The robust normal variate transform for pattern recognition with near-infrared data Original Research Article

The standard normal variate transform (SNV) is applied to pretreat NIR data for pattern recognition. Eleven NIR data sets are analysed. The results show that SNV improves classification results in most of the cases by reducing the within-class variance. Because of the closure problem, SNV leads to artefacts and is difficult to interpret in simple methods of wavelength distance and univariate direct discrimination (DD). A proposed robust normal variate transform (RNV) gives more reasonable results than SNV. Because of the artefacts, SNV sometimes gives worse results for regularised discriminant analysis (RDA) than using the original data. In this case, RNV leads to improved results, and in general, it performs better than SNV, even when SNV gives better results than using the original data. However, the drawback of RNV is that the applied percentile needs to be optimised. A proposal for quick selection of the percentile is given.