal correction , 2002 .

[2]  S. Wold,et al.  Some recent developments in PLS modeling , 2001 .

[3]  R. Sanderson,et al.  The Link between Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) Transformations of NIR Spectra , 1994 .

[4]  T. Næs,et al.  Locally weighted regression and scatter correction for near-infrared reflectance data , 1990 .

[5]  W. E. Morf.  Time-dependent selectivity behavior and dynamic response of silver halide membrane electrodes to interfering ions , 1983 .

[6]  Tom Fearn,et al.  On orthogonal signal correction , 2000 .

[7]  S. Wold,et al.  Orthogonal projections to latent structures (O‐PLS) , 2002 .

[8]  Desire L. Massart,et al.  Direct Orthogonalization: some case studies , 2001 .

[9]  D. L. Wetzel.  Near-Infrared Reflectance Analysis , 1983 .

[10]  Jordi Coello,et al.  Orthogonal signal correction in near infrared calibration , 2001 .

[11]  T. Næs,et al.  The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy , 1988 .

[12]  Olof Svensson,et al.  An evaluation of orthogonal signal correction applied to calibration transfer of near infrared spectra , 1998 .

[13]  R. Barnes,et al.  Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra , 1989 .

[14]  Age K. Smilde,et al.  Direct orthogonal signal correction , 2001 .

[15]  P. Geladi,et al.  Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat , 1985 .

[16]  S. Wold,et al.  Orthogonal signal correction of near-infrared spectra , 1998 .

[17]  W. Fred McClure,et al.  More on Derivatives: Part 2. Band Shifting and Noise , 1994 .

[18]  Tormod Næs,et al.  Near Infra-Red Spectroscopy: Bridging the Gap between Data Analysis and NIR Applications , 1995 .

[19]  W. Fred McClure.  More on Derivatives: Part 1. Segments, Gaps and “Ghosts” , 1993 .

[20]  Rinantonio Viani,et al.  Espresso coffee : the chemistry of quality , 1995 .