Improvement of Near-Infrared Spectral Calibration Models for Brix Prediction in ‘Gannan’ Navel Oranges by a Portable Near-Infrared Device

Abstract

A portable near-infrared (NIR) device was developed to nondestructively predict Brix value in intact ‘Gannan’ navel oranges. This research focused on developing calibration models which were less disturbed by the challenges of portable applications. The spectra of 150 samples were collected in the wavelength range of 820–950 nm. Wavelet transformed (WT) was applied to compress the raw data for improving the optimization efficiency. Classical linear partial least squares regression and nonlinear least squares support vector regression (LSSVR) were applied to building calibration models. By comparison, both prediction precision and optimization efficiency of the compressed regression models were improved. The LSSVR models outperformed the PLS models with higher accuracy and lower error. LSSVR combined with WT compression (WT–LSSVR) produced the best correlation coefficient value (r) and the root mean squared error of prediction of 0.918 and 0.321 oBrix. Based on these results, WT–LSSVR is to be a promising method to improve precision and optimization efficiency of NIR spectral calibration models for Brix prediction in ‘Gannan’ navel oranges by the portable near-infrared device.