Stacking Ensemble Learning Method for Quantitative Analysis of Soluble Solid Content in Apples

Abstract

The soluble solids content (SSC) in apples directly affects their quality. This study aimed to detect SSC nondestructively using hyperspectral technology combined with chemometrics. However, data generation may not follow a specific pattern, and even small perturbations in the data can have a significant impact on the constructed model. To improve the anti-interference capability of individual models, this study proposed a stacking ensemble learning method that adopted partial least squares (PLS), support vector machine (SVM), extreme gradient boosting (Xgboost), random forest (RF) as basic-learners, and RF serving as a meta-learner. Experimental results showed that the performance of the established model on the test set were as follows: the root mean square error (RMSE) was 0.4325, mean absolute error (MAE) was 0.3245, mean absolute percentage error (MAPE) was 0.0271, coefficient of determination ($R^2$) was 0.9250. These results indicate that the stacking ensemble learning approach could appropriately fuse the predictive results of each basic-learner and improve the prediction accuracy of individual models. To verify the superiority of the proposed stacking ensemble learning method, the selection of its basic-learners, meta-learner, and combination strategy were compared and analyzed. This study not only provides a theoretical reference for the further development of related nondestructive detection equipment but also offers guidance for fusion algorithms as well.