Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/243150
Type: Artigo
Title: Aggregated functional data model for near-infrared spectroscopy calibration and prediction
Author: Dias, Ronaldo
Garcia, Nancy L.
Ludwig, G.
Saraiva, Marley A.
Abstract: 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.
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.
Subject: Espectroscopia no infravermelho próximo – Calibração - Métodos estatísticos
Calibração multivariada
Spline, Teoria do
Teoria da previsão
Country: Reino Unido
Editor: Taylor & Francis
Citation: Aggregated Functional Data Model For Near-infrared Spectroscopy Calibration And Prediction. Taylor & Francis Ltd, v. 42, p. 127-143 Jan-2015.
Rights: fechado
Identifier DOI: 10.1080/02664763.2014.938224
Address: http://www.tandfonline.com/doi/full/10.1080/02664763.2014.938224
Date Issue: 2015
Appears in Collections:IMECC - Artigos e Outros Documentos

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