Adaptive wavelet modelling of a nested 3 factor experimental design in NIR chemometrics

Abstract

The objective of this study was to investigate the effects of some commonly used sample preparation procedures, including overnight freezing, and the type of homogeniser on the near-infrared (NIR) spectra of red grape homogenates. Homogenates (n = 284) of three red grape varieties were prepared using one of three types of homogenisers after one of two types of short term storage (fresh and overnight freezing) and then scanned in a FOSS NIRSystems6500 instrument (400–2500 nm). The NIR spectral data were then analysed using various discrimination techniques, namely Penalized Discriminant Analysis (PDA), Multivariate Adaptive Regression Splines discriminant analysis (MARS-DA) and Random Forests (RF) yielding correct classification rates (CCR) of 63.4%, 58.6% and 45.6%, respectively. To improve the CCR of the discrimination models, feature extraction from the NIR spectral data was performed using an adaptive discrete wavelet transformation (DWT). The DWT algorithm employs an adaptive wavelet basis function that maximizes the discrimination between the different combinations of homogenisers, storage and grape varieties. The results after adaptive DWT on the NIR spectra resulted in CCRs of 99.93%, 99.2% and 76.4% for PDA, MARS-DA and RF, respectively. Further analysis of adaptive DWT PDA via MANOVA revealed significant differences in the main and interaction effects of the three treatments, which were then associated with specific regions within the NIR spectrum.