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| Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period |
| HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3* |
1. College of Mathematics and Physics，Xinjiang Agricultural University，Urumqi 830052，China
2. Comprehensive Testing Ground，Xinjiang Academy of Agricultural Sciences, Urumqi 830013，China
3. Mechanical and Traffic College，Xinjiang Agricultural University，Urumqi 830052，China
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Abstract Soluble solids content (SSC) is an important physiological indicator of apple quality and maturation, and can be used for predicting the quality and maturity of apples. In this paper, 552 samples were collected at equal intervals of 3 d from the fruit swelling and setting stage to the complete mature stage, and the SSC was determined by collecting visible/near-infrared spectra from 380 to 1100 nm, and fused with fractional differential (FD) technique and replacement importance-random forest (Permutation Importance-Random Forest, PIMP-RF) algorithm to construct an ensemble learning model for SSC prediction in apple during maturing period. The results showed that the fractional differential orders of the PLS model were 0, 0.4, 1.1, and 1.6, and the results of feature importance and interpretability analysis by the PIMP-RF algorithm showed that the key wavelengths for predicting the SSC of maturity apples using visible/near-infrared spectroscopy were mainly in the visible band, which provided a theoretical basis for the future development of a rapid nondestructive detection device for Xinjiang Red Fuji apples. The ensemble learning model of apple ripening SSC constructed based on fractional differential technique and PIMP-RF algorithm has good prediction ability, with the correlation coefficient r equal to 0.989 2, mean absolute error MAE equal to 0.241 2, root mean square error RMSE equal to 0.309 1 and mean absolute percentage error equal to 0.018 3 in the training set. The correlation coefficient r of the test set is equal to 0.903 8, the mean absolute error MAE is equal to 0.549 9, the root mean square error RMSE is equal to 0.740 8, and the mean absolute percentage error is equal to 0.043 4, compared to the FD0-PIMP-RF, FD0.4-PIMP-RF, FD1.1-PIMP-RF, and FD1.6-PIMP-RF models, the ensemble learning model is optimal. Therefore, the integrated fractional order differentiation technique and PIMP-RF algorithm, combined with visible/near-infrared spectroscopy, can successfully and effectively predict the soluble solids content of apples during maturing period.
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Received: 2022-03-09
Accepted: 2022-11-28
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Corresponding Authors:
GUO Jun-xian
E-mail: junxianguo@163.com
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