zhpsЖурнал прикладной спектроскопииZhurnal Prikladnoii Spektroskopii0514-7506B. I. Stepanov Institute of Physics of the National Academy of Scienceszhps-331Research Article******СРАВНИТЕЛЬНЫЙ АНАЛИЗ ПРИМЕНЕНИЯ МЕТОДОВ ГЛАВНЫХ КОМПОНЕНТ И ЧАСТНЫХ НАИМЕНЬШИХ КВАДРАТОВ В ЛАЗЕРНО-ИСКРОВОЙ ЭМИССИОННОЙ СПЕКТРОСКОПИИCOMPARATIVE STUDY OF THE PLSR AND PCR METHODS IN LASER-INDUCED BREAKDOWN SPECTROSCOPIC ANALYSISSinghM. .SinghM. .noemail@neicon.ruSarkarA. .SarkarA. .arnab@barc.gov.in; asarkar@ymail.comАтомный исследовательский центр Бхабха; Национальный институт Хоми БхабхаBhabha Atomic Research Centre; India Homi Bhabha National Institute201810032020855852(1)-852(9)Copyright © Singh M..., Sarkar A..., 20202020Singh M..., Sarkar A...Singh M..., Sarkar A...Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://zhps.ejournal.by/jour/article/view/331

Проведено сравнение результатов применения методов главных компонент (МГК) и частных наименьших квадратов (МЧНК) к одним и тем же образцам стали, содержащим множество химических элементов. Детально изучены коэффициенты МГК и МЧНК и соответствующие им спектры. Предложен ряд рекомендаций относительно выбора метода в зависимости от аналитических ситуаций. Установлены ситуации, когда методы МГК и МЧНК эквивалентны или один из них предпочтительнее для лазерно-искровой эмиссионной спектроскопии.

Principal components regression (PCR) and partial least squares regression (PLSR) are the two most powerful and widely used chemometric regression methods applied in laser-induced breakdown spectroscopy (LIBS) studies. In this work, we compared the analytical merits (accuracy and precision) of applying the PCR and PLRS algorithms to identical LIBS data sets. A set of stainless steel samples with multielemental concentration data was studied for this work. A detailed study of the PCR and PLSR coefficients and the corresponding spectra used to generate them was carried out to understand the role of the two algorithms in the evolution of the regression coefficient signal data. Based on this understanding, a few guidelines were proposed in this work for selecting PCR or PLSR depending on the analytical situation. This work identifies the situations wherein the PCR and PLSR application results are analytically equivalent and where one approach is superior over the other for LIBS data analysis.

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The authors declare that there are no conflicts of interest present.