Semi-supervised robust models for predicting dry matter in mango fruit with near-infrared spectroscopy

Abstract

A novel approach to developing robust calibration models for predicting dry matter in mango fruit is presented. The robust methodology includes automatic iterative downweighting of outlying samples during the chemometric modelling to learn a robust mathematical relationship between near-infrared (NIR) spectra and dry matter (DM). The robust models were compared with traditional partial least-squares (PLS) modelling by validation on independent data derived from four different cultivars of another origin and measured by a different instrument (without calibration transfer) from an open access mango dataset. The results showed that downweighting of several outliers in the robust modelling approach reduced the root mean squared error of prediction (RMSEP) from 1.03 % DM to 0.75 % DM compared to that achieved with a PLS model tested on samples of different cultivars. Furthermore, independent tests of the robust model on sample sets composed of data from different cultivars, origin, and measured with a different NIR instrument reduced the RMSEP from 2.06 % DM to 0.89 % DM without any need for model update and transfer. The robust models can help improve the prediction of fruit traits and are a further step in broadening the application of NIR spectroscopy in horticultural practice.