Journal
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Volume 106, Issue 2, Pages 183-189Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.chemolab.2010.05.004
Keywords
Mid-infrared (MIR) spectrometry; Milk; Fatty acid; Genetic algorithms; Partial Least Squares (PLS) regression
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The new challenges of the dairy industry require an accurate estimation of fine milk composition. The mid-infrared (MIR) spectrometry method appears to be a good, fast and cheap method for assessing milk fatty acid profile. Although partial least squares (PLS) regression is a very useful and powerful method to determine fine milk composition from the spectra, the estimations are not always very accurate and stable over time. Therefore a genetic algorithm (GA) combined with a PLS regression was used to produce models with a reduced number of wavelengths and a better accuracy. The results are a little sensitive to the choice of parameters in the algorithm. The number of wavelengths to consider is reduced substantially by 4 and accuracy is increased on average by 15%. (C) 2010 Elsevier B.V. All rights reserved.
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