Abstract

Journal of Near Infrared Spectroscopy
Volume 11 Issue 6, Pages 415–431 (2003)
doi: 10.1255/jnirs.393

SAS^® partial least squares regression for analysis of spectroscopic data

James B. Reeves, III^a and Stephen R. Delwiche^b
aUSDA, ARS, Animal Manure and By-Products Laboratory, Bldg 306, BARC East, Beltsville, MD 20705, USA
^bUSDA, ARS, Instrumentation and Sensing Laboratory, Bldg 303, BARC East, Beltsville, MD 20705, USA

The objective was to investigate the potential of SAS^® partial least squares (PLS) to perform chemometric analysis of spectroscopic data. As implemented, SAS^® (Version 8) can perform PLS regression (PLSR, Type II only), principal components and reduced rank regression. While possessing several algorithms for PLSR, various cross-validation options, the ability to mean centre and variance scale data prior to PLSR analysis or for each cross-validation and various options for determining the number of factors to use, SAS^® does not possess any other spectral pre- treatments routinely used in spectroscopy. A program was written using SAS^® macro language to implement 1st and 2nd gap derivatives, Savitzky–Golay derivatives and smoothing, the ability to skip or average spectral data points, to correct spectra for scatter correction by either multiplicative scatter correction or standard normal variate correction with or without detrend and, finally, to mean centre all data prior to regression analysis. In addition, an F-test method for factor selection was added. These macros can be implemented alone or in differing combinations or order, and result in a summary report containing results for hundreds or thousands of different data pre-treatments. A second program implements the macros in a fixed order. Results using a set of 67 forage samples, scanned in the near infrared, demonstrated that the same results can be achieved as with commercial chemometrics packages. In conclusion, SAS^® PLS, while not possessing all the data pre-treatments of standard chemometric programs, can quickly and conveniently test many different data pre-treatments resulting in a single summary results file.

Keywords: SAS, PLS, pre-treatment, near infrared

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