A method for calibration and validation subset partitioning

This paper proposes a new method to divide a pool of samples into calibration and validation subsets for multivariate modelling. The proposed method is of value for analytical applications involving complex matrices, in which the composition variability of real samples cannot be easily reproduced by...

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Published in	Talanta (Oxford) Vol. 67; no. 4; pp. 736 - 740
Main Authors	Galvão, Roberto Kawakami Harrop, Araujo, Mário César Ugulino, José, Gledson Emídio, Pontes, Marcio José Coelho, Silva, Edvan Cirino, Saldanha, Teresa Cristina Bezerra
Format	Journal Article
Language	English
Published	Amsterdam Elsevier B.V 15.10.2005 Oxford Elsevier
Subjects	Analytical chemistry Chemistry Diesel analysis Exact sciences and technology Kennard–Stone algorithm NIR spectrometry PLS regression Sample subset partitioning Spectrometric and optical methods PLS regression Diesel analysis Kennard–Stone algorithm NIR spectrometry Sample subset partitioning Performance evaluation Calibration Multivariate analysis Algorithm Near infrared spectrometry Instrumental response Kennard-Stone algorithm Partitioning Experimental design Sampling Comparative study NIR spectroinetry
Online Access	Get full text
ISSN	0039-9140 1873-3573 1873-3573
DOI	10.1016/j.talanta.2005.03.025

