@article {Pérez-Marín:2008-05-01T00:00:00:0003-7028:536,
author = "Pérez-Marín, D. and Garrido-Varo, A. and Guerrero, J. E. and Fearn, T. and Davies, A. M. C.",
title = "Advanced Nonlinear Approaches for Predicting the Ingredient Composition in Compound Feedingstuffs by Near-Infrared Reflection Spectroscopy",
journal = "Applied Spectroscopy",
volume = "62",
number = "5",
year = "2008-05-01T00:00:00",
abstract = "For quantitative applications, the most common usage of near-infrared reflection spectroscopy (NIRS) technology, calibration involves establishing a mathematical relationship between spectral data and data provided by the reference. This model may be fairly complex, since the near-infrared
spectrum is highly variable and contains physical/chemical information for the sample that may be redundant, and multivariate calibration is usually required. When the relationship to be modeled is nonlinear, classical regression methods are inadequate, and more complex strategies and algorithms
must be sought in order to model this nonlinearity. The development of NIRS calibrations to predict the ingredient composition, i.e., the inclusion percentage of each ingredient, in compound feeds is a complex task, due to the nature of the parameters to be predicted and to the heterogeneous
nature of the matrices/formulas in which each ingredient participates. The present paper evaluates the use of least squares support vector machines (LSSVM) and two local calibration methods, CARNAC and locally biased regression, for developing NIRS models to predict two of the most representative
ingredients in compound feed formulations, wheat and sunflower meal, using a large spectral library of 7523 commercial compound feed samples. For both ingredients, the best results were obtained using CARNAC, with standard errors of prediction (SEP) of 1.7% and 0.60% for wheat and sunflower
meal, respectively, and even better results when the algorithm was allowed to refuse to predict 10% of the unknowns. Meanwhile, LSSVM performed less well on wheat (SEP 2.6%) but comparably on sunflower meal (SEP 0.60%), giving results very similar to those reported previously for artificial
neural networks. Locally biased regression was the least successful of the three methods, with SEPs of 3.3% for wheat and 0.72% for sunflower meal. All the nonlinear methods improved on the standard approach using partial least squares (PLS), which gave SEPs of 5.3% for wheat and 0.81% for
sunflower meal.",
pages = "536-541",
url = "http://www.ingentaconnect.com/content/sas/sas/2008/00000062/00000005/art00016",
doi = "doi:10.1366/000370208784344389",
keyword = "PLS, INGREDIENT PERCENTAGE, NONLINEAR CALIBRATION, LOCAL CALIBRATION, LEAST SQUARES SUPPORT VECTOR MACHINES, NIRS, LOCALLY BIASED REGRESSION, PARTIAL LEAST SQUARES, NEAR-INFRARED REFLECTION SPECTROSCOPY, COMPOUND FEEDINGSTUFFS, CARNAC"
}