Gao Feng, Xing Yage, Li Jialong, Guo Lin, Sun Yiye, Shi Wen, Yuan Leiming
College of Horticulture and Forestry, Tarim University, Alar, Xinjiang 843300, China.
Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China.
Molecules. 2025 Mar 30;30(7):1543. doi: 10.3390/molecules30071543.
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.
总可溶性固形物（TSS）是杏果实成熟度的关键指标和品质决定因素，影响着采收时机和采后管理决策。本研究开发了一种先进框架，将自适应增强（Adaboost）集成学习与通过无信息变量消除（UVE）选择的高频光谱变量相结合，用于果实品质的快速无损检测。采集近红外（NIR）光谱（1000~2500 nm），然后通过稳健主成分分析（ROBPCA）进行预处理以检测异常值，并结合z分数归一化进行光谱预处理。后续数据处理包括三个步骤：（1）UVE连续运行100次以识别特征波长，这些波长被分为三个级别——高频（≥90次）、中频（30 - 90次）和低频（≤30次）子集；（2）为每个波长子集建立基本最优偏最小二乘回归（PLSR）模型；（3）通过Adaboost集成算法执行自适应权重优化。实验结果表明：（1）基于高频波长建立的模型优于全光谱模型和全特征波长模型。（2）优化后的UVE - PLS - Adaboost模型达到了最佳性能（R = 0.889，RMSEP = 1.267，MAE = 0.994）。本研究表明，UVE - Adaboost融合方法通过多维度特征优化和模型权重分配提高了模型预测精度和泛化能力。所提出的框架能够快速、无损地检测杏的TSS，并为农业应用中其他水果的品质评估提供参考。
