Predicting internal quality parameters of individual pieces of intact oranges using hyperspectral images

Abstract

Conventional methods for the determination of chemical parameters of the fruit like soluble solids and acid content are often complicated and destructive, cannot be run on a large scale and are still far away from being implemented to large volumes of products or even better to individual piece fruits. In this study, the potential of hyperspectral imaging was evaluated for quantifying solid soluble content (SSC) and titratable acidity (TA) in intact oranges. Hyperspectral images (900–1700 nm) of 264 oranges collected during 2017 and 2018 at different maturation stages in Southern Spain farms were recorded. Partial least-squares analysis (PLS), Artificial Neural Network (ANN), optimized Support Vector Machine (SVM) and Gaussian Process Regression (GPR), as well as different spectral pre-processing methods, were tested for their effectiveness in quantifying titratable acidity (TA) and solid soluble content (SSC) in intact oranges. Random samples were chosen to validate the models by cross-validation. The best-selected models were then applied to a validation set of “unknown” samples and standard errors of prediction as well as correlation coefficients between actual and predicted values were calculated. Finally, a prediction map was developed to display the concentration distribution of the TA and SSC in the orange fruit, demonstrating that hyperspectral imaging (HSI) technique was feasible to quantify parameters in citrus fruit and can be further used for monitoring the quality of oranges at pre- and post-harvest in real-time.