ParLeS: Software for chemometric analysis of spectroscopic data

This paper describes the development and implementation of ParLeS, chemometrics software for multivariate modelling and prediction. ParLeS is shareware that was developed for teaching and research in chemometrics and spectroscopy; however, it may also be used with other types of multivariate data. ParLeS may be used to transform, preprocess and pretreat spectra using various algorithms; it may be used to implement principal components analysis (PCA); partial least squares regression (PLSR) with leave-n-out cross validation; and bootstrap aggregation-PLSR (bagging-PLSR). ParLeS facilitates the implementation of a large number of preprocessing techniques as well as bagging-PLSR, which can improve the robustness and accuracy of PLSR models. Other unique features of ParLeS include the provision of a number of assessment statistics and graphical output as well as a user-friendly interface and functionality. The implementation of ParLeS is demonstrated by modelling soil mid infrared (mid-IR) diffuse reflectance spectra for predictions of soil organic carbon.

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

[2]  Elaine B. Martin,et al.  Model selection for partial least squares regression , 2002 .

[3]  E. V. Thomas,et al.  Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information , 1988 .

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

[5]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[6]  S. Wold,et al.  The multivariate calibration problem in chemistry solved by the PLS method , 1983 .

[7]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[8]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[9]  R. V. Rossel,et al.  Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy , 2006 .

[10]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[11]  R. V. Rossel,et al.  Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties , 2006 .

[12]  Tom Fearn.  How Reliable is an RMSEP from a Validation Set? , 2002 .

[13]  Alex B. McBratney,et al.  Multivariate calibration of hyperspectral γ‐ray energy spectra for proximal soil sensing , 2007 .

[14]  Paul Geladi,et al.  Chemometrics in spectroscopy. Part 1. Classical chemometrics , 2003 .

[15]  P. Williams,et al.  Near-Infrared Technology in the Agricultural and Food Industries , 1987 .

[16]  S. Wold.  Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .

[17]  G. E. Rayment,et al.  Australian laboratory handbook of soil and water chemical methods. , 1992 .

[18]  L.M.C. Buydens,et al.  Circular effects in representations of an RNA nucleotides data set in relation with principal components analysis , 2001 .

[19]  H. Akaike.  Fitting autoregressive models for prediction , 1969 .