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Principal component analysis (PCA) including procedures as Is commonly performed using various implementations of
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Keywords: PLS, partial least squares, principal components, PCA, SAS®, discriminant analysisĬlassification or discriminant analysis using spectral data Please note that the pr ogram will not work properly on The program allowsįor testing of multiple spectral pre-treatments in a one-step fashion with summary of all results. Examples are presented using two datasets: forages and by-products, and grains. The authors’ previously written SAS® macros for pre-treatment of This paper discusses a SAS® program designed to perform classification/discriminant analysis using SAS® PLS, and to a smallerĮxtent, principal component analysis and reduced rank regression. Thus, for four classes, all samples are coded with one of four analyte combinations (1,0,0,0 0,1,0,0 0,0,1,0 orĠ,0,0,1). For discriminant analysis, samples belonging to one of Z classes are coded for Z analytes with all but one (class to which sample belongs codedĪs 1) coded as being a 0. Partial least squares analysis is implemented in SAS® as type 2 whereĪ solution for multiple analytes ( Y-variables) is determined simultaneously, but cannot work with non-numeric analyte values. This was done in combination with previous efforts, which implemented data pre-treatments including scatter correction, derivatives, mean centring and variance scaling for spectral analysis. The objective of this work was to implement discriminant analysis using SAS® partial least squares (PLS) regression for analysis of spectral data. USDA, ARS, Environmental Management and Byproduct Utilization Laboratory, Bldg 306, BARC East, Beltsville, MD 20705, USA.Į-mail: ARS, Food Safety Laboratory, Bldg 303, BARC East, Beltsville, MD 20705, USA SAS® partial least squares for discriminant Received: 26 December 2006 n Revised: 9 November 2007 n Accepted: 14 November 2007 n Publication: 13 February 2008