A fair and efficient two-step procedure for sugarcane properties prediction based on near-infrared spectra

The growing demand for biofuels and sugar has prompted the selection of sugarcane cultivars with higher fiber content and apparent sucrose levels. In the initial phase of a breeding program, researchers might focus on classifying individuals as selected or not based on whether they display a property value above or below a specific threshold. Integrating classification methods with near-infrared (NIR) spectroscopy is essential for effective selection. This work outlines a two-step procedure designed to facilitate a fair comparison of the classifications under evaluation. We applied our approach to assess three classification techniques – Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machines (SVM), and Random Forests (RF) – about their performance in predicting the classes of two sugarcane properties derived from NIR data. Our study utilized a dataset of 460 samples, categorized into high and low levels of fiber percentage (FIB) and apparent sucrose content (ASC), both of which are closely related to sucrose content. In the first step, the data was split into training and testing sets via the Kernard-Stone algorithm to determine the appropriate pretreatments for the NIR spectra. The selected pretreatment for each method was then applied in the second step, where the 460 samples were randomly divided again into training and test sets over ten repetitions. A key finding of this study is that the optimal set of pretreatments for a specific dataset varies depending on the classification method used. This two-step procedure streamlines the process for researchers to compare different classification techniques.