Dynamic Localized SNV, Peak SNV, and Partial Peak SNV: Novel Standardization Methods for Preprocessing of Spectroscopic Data Used in Predictive Modeling

Dominic Lingenfelser,Emily Grisanti,Matthias Otto,Monika Hohmann,Stefan Huber,Christina Krick Calderon,Maria Totska

doi:10.1155/2018/5037572

Dominic Lingenfelser, Emily Grisanti + Show 5 more

Open Access

https://doi.org/10.1155/2018/5037572

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Abstract

An essential part of multivariate analysis in spectroscopic context is preprocessing. The aim of preprocessing is to remove scattering phenomena or disturbances in the spectra due to measurement geometry in order to improve subsequent predictive models. Especially in vibrational spectroscopy, the Standard Normal Variate (SNV) transformation has become very popular and is widely used in many practical applications, but standardization is not always ideal when performed across the full spectrum. Herein, three different new standardization techniques are presented that apply SNV to defined regions rather than to the full spectrum: Dynamic Localized SNV (DLSNV), Peak SNV (PSNV) and Partial Peak SNV (PPSNV). DLSNV is an extension of the Localized SNV (LSNV), which allows a dynamic starting point of the localized windows on which the SNV is executed individually. Peak and Partial Peak SNV are based on picking regions from the spectra with a high correlation to the target value and perform SNV on these essential regions to ensure optimal scatter correction. All proposed methods are able to significantly improve the model performance in cross validation and robustness tests compared to SNV. The prediction errors could be reduced by up to 16% and 29% compared with LSNV for two regression models.

Highlights

Chemometric approaches are becoming increasingly popular as they enable more comprehensive extraction of relevant information out of complex data provided by modern instrumental analytics
All proposed methods are able to significantly improve the model performance in cross validation and robustness tests compared to Standard Normal Variate (SNV). e prediction errors could be reduced by up to 16% and 29% compared with Localized SNV (LSNV) for two regression models
A Partial Peak SNV (PPSNV) spectrum is calculated as follows: PPSNV algorithm (i) Perform SNV across the POIs with a left and right margin of pw e optimization focuses on the adjustment of the window size pw around the POIs in which the SNV is applied for maximal predictive power in calibration (Figure 7(c)). e optimization process is divided into the following steps: (1) Fit the data set to the target values (2) Pick peaks from the normalized model coe cient vector (|w|/max(|w|)), threshold for peaks 0.1

Summary

Introduction

Chemometric approaches are becoming increasingly popular as they enable more comprehensive extraction of relevant information out of complex data provided by modern instrumental analytics. In combination with multivariate calibration, the development of models based on low-cost analytics, such as vibrational spectroscopy, allows the development of models that predict parameters usually determined with cost-intensive measuring instruments or complex methods. Preprocessing methods play a decisive role for the performance of these models, as spectra can be influenced by various disturbing factors that interfere with the significance of the measurement [8,9,10,11]. A reference spectrum, in most cases represented by the mean spectrum of the calibration data set, is defined, and the spectra are corrected for the baseline and the multiplicative amplification effects [16, 17]. PSNV and PPSNV are based on the idea that the standardization can be optimized when performed on distinct wavenumber windows across highly specific regions of the spectrum

Experimental

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Methods

Results and Discussion

Method