Near-Infrared Spectroscopy for the Prediction of Disease Ratings for Fiji Leaf Gall in Sugarcane Clones

Abstract

This paper demonstrates how inferential measurements or indirect methods using near-infrared (NIR) methodology and chemometrics can be used to predict sugarcane clonal performance. Fiji leaf gall resistance is used in this study as an example. Fiji leaf gall is one of Australia's most serious sugarcane diseases, representing a significant problem in almost half of the total area under production. Traditional rating of sugarcane clones for resistance/susceptibility is difficult and expensive because of the nature of field-based methods and variable infection levels of the trials. Thus, the aim of this work was to investigate the potential of NIR spectroscopy as an alternative means to rate clones from direct measurement of sugarcane leaf spectra and to examine its ability to successfully predict traditional resistance ratings using a calibration model based on a chemometrics method such as partial least squares (PLS). A scanning electron microscopy (SEM) study of the leaf substrate was undertaken to elucidate the nature of the NIR sample site. In addition, an NIR study of freeze-dried sugarcane leaf samples resolved the heavily overlapping O–H bands present in the NIR spectrum due to water/cellulose interaction. A significant decrease in the spectral intensity between 5205 and 5393 cm−1 was observed and a similar decrease was noted in the OH stretching overtone (7114 cm−1) with an accompanying shift to lower wavenumbers. PLS modeling based on traditional ratings as the dependent variable and the corresponding NIR spectra showed satisfactory results with standard error of validation (SEV) and standard error of prediction (SEP) values being 0.98 ( R2 = 0.97) and 1.20 ( R2 = 0.88), respectively. This methodology has now been recommended for more extensive field trials.