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Selection of robust variables for transfer of classification models employing the successive projections algorithm.
Milanez, Karla Danielle Tavares Melo; Araújo Nóbrega, Thiago César; Silva Nascimento, Danielle; Galvão, Roberto Kawakami Harrop; Pontes, Márcio José Coelho.
Affiliation
  • Milanez KDTM; Departamento de Química, Universidade Federal da Paraíba, João Pessoa, PB, Brazil.
  • Araújo Nóbrega TC; Departamento de Química, Universidade Federal da Paraíba, João Pessoa, PB, Brazil.
  • Silva Nascimento D; Laboratorio FIA, INQUISUR-CONICET, Departamento de Química, Universidad Nacional del Sur, Av. Alem 1253, B8000CPB Bahía Blanca, Buenos Aires, Argentina.
  • Galvão RKH; Instituto Tecnológico de Aeronáutica, Divisão de Engenharia Eletrônica, São José dos Campos, São Paulo 12228-900, Brazil.
  • Pontes MJC; Departamento de Química, Universidade Federal da Paraíba, João Pessoa, PB, Brazil. Electronic address: marciocoelho@quimica.ufpb.br.
Anal Chim Acta ; 984: 76-85, 2017 Sep 01.
Article in En | MEDLINE | ID: mdl-28843571
Multivariate models have been widely used in analytical problems involving quantitative and qualitative analyzes. However, there are cases in which a model is not applicable to spectra of samples obtained under new experimental conditions or in an instrument not involved in the modeling step. A solution to this problem is the transfer of multivariate models, usually performed using standardization of the spectral responses or enhancement of the robustness of the model. This present paper proposes two new criteria for selection of robust variables for classification transfer employing the successive projections algorithm (SPA). These variables are then used to build models based on linear discriminant analysis (LDA) with low sensitivity with respect to the differences between the responses of the instruments involved. For this purpose, transfer samples are included in the calculation of the cost for each subset of variables under consideration. The proposed methods are evaluated for two case studies involving identification of adulteration of extra virgin olive oil (EVOO) and hydrated ethyl alcohol fuel (HEAF) using UV-Vis and NIR spectroscopy, respectively. In both cases, similar or better classification transfer results (obtained for a test set measured on the secondary instrument) employing the two criteria were obtained in comparison with direct standardization (DS) and piecewise direct standardization (PDS). For the UV-Vis data, both proposed criteria achieved the correct classification rate (CCR) of 85%, while the best CCR obtained for the standardization methods was 81% for DS. For the NIR data, 92.5% of CCR was obtained by both criteria as well as DS. The results demonstrated the possibility of using either of the criteria proposed for building robust models as an alternative to the standardization of spectral responses for transfer of classification.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Algorithms / Discriminant Analysis / Spectroscopy, Near-Infrared / Ethanol / Olive Oil Type of study: Prognostic_studies / Qualitative_research Language: En Journal: Anal Chim Acta Year: 2017 Document type: Article Affiliation country: Brazil Country of publication: Netherlands

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Algorithms / Discriminant Analysis / Spectroscopy, Near-Infrared / Ethanol / Olive Oil Type of study: Prognostic_studies / Qualitative_research Language: En Journal: Anal Chim Acta Year: 2017 Document type: Article Affiliation country: Brazil Country of publication: Netherlands