College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China.
College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning Province, China.
Anal Chim Acta. 2021 Dec 15;1188:339205. doi: 10.1016/j.aca.2021.339205. Epub 2021 Oct 21.
When fourier transform infrared spectroscopy (FTIR) techniques combined with multivariate calibration are used to measure the key process features or analyte concentrations during batch process, model adaption is indispensable for maintaining the predictability of a primary calibration model in new secondary batches. Many model adaption methods conforming to the actual application scenario of batch process have been proposed. Here we report on a novel standard-free model adaption method without reference measurement called variable selection strategy with self-organizing maps (VSSOM). It uses self-organizing maps (SOM) to classify the whole spectral variables into multiple classes according to the spectra from primary batch and secondary batch, respectively; and the corresponding primary feature subsets and secondary feature subsets are formed firstly. Secondly, candidate feature subsets without empty elements are generated by operating intersection between any primary feature subsets and any secondary feature subsets. Thirdly, the candidate feature subset with minimum root mean square error of cross-validation (RMSECV) for the primary calibration set is selected as the optimal feature subset. In this manner, the optimal feature subset can be identified from the candidate feature subsets. In other words, VSSOM aims to create a stable and consistent feature subset across different batches provided that it selects better features within the intersection sets between primary feature subsets and any secondary feature subsets. Two batch process datasets (γ-polyglutamic acid fermentation and paeoniflorin extraction) are presented for comparing the VSSOM method with No transfer partial least squares (PLS), boxcar signal transfer (BST), successive projection algorithm (SPA), transfer component analysis (TCA) and domain-invariant iterative partial least squares (DIPALS). Experimental results show that VSSOM has superior performance and comparable prediction performance in all the scenarios.
当傅里叶变换红外光谱（FTIR）技术与多元校正相结合，用于测量分批过程中的关键过程特征或分析物浓度时，模型适应对于保持原始校准模型在新的二级批次中的可预测性是必不可少的。已经提出了许多符合分批过程实际应用场景的模型适应方法。在这里，我们报告了一种新的无参考测量的标准自由模型适应方法，称为自组织映射变量选择策略（VSSOM）。它使用自组织映射（SOM）根据原始批次和二级批次的光谱分别将整个光谱变量分类为多个类别，并形成相应的原始特征子集和二级特征子集。其次，通过在任何原始特征子集和任何二级特征子集之间进行交集运算，生成没有空元素的候选特征子集。然后，选择原始校准集的交叉验证均方根误差（RMSECV）最小的候选特征子集作为最优特征子集。通过这种方式，可以从候选特征子集中识别出最优特征子集。换句话说，VSSOM 的目的是在不同批次之间创建一个稳定且一致的特征子集，前提是它在原始特征子集和任何二级特征子集的交集内选择更好的特征。我们提出了两个分批过程数据集（γ-聚谷氨酸发酵和芍药苷提取），用于将 VSSOM 方法与无转移偏最小二乘法（PLS）、方波信号传输（BST）、连续投影算法（SPA）、转移成分分析（TCA）和域不变迭代偏最小二乘法（DIPALS）进行比较。实验结果表明，VSSOM 在所有场景中都具有优越的性能和可比的预测性能。
