Transfer of multivariate classification models applied to digital images and fluorescence spectroscopy data
| Autor: | Karla Danielle Tavares de Melo Milanez, Thiago César Araújo Nóbrega, Márcio José Coelho Pontes, Matías Insausti, Danielle Silva Do Nascimento |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
02 engineering and technology
01 natural sciences ADULTERATION Fluorescence spectroscopy Analytical Chemistry Digital image CLASSIFICATION TRANSFER Histogram Partial least squares regression Statistics Spectroscopy Mathematics business.industry 010401 analytical chemistry Analytical technique Ciencias Químicas Digital imaging Pattern recognition 021001 nanoscience & nanotechnology Linear discriminant analysis EXTRA VIRGIN OLIVE OIL 0104 chemical sciences DIGITAL IMAGES FLUORESCENCE SPECTROSCOPY Piecewise Química Analítica Artificial intelligence 0210 nano-technology business CIENCIAS NATURALES Y EXACTAS |
| Zdroj: | Microchemical Journal. 133:669-675 |
| ISSN: | 0026-265X |
| DOI: | 10.1016/j.microc.2017.03.004 |
| Popis: | This work evaluates the use of transfer of classification models for identifying adulteration of extra virgin olive oil (EVOO) samples involving, separately, two analytical techniques: fluorescence spectroscopy and digital imaging. The chemometric procedures, including development of classification models and application of classification transfer methods, were performed individually for each analytical technique. Methods of direct standardization (DS) and piecewise direct standardization (PDS) were applied to transfer samples sets in order to estimate an adjustment function and apply it to a samples set measured by the secondary instrument. For purposes of comparison, classification models were built based on linear discriminant analysis (LDA) with previous selection of variables by the successive projections algorithm (SPA), and partial least squares discriminant analysis (PLS-DA). The performance of the classification models was evaluated according to the number of errors and correct classification rate (CCR) for the prediction set measured by the secondary instrument. Before standardization, SPA-LDA and PLS-DA models achieved the same CCR using two analytical techniques: 54% for fluorescence emission spectra and 47% for histograms of digital images. After the standardization, a substantial increase of the CCR was observed. For the SPA-LDA models, a CCR of 88% was obtained for the fluorescence emission spectra and 82% for the histograms of the digital images. The PLS-DA classification models reached 85% and 76% of CCR for the fluorescence and imaging data, respectively, after standardization. These results demonstrate the efficiency of standardization procedures applied to multivariate classification models developed from fluorescence spectroscopy and digital images. Fil: Milanez, Karla Danielle Tavares Melo. Universidade Federal da Paraíba; Brasil Fil: Nóbrega, Thiago César Araújo. Universidade Federal da Paraíba; Brasil Fil: Silva Do Nascimento, Danielle. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; Argentina. Universidade Federal da Paraíba; Brasil Fil: Insausti, Matías. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; Argentina Fil: Pontes, Márcio José Coelho. Universidade Federal da Paraíba; Brasil |
| Databáze: | OpenAIRE |
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