Multivariate optical computation for predictive spectroscopy
Multivariate optical computation for predictive spectroscopy
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DOI：
10.1021/ac970791w
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发表时间：
1998-01-01
影响因子：
7.4
通讯作者：
Myrick, ML
中科院分区：
文献类型：
--
作者：
Nelson, MP;Aust, JF;Myrick, ML
提出了一种基于主成分分析（PCA）预测化学和物理性质的新型光学方法，并使用早期工作的数据集进行了评估。在我们的方法中，主成分分析产生的回归向量被设计成一组配对滤光片的结构。通过配对滤波器的光产生与回归向量设计的化学/物理性质成正比的模拟探测器信号。这种用于预测光谱的简单光学计算方法通过几种方式进行评估，使用示例数据进行数值模拟。我们评估了该方法对常见的各种光谱误差的敏感性，发现该方法对误差的敏感性与标准方法相同。其次，我们使用误差传播来确定检测器噪声对方法预测能力的影响，发现光学计算方法比传统方法具有较大的多路复用优势；第三，我们使用两种不同的设计方法来构建用于示例测量的配对滤波器集来评估可制造性，发现存在足够的方法来设计合适的光学器件；我们数值模拟了配对滤波器中设计误差引入的预测误差，发现预测误差不会比传统方法增加。第五，我们考虑了与化学成分（如透射光谱）无关的光强度对方法性能的影响，以及该方法仅受轻微影响的fmd。我们得出的结论是，与传统的色散或干涉测量仪器相比，基于回归（或其他）向量和线性数学的许多类型的预测测量可以通过所提出的光学计算方法更快、更有效、成本更低。虽然我们的模拟使用了拉曼实验数据，但该方法同样适用于近红外、紫外-可见、红外、荧光和其他光谱。
A novel optical approach to predicting chemical and physical properties based on principal component analysis (PCA) is proposed and evaluated using a data set from earlier work, In our approach, a regression vector produced by PCA is designed into the structure of a set of paired optical filters. Light passing through the paired filters produces an analog detector signal that is directly proportional to the chemical/physical property for which the regression vector was designed, This simple optical computational method for predictive spectroscopy is evaluated in several ways, using the example data for numeric simulation, First, we evaluate the sensitivity of the method to various types of spectroscopic errors commonly encountered and find the method to have the same susceptibilities toward error as standard methods. Second, we use propagation of errors to determine the effects of detector noise on the predictive power of the method, finding the optical computation approach to have a large multiplex advantage over conventional methods, Third, we use two different design approaches to the construction of the paired filter set for the example measurement to evaluate manufacturability, finding that adequate methods exist to design appropriate optical devices, Fourth, we numerically simulate the predictive errors introduced by design errors in the paired filters, finding that predictive errors are not increased over conventional methods, Fifth, we consider how the performance of the method is affected by light intensities that are not linearly related to chemical composition (as in transmission spectroscopy) and fmd that the method is only marginally affected, In summary, we conclude that many types of predictive measurements based on use of regression (or other) vectors and linear mathematics can be performed more rapidly, more effectly, and at considerably lower cost by the proposed optical computation method than by traditional dispersive or interferometric instrumentation. Although our simulations have used Raman experimental data, the method is equally applicable to Near-IR, UV-vis, IR, fluorescence, and other spectroscopies.