Prediction of Total Soluble Solids in Apricot Using Adaptive Boosting Ensemble Model Combined with NIR and
Feng Gao1,2, Yage Xing1,3,4, Jialong Li1,3,4
1College of Horticulture and Forestry, Tarim University, Alar, Xinjiang 843300, China.
Molecules (Basel, Switzerland)
|April 26, 2025
Summary
This study introduces a new method using adaptive boosting (Adaboost) and uninformative variable elimination (UVE) for non-destructive apricot quality testing. The approach accurately predicts total soluble solids (TSSs), enhancing fruit quality assessment.
Area of Science:
- Agricultural Science
- Spectroscopy
- Machine Learning
Background:
- Total soluble solids (TSSs) are key indicators of apricot maturity and quality.
- Accurate, non-destructive methods are needed for timely harvest and postharvest management.
Purpose of the Study:
- To develop an advanced framework for rapid, non-destructive detection of apricot TSSs.
- To integrate adaptive boosting (Adaboost) with spectral variable selection for improved accuracy.
Main Methods:
- Acquired near-infrared (NIR) spectra (1000-2500 nm).
- Preprocessed spectra using robust principal component analysis (ROBPCA) and z-score normalization.
- Applied uninformative variable elimination (UVE) for wavelength selection and Adaboost for model optimization.
Main Results:
- The model using high-frequency wavelengths showed superior performance.
- The optimized UVE-PLS-Adaboost model achieved high accuracy (R=0.889, RMSEP=1.267, MAE=0.994).
Conclusions:
- The UVE-Adaboost fusion method significantly improves prediction accuracy and generalization.
- This framework offers a reliable approach for non-destructive apricot quality evaluation and can be applied to other fruits.
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