2026-04-19T23:49:41Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/82812024-01-11T01:05:24Zcom_10259_4249com_10259_5086com_10259_2604col_10259_4250

Valencia García, Olga 759 600 0000-0002-7105-7309 Ortiz Fernández, Mª Cruz 406 600 0000-0002-4751-8929 Sarabia Peinador, Luis Antonio 534 600 0000-0002-5936-7929 2024-01-10T11:19:22Z 2024-01-10T11:19:22Z 2021 0886-9383 http://hdl.handle.net/10259/8281 10.1002/cem.3341 1099-128X Relative errors are typically used in chemometrics to evaluate the performance of a multivariate predictive model. However, these models are not obtained through the criterion of minimizing relative errors, as would be expected in a model whose response is the concentration of an analyte. There are no studies in chemometrics on the use of a principal component regression that minimizes the sum of the squares of the relative errors. This work proposes a model, which serves this purpose. The suggested model, wPCR, has been applied to 7 datasets with 12 predicted responses, 10 of which are multivariate calibrations of analytes in complex mixtures based on instrumental signals coming from various analytical techniques. As PCR and wPCR are methods seeking to optimize different criteria, each one achieves a better performance with respect to its own criterion. Therefore, the new model wPCR leads to better results insofar as the relative errors are considered, especially for the smallest responses. In this sense, the wPCR model also outperforms PCR with logarithmic transformation of the response (logPCR). In addition, as for the performance of the method using Joint Confidence Regions for the intercept and the slope of the accuracy line, it is shown that the application of wPCR does not introduce bias, neither constant nor proportional for the models built, nor a systematic alteration of the achievable accuracy. The authors thank the financial support provided by Spanish MINECO (AEI/FEDER, UE) through project CTQ2017-88894-R and Consejería de la Junta de Castilla y León (BU052P20), both co-financed with European Regional Development Fund, Junta de Castilla y León and Fondo Social Europeo. application/pdf eng Wiley Journal of Chemometrics. 2021, V. 35, n. 6, e3341 https://doi.org/10.1002/cem.3341 Accuracy line Joint confidence region Multivariate calibration Principal component regression Relative error Matemáticas Mathematics Química Chemistry Química analítica Chemistry, Analytic Principal component regression that minimizes the sum of the squares of the relative errors: Application in multivariate calibration models info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion info:eu-repo/semantics/openAccess Journal of Chemometrics 35 6 THUMBNAIL Valencia-joc_2021.pdf.jpg Valencia-joc_2021.pdf.jpg IM Thumbnail image/jpeg 3810 https://riubu.ubu.es/bitstream/10259/8281/4/Valencia-joc_2021.pdf.jpg 0ba699c57f7e09c80e5a0ccf4c242b20 MD5 4 ORIGINAL Valencia-joc_2021.pdf Valencia-joc_2021.pdf application/pdf 529342 https://riubu.ubu.es/bitstream/10259/8281/3/Valencia-joc_2021.pdf 15f5318071779ffcb854d88b49c77ea8 MD5 3 LICENSE license.txt license.txt text/plain; charset=utf-8 998 https://riubu.ubu.es/bitstream/10259/8281/2/license.txt aa90105e1c90a1b4fe810649aa967b7d MD5 2 10259/8281 oai:riubu.ubu.es:10259/8281 2024-01-11 02:05:24.952 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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