Improvement of classification using robust soft classification rules for near-infrared reflectance spectral data
作者： M. Daszykowski , J. Orzel , M.S. Wrobel , H. Czarnik-Matusewicz , B. Walczak
DOI: 10.1016/J.CHEMOLAB.2011.08.004
关键词:
摘要: Abstract The aim of this work was to propose a quick and cost-effective procedure, which could help identify the types fat (rapeseed, mixture rapeseed soybean, lard oils) added feed used for raising pigs. For purpose, liver samples were examined their near-infrared reflectance spectra served as data construction classic robust soft independent modeling class analogy (SIMCA) models. results showed that contained information sufficient build good classification models enabled three additions be distinguished. best obtained from SIMCA, indicating its superior performance in terms high sensitivity specificity comparison with SIMCA. Specifically, had sensitivities 100% specificities 96.05%, 97.73% 100%, rapeseed, enriched feed, respectively.
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