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Mas, C. 47cddb56-4908-4220-aaf5-43603280ba2c 500 Rubio Martínez, Laura 492 500 Valverde Som, Lucía 583 500 Sarabia Peinador, Luis Antonio 534 500 Ortiz Fernández, Mª Cruz 406 500 2020-05-11T19:17:55Z 2020-05-11T19:17:55Z 2020-06 0169-7439 http://hdl.handle.net/10259/5300 10.1016/j.chemolab.2020.104006 The efficiency of the analytical methods based on vibrational spectroscopy has been widely verified in a high number of publications. In addition, it has been recognized that the pretreatment of the original signals is absolutely necessary to obtain enough quality in the subsequent classification and/or regression models. In fact, an inappropriate pretreatment makes the results worse. It is also impossible to give “a priori” rules that guarantee the adequacy of a pretreatment for specific data. The effect of the pretreatments is evaluated through their impact on the quality of the classification and/or regression models built from them due to the double dependence (on the data and on the purpose of the analysis). The effect of the pretreatment has been evaluated using partial least squares regression (PLSR) in some works and the root mean squares in prediction or in cross-validation has been always used as a criterion to evaluate the regression in all these cases. However, it seems appropriate to use quality criteria of the calibration of the analytical method through the figures of merit: the significance of the regression, the absence of constant or proportional bias, the residual standard deviation, the mean of the absolute values of the relative errors and the capability of detection. In this work, the use of these analytical criteria in a desirability function is proposed for the first time with calibration data of oxybenzone obtained by ATR-FTIR and PLSR. This desirability function enables to choose the best pretreatment among the 39 possibilities studied. In addition, it is shown that the same optimum is not obtained if the minimum of RMSEC_CV is considered as a criterion. Spanish MINECO (AEI/FEDER, UE) through project CTQ2017‐88894‐R and by Junta de Castilla y León through project BU012P17 (both co‐financed with European FEDER funds). L. Valverde-Som thanks JCyL for her postdoctoral contract through BU012P17 project. application/pdf eng Elsevier Chemometrics and Intelligent Laboratory Systems. 2020, V. 201, 104006 https://doi.org/10.1016/j.chemolab.2020.104006 info:eu-repo/grantAgreement/MINECO/CTQ2017‐88894‐R info:eu-repo/grantAgreement/JCyL/BU012P17 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Pretreatment data ATR-FTIR PLS Oxybenzone Desirability function Figures of merit Química analítica Chemistry, Analytic Impact of the pretreatment of ATR-FTIR signals on the figures of merit when PLS is used info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Chemometrics and Intelligent Laboratory Systems 201 104006 TEXT Mas-cils_2020.pdf.txt Mas-cils_2020.pdf.txt Extracted text text/plain 45486 https://riubu.ubu.es/bitstream/10259/5300/5/Mas-cils_2020.pdf.txt e9f831120359591ab97661fa575a2824 MD5 5 THUMBNAIL Mas-cils_2020.pdf.jpg Mas-cils_2020.pdf.jpg IM Thumbnail image/jpeg 4363 https://riubu.ubu.es/bitstream/10259/5300/4/Mas-cils_2020.pdf.jpg cc8f6dec6ab79b523ce5460fe78d678b MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 998 https://riubu.ubu.es/bitstream/10259/5300/3/license.txt aa90105e1c90a1b4fe810649aa967b7d MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 805 https://riubu.ubu.es/bitstream/10259/5300/2/license_rdf 4460e5956bc1d1639be9ae6146a50347 MD5 2 ORIGINAL Mas-cils_2020.pdf Mas-cils_2020.pdf application/pdf 676708 https://riubu.ubu.es/bitstream/10259/5300/1/Mas-cils_2020.pdf 7ad5779ad120bb94cd14e6582a087492 MD5 1 10259/5300 oai:riubu.ubu.es:10259/5300 2022-06-16 00:42:06.848 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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