| 基于迭代缩减窗口自助软收缩算法的近红外光谱变量选择方法研究 |
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| 基金项目:国家自然科学基金（62103216）；山东省自然科学基金（ZR2020QF060） |
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| 中文摘要:该文针对近红外光谱因冗余变量导致的标定模型预测性能差的问题，提出了一种迭代缩减窗口自助软收缩（ISWBOSS）算法。该方法使用窗口对变量进行划分，随机抽取窗口并利用其中的变量建立子模型，计算窗口内变量回归系数的归一化并作为权重继续进行加权采样，从而逐步实现变量空间的软收缩。同时在迭代过程中不断缩减窗口大小对特征变量进行精确搜索。通过在玉米数据集上进行验证，并与全谱法、遗传算法、竞争自适应重加权采样法和自助软收缩法建立的偏最小二乘模型对比，结果表明，新方法不论在准确性还是稳定性上都具有显著优势。以玉米蛋白质含量预测为例，与自助软收缩算法相比，ISWBOSS的预测均方根误差从0.041 8降至0.010 3，且达到最优模型所需的迭代次数更少，运算效率更高。该方法对提高近红外光谱标定模型的性能具有一定的指导意义。 |
| 中文关键词:变量选择 迭代收缩窗口 近红外光谱 偏最小二乘 模型标定 |
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| A Variable Selection Method for Near-infrared Spectroscopy Based on Iterative Shrinkage Window Bootstrapping Soft Shrinkage Algorithm |
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| Abstract:In this paper，an iterative shrinkage window-bootstrapping soft shrinkage（ISWBOSS） algorithm was proposed to address problems of poor prediction performance of the calibration models based on near-infrared（NIR） spectroscopy due to their redundant variables in NIR spectra．The variables was divided by windows in the method，and the windows were randomly selected，in which the sub-models were built with the variables．The soft shrinkage of the variable space was gradually achieved by calculating the normalization of the regression coefficients of the variables in the window，and continuing the weighted sampling as weights．Meanwhile，the window size was continuously shrunk during the iterative process to perform an accurate search of the feature variables．It was validated on a corn dataset，and compared with the partial least squares models established by the full-spectrum method，genetic algorithm，competitive adaptive reweighted sampling，and bootstrapping soft shrinkage approach．The results showed that the new method had significant advantages in terms of both accuracy and stability．Taking corn protein content prediction as an example，the root mean square error of prediction of ISWBOSS was reduced from 0.041 8 to 0.010 3，compared with the bootstrapping soft shrinkage approach．Moreover，the new method required fewer iterations and higher operational efficiency to reach the optimal model，which was a guideline for improving the performance of NIR spectral calibration models． |
| Key Words:variable selection iterative shrinkage window near-infrared spectroscopy partial least squares model calibration |
| 引用本文：徐啟蕾，郭鲁钰，杜康，单宝明，张方坤.基于迭代缩减窗口自助软收缩算法的近红外光谱变量选择方法研究[J].分析测试学报,2022,41(8):1229-1234. |
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