阿达布思
主成分分析
人工智能
模式识别(心理学)
Boosting(机器学习)
变量消去
特征选择
计算机科学
数学
生物系统
支持向量机
生物
推论
作者
Meng Zhao,An Sun,Siying He,Luoshui Yu,Rongxiang Wang,Wenjie Fu,Yang Mu
摘要
Summary This study focuses primarily on the real‐time monitoring of reducing sugar levels throughout solid‐state ethanol fermentation. A predictive model for accurate quantification of reducing sugar levels will be formulated through a comprehensive analysis of near‐infrared spectra across different fermentation stages. Building a predictive model using Adaptive Boosting (AdaBoost), The spectral data was pre‐processed using the Standard Normal Variate (SNV) method, followed by feature band selection using the Genetic Algorithm (GA), resulting in the identification of 181 characteristic wavelengths. The resulting GA‐AdaBoost model has excellent performance metrics: of 0.989, RMSEC of 0.393, of 0.978, RMSEP of 0.640, RPD of 6.651 and RER of 0.142. Comparative evaluations against Competitive Adaptive Reweighted Sampling (CARS), Successive Projection Algorithm (SPA), Uninformative Variable Elimination (UVE) and Principal Component Analysis (PCA) consistently underscore the superior efficacy of the GA‐AdaBoost model. These results robustly confirm its reliability in accurately quantifying reducing sugars in solid‐state fermentation processes.
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