Dynamic Localized SNV, Peak SNV, and Partial Peak SNV: Novel Standardization Methods for Preprocessing of Spectroscopic Data Used in Predictive Modeling
المؤلفون المشاركون
Otto, Matthias
Grisanti, Emily
Totska, Maria
Huber, Stefan
Krick Calderon, Christina
Hohmann, Monika
Lingenfelser, Dominic
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-10-28
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Grisanti, Emily& Totska, Maria& Huber, Stefan& Krick Calderon, Christina& Hohmann, Monika& Lingenfelser, Dominic…[et al.]. 2018. Dynamic Localized SNV, Peak SNV, and Partial Peak SNV: Novel Standardization Methods for Preprocessing of Spectroscopic Data Used in Predictive Modeling. Journal of Spectroscopy،Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1202536
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Grisanti, Emily…[et al.]. Dynamic Localized SNV, Peak SNV, and Partial Peak SNV: Novel Standardization Methods for Preprocessing of Spectroscopic Data Used in Predictive Modeling. Journal of Spectroscopy No. 2018 (2018), pp.1-14.
https://search.emarefa.net/detail/BIM-1202536
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Grisanti, Emily& Totska, Maria& Huber, Stefan& Krick Calderon, Christina& Hohmann, Monika& Lingenfelser, Dominic…[et al.]. Dynamic Localized SNV, Peak SNV, and Partial Peak SNV: Novel Standardization Methods for Preprocessing of Spectroscopic Data Used in Predictive Modeling. Journal of Spectroscopy. 2018. Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1202536
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1202536
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر