Improvement of classification using robust soft classification rules for near-infrared reflectance spectral data

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