This paper presents a novel spectrum analysis tool named synergy adaptive moving window modeling based on immune clone algorithm (SA-MWM-ICA) considering the tedious and inconvenient labor involved in the selection of pre-processing methods and spectral variables by prior experience. In this work, immune clone algorithm is first introduced into the spectrum analysis field as a new optimization strategy, covering the shortage of the relative traditional methods. Based on the working principle of the human immune system, the performance of the quantitative model is regarded as antigen, and a special vector corresponding to the above mentioned antigen is regarded as antibody. The antibody contains a pre-processing method optimization region which is created by 11 decimal digits, and a spectrum variable optimization region which is formed by some moving windows with changeable width and position. A set of original antibodies are created by modeling with this algorithm. After calculating the affinity of these antibodies, those with high affinity will be selected to clone. The regulation for cloning is that the higher the affinity, the more copies will be. In the next step, another import operation named hyper-mutation is applied to the antibodies after cloning. Moreover, the regulation for hyper-mutation is that the lower the affinity, the more possibility will be. Several antibodies with high affinity will be created on the basis of these steps. Groups of simulated dataset, gasoline near-infrared spectra dataset, and soil near-infrared spectra dataset are employed to verify and illustrate the performance of SA-MWM-ICA. Analysis results show that the performance of the quantitative models adopted by SA-MWM-ICA are better especially for structures with relatively complex spectra than traditional models such as partial least squares (PLS), moving window PLS (MWPLS), genetic algorithm PLS (GAPLS), and pretreatment method classification and adjustable parameter changeable size moving window PLS (CA-CSMWPLS). The selected pre-processing methods and spectrum variables are easily explained. The proposed method will converge in few generations and can be used not only for near-infrared spectroscopy analysis but also for other similar spectral analysis, such as infrared spectroscopy.

中文翻译：

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新型近红外光谱分析工具：基于免疫克隆算法的协同自适应移动窗口模型

考虑到根据先验经验选择预处理方法和频谱变量所涉及的繁琐和不方便的劳动，本文提出了一种新的频谱分析工具，称为基于免疫克隆算法的协同自适应移动窗口建模（SA-MWM-ICA）。在这项工作中，免疫克隆算法作为一种新的优化策略首次引入频谱分析领域，弥补了相对传统方法的不足。基于人体免疫系统的工作原理，将定量模型的表现视为抗原，将与上述抗原对应的特殊载体视为抗体。该抗体包含一个由 11 个十进制数字创建的预处理方法优化区域，由一些宽度和位置可变的移动窗口组成的频谱变量优化区域。通过使用该算法建模，创建了一组原始抗体。计算这些抗体的亲和力后，将选择亲和力高的抗体进行克隆。克隆的规则是亲和力越高，拷贝数就越多。在下一步中，另一个名为超突变的导入操作将应用于克隆后的抗体。此外，超突变的规则是亲和力越低，可能性越大。在这些步骤的基础上，将创建几种具有高亲和力的抗体。采用模拟数据集、汽油近红外光谱数据集和土壤近红外光谱数据集对SA-MWM-ICA的性能进行验证和说明。分析结果表明，SA-MWM-ICA采用的定量模型的性能优于偏最小二乘法（PLS）、移动窗口PLS（MWPLS）、遗传算法PLS（GAPLS）等传统模型，尤其是对于光谱相对复杂的结构。 ），以及预处理方法分类和可调参数可变大小移动窗口PLS（CA-CSMWPLS）。所选的预处理方法和频谱变量很容易解释。所提出的方法将在几代内收敛，不仅可以用于近红外光谱分析，还可以用于其他类似的光谱分析，例如红外光谱。