Prediction of Total Soluble Solids in Apricot Using Adaptive Boosting Ensemble Model Combined with NIR and High-Frequency UVE-Selected Variables
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| Publicado en: | Molecules vol. 30, no. 7 (2025), p. 1543 |
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| Otros Autores: | , , , , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 1420-3049 | ||
| 024 | 7 | |a 10.3390/molecules30071543 |2 doi | |
| 035 | |a 3188791687 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231541 |2 nlm | ||
| 100 | 1 | |a Gao, Feng |u College of Horticulture and Forestry, Tarim University, Alar, Xinjiang 843300, China; <email>15950516317@163.com</email> (F.G.); ; Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China | |
| 245 | 1 | |a Prediction of Total Soluble Solids in Apricot Using Adaptive Boosting Ensemble Model Combined with NIR and High-Frequency UVE-Selected Variables | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Total soluble solids (TSSs) serve as a crucial maturity indicator and quality determinant in apricots, influencing harvest timing and postharvest management decisions. This study develops an advanced framework integrating adaptive boosting (Adaboost) ensemble learning with high-frequency spectral variables selected by uninformative variable elimination (UVE) for the rapid non-destructive detection of fruit quality. Near-infrared (NIR) spectra (1000~2500 nm) were acquired and then preprocessed through robust principal component analysis (ROBPCA) for outlier detection combined with z-score normalization for spectral pretreatment. Subsequent data processes included three steps: (1) 100 continuous runs of UVE identified characteristic wavelengths, which were classified into three levels—high-frequency (≥90 times), medium-frequency (30–90 times), and low-frequency (≤30 times) subsets; (2) the development of the base optimal partial least squares regression (PLSR) models for each wavelength subset; and (3) the execution of adaptive weight optimization through the Adaboost ensemble algorithm. The experimental findings revealed the following: (1) The model established based on high-frequency wavelengths outperformed both full-spectrum model and full-characteristic wavelength model. (2) The optimized UVE-PLS-Adaboost model achieved the peak performance (R = 0.889, RMSEP = 1.267, MAE = 0.994). This research shows that the UVE-Adaboost fusion method enhances model prediction accuracy and generalization ability through multi-dimensional feature optimization and model weight allocation. The proposed framework enables the rapid, non-destructive detection of apricot TSSs and provides a reference for the quality evaluation of other fruits in agricultural applications. | |
| 651 | 4 | |a China | |
| 653 | |a Variables | ||
| 653 | |a Machine learning | ||
| 653 | |a Feature selection | ||
| 653 | |a Accuracy | ||
| 653 | |a Principal components analysis | ||
| 653 | |a Datasets | ||
| 653 | |a Spectrum analysis | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Food quality | ||
| 653 | |a Citrus fruits | ||
| 653 | |a Calibration | ||
| 700 | 1 | |a Xing, Yage |u College of Horticulture and Forestry, Tarim University, Alar, Xinjiang 843300, China; <email>15950516317@163.com</email> (F.G.); ; Xinjiang Production & Construction Corps, Key Laboratory of Facility Agriculture, Alar, Xinjiang 843300, China; Instrumental Analysis Center, Tarim University, Alar, Xinjiang 843300, China | |
| 700 | 1 | |a Li, Jialong |u College of Horticulture and Forestry, Tarim University, Alar, Xinjiang 843300, China; <email>15950516317@163.com</email> (F.G.); ; Xinjiang Production & Construction Corps, Key Laboratory of Facility Agriculture, Alar, Xinjiang 843300, China; Instrumental Analysis Center, Tarim University, Alar, Xinjiang 843300, China | |
| 700 | 1 | |a Guo, Lin |u College of Horticulture and Forestry, Tarim University, Alar, Xinjiang 843300, China; <email>15950516317@163.com</email> (F.G.); ; Xinjiang Production & Construction Corps, Key Laboratory of Facility Agriculture, Alar, Xinjiang 843300, China; Instrumental Analysis Center, Tarim University, Alar, Xinjiang 843300, China | |
| 700 | 1 | |a Sun, Yiye |u College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, China | |
| 700 | 1 | |a Shi, Wen |u College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, China | |
| 700 | 1 | |a Yuan, Leiming |u College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, China | |
| 773 | 0 | |t Molecules |g vol. 30, no. 7 (2025), p. 1543 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3188791687/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3188791687/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
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