Is this pear sweeter than this apple? A universal SSC model for fruits with similar physicochemical properties

Near-infrared spectroscopy (NIRS) is one of the most promising technique for nonde-structive and rapid detection of fruit's soluble solid content (SSC). However, when using NIRS to assess the SSC of fruit, a strategy for individually modeling different fruit cultivars is usually required, while the maintenance and upgrading of the models are time-consuming and laborious. To cope with these problems, this study aimed to explore the feasibility of developing a universal model to predict SSC for thin-skinned fruits with similar physicochemical properties. A progressive hybrid variable selection strategy was used to establish the universal model to decrease the complexity of the modeling and ultimately increase the model accuracy. First, the characteristic wavebands of four culti-vars were chosen by synergy interval partial least squares (Si-PLS). Next, the wavelength point selection method was employed to filter the uninformative wavelengths and then mapped to an L1 regularization optimization task with constraints. Finally, the effective variables were further identified by simulated annealing (SA) and genetic algorithm (GA), separating them from the remaining variables. The coefficients of determination of cali-bration (RC2) and prediction (RP2) were both 0.93, and the root mean square error of cali-bration (RMSECV) and prediction (RMSEP) were 0.62 degrees Brix and 0.60 degrees Brix, respectively, for the Si-L1-UVE-GA model. A new external sample set was used for external prediction, and the RMSEP and Rp2 for the multi-cultivars model were 0.73 degrees Brix and 0.90, respectively. The results showed superiority of developing a universal model for predicting SSC of different species by using wavelength-limited portable NIR equipment.(c) 2023 IAgrE. Published by Elsevier Ltd. All rights reserved.

Automated summary

This study aimed to develop a universal model to predict the soluble solid content (SSC) of thin-skinned fruits with similar physicochemical properties. A progressive hybrid variable selection strategy was used to establish the model, resulting in accurate predictions of SSC for different species using wavelength-limited portable NIR equipment.