近红外光谱转移和混合建模实现黄精多糖定量分析
Quantitative Analysis of Polygonatum sibiricum Polysaccharide Using Near-Infrared Spectrum Transfer and Hybrid Model
鲁嘉滢 1戴宇佳 2王悦悦 2曾松伟2
作者信息
- 1. 浙江农林大学数学与计算机科学学院,浙江 杭州 311300
- 2. 浙江农林大学光机电工程学院,浙江 杭州 311300
- 折叠
摘要
为解决非均质样本预测模型的准确性和鲁棒性较低的问题,以黄精多糖为研究对象,提出了一种基于均质样本模型转移算法,并结合混合建模策略,建立不同物理状态混合预测模型.采用Stacking集成学习建立基本预测模型,引入径向基函数(RBF)神经网络转移近红外光谱,拟合不同物理状态样本光谱的非线性映射关系,通过调整吸光度矩阵窗口大小,使网络拟合效果趋于最优,确定近红外光谱转移函数.结果表明,经过RBF修正后的混合预测模型在测试集的拟合系数(R2)为0.991,均方根误差(RMSE)为0.497%,平均绝对误差(MAE)为0.383%.所提出的非线性转移算法能应对样本复杂性,降低样本表面形态、水分对建模的影响,提高了黄精多糖含量混合预测模型的精度和泛化能力.
Abstract
To address issues of low accuracy and low robustness in predicting heterogeneous samples,this study focuses on Polygonatum sibiricum polysaccharide and proposes a model transfer algorithm based on homogeneous samples.By incorporating a hybrid modeling strategy,different physical-state mixed prediction models were established.Stacking ensemble learning was employed to establish base prediction models,and a radial basis function(RBF)neural network was introduced as the transfer function in transfer near-infrared spectroscopy.It was used to fit the nonlinear mapping relationship of spectra from samples with different physical states.By adjusting the size of absorbance matrix window,the network fitting effect was optimized and the near-infrared spectroscopy transfer function was determined.Results indicate that the mixed prediction model corrected using the RBF achieves fitting coefficient(R2)of 0.991,root mean square error(RMSE)of 0.497%,and mean absolute error(MAE)of 0.383%for testing set.Proposed nonlinear transfer algorithm effectively manages sample complexity,reduces the effects of sample surface morphology and moisture on modeling,and enhances the accuracy and generalizability of mixed prediction model for Polygonatum sibiricum polysaccharide content.
关键词
光谱学/黄精多糖/模型转移/径向基神经网络Key words
spectroscopy/Polygonatum sibiricum polysaccharide/model transfer/radial basis neural network引用本文复制引用
出版年
2024
NETL
NSTL
万方数据