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Abstract	This paper proposes a new method to divide a pool of samples into calibration and validation subsets for multivariate modelling. The proposed method is of value for analytical applications involving complex matrices, in which the composition variability of real samples cannot be easily reproduced by optimized experimental designs. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. The proposed technique is illustrated in a case study involving the prediction of three quality parameters (specific mass and distillation temperatures at which 10 and 90% of the sample has evaporated) of diesel by NIR spectrometry and PLS modelling. For comparison, PLS models are also constructed by full cross-validation, as well as by using the Kennard–Stone and random sampling methods for calibration and validation subset partitioning. The obtained models are compared in terms of prediction performance by employing an independent set of samples not used for calibration or validation. The results of F-tests at 95% confidence level reveal that the proposed technique may be an advantageous alternative to the other three strategies.
AbstractList	This paper proposes a new method to divide a pool of samples into calibration and validation subsets for multivariate modelling. The proposed method is of value for analytical applications involving complex matrices, in which the composition variability of real samples cannot be easily reproduced by optimized experimental designs. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. The proposed technique is illustrated in a case study involving the prediction of three quality parameters (specific mass and distillation temperatures at which 10 and 90% of the sample has evaporated) of diesel by NIR spectrometry and PLS modelling. For comparison, PLS models are also constructed by full cross-validation, as well as by using the Kennard-Stone and random sampling methods for calibration and validation subset partitioning. The obtained models are compared in terms of prediction performance by employing an independent set of samples not used for calibration or validation. The results of F-tests at 95% confidence level reveal that the proposed technique may be an advantageous alternative to the other three strategies.This paper proposes a new method to divide a pool of samples into calibration and validation subsets for multivariate modelling. The proposed method is of value for analytical applications involving complex matrices, in which the composition variability of real samples cannot be easily reproduced by optimized experimental designs. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. The proposed technique is illustrated in a case study involving the prediction of three quality parameters (specific mass and distillation temperatures at which 10 and 90% of the sample has evaporated) of diesel by NIR spectrometry and PLS modelling. For comparison, PLS models are also constructed by full cross-validation, as well as by using the Kennard-Stone and random sampling methods for calibration and validation subset partitioning. The obtained models are compared in terms of prediction performance by employing an independent set of samples not used for calibration or validation. The results of F-tests at 95% confidence level reveal that the proposed technique may be an advantageous alternative to the other three strategies. This paper proposes a new method to divide a pool of samples into calibration and validation subsets for multivariate modelling. The proposed method is of value for analytical applications involving complex matrices, in which the composition variability of real samples cannot be easily reproduced by optimized experimental designs. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. The proposed technique is illustrated in a case study involving the prediction of three quality parameters (specific mass and distillation temperatures at which 10 and 90% of the sample has evaporated) of diesel by NIR spectrometry and PLS modelling. For comparison, PLS models are also constructed by full cross-validation, as well as by using the Kennard–Stone and random sampling methods for calibration and validation subset partitioning. The obtained models are compared in terms of prediction performance by employing an independent set of samples not used for calibration or validation. The results of F-tests at 95% confidence level reveal that the proposed technique may be an advantageous alternative to the other three strategies. This paper proposes a new method to divide a pool of samples into calibration and validation subsets for multivariate modelling. The proposed method is of value for analytical applications involving complex matrices, in which the composition variability of real samples cannot be easily reproduced by optimized experimental designs. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. The proposed technique is illustrated in a case study involving the prediction of three quality parameters (specific mass and distillation temperatures at which 10 and 90% of the sample has evaporated) of diesel by NIR spectrometry and PLS modelling. For comparison, PLS models are also constructed by full cross-validation, as well as by using the Kennard-Stone and random sampling methods for calibration and validation subset partitioning. The obtained models are compared in terms of prediction performance by employing an independent set of samples not used for calibration or validation. The results of F-tests at 95% confidence level reveal that the proposed technique may be an advantageous alternative to the other three strategies.
Author	José, Gledson Emídio Silva, Edvan Cirino Saldanha, Teresa Cristina Bezerra Pontes, Marcio José Coelho Galvão, Roberto Kawakami Harrop Araujo, Mário César Ugulino
Author_xml	– sequence: 1 givenname: Roberto Kawakami Harrop surname: Galvão fullname: Galvão, Roberto Kawakami Harrop organization: Instituto Tecnológico de Aeronáutica, Divisão de Engenharia Eletrônica, São José dos Campos, São Paulo, Brazil – sequence: 2 givenname: Mário César Ugulino surname: Araujo fullname: Araujo, Mário César Ugulino email: laqa@quimica.ufpb.br organization: Universidade Federal da Paraíba, Departamento de Química, P.O. Box 5093, João Pessoa, Paraiba 58051-970, Brazil – sequence: 3 givenname: Gledson Emídio surname: José fullname: José, Gledson Emídio organization: Universidade Federal da Paraíba, Departamento de Química, P.O. Box 5093, João Pessoa, Paraiba 58051-970, Brazil – sequence: 4 givenname: Marcio José Coelho surname: Pontes fullname: Pontes, Marcio José Coelho organization: Universidade Federal da Paraíba, Departamento de Química, P.O. Box 5093, João Pessoa, Paraiba 58051-970, Brazil – sequence: 5 givenname: Edvan Cirino surname: Silva fullname: Silva, Edvan Cirino organization: Universidade Federal da Paraíba, Departamento de Química, P.O. Box 5093, João Pessoa, Paraiba 58051-970, Brazil – sequence: 6 givenname: Teresa Cristina Bezerra surname: Saldanha fullname: Saldanha, Teresa Cristina Bezerra organization: Universidade Federal da Paraíba, Departamento de Química, P.O. Box 5093, João Pessoa, Paraiba 58051-970, Brazil
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17087386$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/18970233$$D View this record in MEDLINE/PubMed
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References	Tominaga (bib7) 1998; 43 Kanduc, Zupan, Majcen (bib10) 2003; 65 Capitan-Vallvey, Navas, del Olmo, Consonni, Todeschini (bib13) 2000; 52 Dantas Filho, Galvão, Araújo, Silva, Saldanha, José, Pasquini, Raimundo, Rohwedder (bib14) 2004; 72 Kennard, Stone (bib11) 1969; 11 Beebe, Pell, Seasholtz (bib5) 1998 Gomez-Carracedo, Andrade, Calvino, Prada, Fernandez, Muniategui (bib4) 2003; 60 Massart, Vandeginste, Buydens, De Jong, Lewi, Smeyers-Verbeke (bib1) 1997 Daszykowski, Walczak, Massart (bib6) 2002; 468 Mansfield, Barman (bib2) 1999; 71 Judge (bib3) 2004; 62 Wu, Walczak, Massart, Heuerding, Erni, Last, Prebble (bib9) 1996; 33 Bouveresse, Hartmann, Massart, Last, Prebble (bib12) 1996; 68 Sales, Rius, Callao, Rius (bib8) 2000; 52
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Snippet	This paper proposes a new method to divide a pool of samples into calibration and validation subsets for multivariate modelling. The proposed method is of...
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SubjectTerms	Analytical chemistry Chemistry Diesel analysis Exact sciences and technology Kennard–Stone algorithm NIR spectrometry PLS regression Sample subset partitioning Spectrometric and optical methods
Title	A method for calibration and validation subset partitioning
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