Artículos de revistas
Aggregated Functional Data Model For Near-infrared Spectroscopy Calibration And Prediction
Registro en:
Aggregated Functional Data Model For Near-infrared Spectroscopy Calibration And Prediction. Taylor & Francis Ltd, v. 42, p. 127-143 Jan-2015.
0266-4763
WOS:000343668600010
10.1080/02664763.2014.938224
Autor
Dias
Ronaldo; Garcia
Nancy L.; Ludwig
Guilherme; Saraiva
Marley A.
Institución
Resumen
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Calibration and prediction for NIR spectroscopy data are performed based on a functional interpretation of the Beer-Lambert formula. Considering that, for each chemical sample, the resulting spectrum is a continuous curve obtained as the summation of overlapped absorption spectra from each analyte plus a Gaussian error, we assume that each individual spectrum can be expanded as a linear combination of B-splines basis. Calibration is then performed using two procedures for estimating the individual analytes' curves: basis smoothing and smoothing splines. Prediction is done by minimizing the square error of prediction. To assess the variance of the predicted values, we use a leave-one-out jackknife technique. Departures from the standard error models are discussed through a simulation study, in particular, how correlated errors impact on the calibration step and consequently on the analytes' concentration prediction. Finally, the performance of our methodology is demonstrated through the analysis of two publicly available datasets. 42 1
127 143 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) FAPEG [201210267000904] Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)