A feature-based soft sensor for spectroscopic data analysis
A feature-based soft sensor for spectroscopic data analysis
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DOI：
10.1016/j.jprocont.2019.03.016
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发表时间：
2019-06-01
影响因子：
4.2
通讯作者：
He, Q. Peter
中科院分区：
文献类型：
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作者：
Shah, Devarshi;Wang, Jin;He, Q. Peter
在过去的几十年里，近红外(NIR)和紫外可见光谱等光谱技术得到了广泛的应用。因此，已经开发了各种软测量来根据其光谱读数来预测样品的性质。由于不同波长的读数高度相关，已有研究表明，变量选择可以显著提高软测量的预测性能，降低模型的复杂性。目前，几乎所有的变量选择方法都集中在如何选择与因变量强相关的变量(即波长或波长段)，以提高预测性能。虽然已经有许多成功的应用报道，但这种变量选择方法确实有其局限性，例如对训练数据的选择高度敏感，以及在对新样本进行测试时性能恶化。一个可能的原因是在校准过程中移除了有用的波长或波长段，这些波长段可能会被“倾斜”，以适应或捕获校准数据中包含的噪声或未知干扰。因此，当模型被外推或应用于新样本时，模型的预测性能可能会显著恶化。针对这一局限性，提出了一种基于统计模式分析(SPA)的基于特征的软测量方法。基于SPA的方法不是选择特定的波长或波长段，而是考虑将整个光谱分成几个段，并在每个段上提取不同的特征来构建软测量。换句话说，SPA模型包含了全谱的完整信息，没有任何选择或删除，我们认为这是基于SPA的方法具有高度稳健性的主要原因。此外，我们还提出了一个蒙特卡罗验证和测试(MCVT)过程和三个基于MCVT的性能指标，以便在不同的数据集上对不同的软测量方法进行一致和公平的比较。MCVT程序和指标一般适用于其他应用中的模型比较。通过四个案例验证了基于特征的软测量方法的性能，并将其与完全偏最小二乘(PLS)、最小绝对收缩和选择算子(Lasso)以及基于协同区间偏最小二乘(SiPLS)的模型进行了比较。此外，我们还考察了基于核偏最小二乘(KPLS)的软测量方法的潜力，考察了它们的性能，并讨论了它们的优缺点。(C)2019爱思唯尔有限公司。保留所有权利。
In the last few decades, spectroscopic techniques such as near-infrared (NIR) and UV/vis spectroscopies have gained wide applications. As a result, various soft sensors have been developed to predict sample properties from its spectroscopic readings. Because the readings at different wavelengths are highly correlated, it has been shown that variable selection could significantly improve a soft sensor's prediction performance and reduce the model complexity. Currently, almost all variable selection methods focus on how to select the variables (i.e., wavelengths or wavelength segments) that are strongly correlated with the dependent variable to improve the prediction performance. Although many successful applications have been reported, such variable selection methods do have their limitations, such as high sensitivity to the choice of training data, and deteriorated performance when testing on new samples. One possible reason is the removal of useful wavelengths or segments of wavelengths during the calibration process, which could be "tilted" to overfit or capture the noise or unknown disturbances contained in the calibration data. As a result, the model prediction performance may deteriorate significantly when the model is extrapolated or applied to new samples. To address this limitation, we propose a feature-based soft sensor approach utilizing statistics pattern analysis (SPA). Instead of selecting certain wavelengths or wavelength segments, the SPA-based method considers the whole spectrum which is divided into segments, and extracts different features over each spectrum segment to build the soft sensor. In other words, the SPA model contains the complete information from the full spectrum without any selection or removal, which we believe is the main reason for the high robustness of the SPA-based method. In addition, we propose a Monte Carlo validation and testing (MCVT) procedure and three MCVT-based performance indices for consistent and fair comparison of different soft sensor methods across different datasets. The MCVT procedure and indices are generally applicable for model comparison in other applications. Four case studies are presented to demonstrate the performance of the feature-based soft sensor and to compare it with a full partial least squares (PLS), a least absolute shrinkage and selection operator (Lasso), and a synergy interval PLS (SiPLS) based models following the proposed MCVT procedure. In addition, we examine the potential of kernel PLS (KPLS) based soft sensor approaches, examine their performances, and discuss their pros and cons. (C) 2019 Elsevier Ltd. All rights reserved.