Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm

TALANTA

卷:181

Article

de Almeida, Valber Elias¹  Gomes, Adriano de Araujo²  de Sousa Fernandes, David Douglas¹  Casimiro Goicoechea, Hector³  Harrop Galvao, Roberto Kawakami⁴  Ugulino Araujo, Mario Cesar¹ 

[1] Univ Fed Paraiba, CCEN, Lab Automacao & Instrumentacao Quim Analit & Quim, Dept Quim, Caixa Postal 5093, BR-58051970 Joao Pessoa, PB, Brazil

[2] Univ Fed Sul & Sudoeste Para, Inst Ciencias Exatas, Fac Quim, Folha 17,Quadra 04, BR-68505080 Maraba, Para, Brazil

[3] Univ Natl Litoral, CONICET, Lab Desarrollo Analit Quimiometr LADAQ, Fac Bioquim & Ciencias Biol,Catedra Quim Analit 1, Ciudad Univ, RA-3000 Santa Fe, Argentina

[4] Inst Tecnol Aeronaut, Div Engn Eletron, BR-12228900 Sao Jose Dos Campos, SP, Brazil

关键词: Nonlinear multivariate calibration;    Kernel partial least squares;    Successive projections algorithm;    Variable selection;    Near infrared spectrometry;    Sugar;   

DOI  :  10.1016/j.talanta.2017.12.064

来源: Elsevier

【 摘 要 】

This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions.

【 授权许可】

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