Interval Partial Least-Squares Regression (iPLS): A Comparative Chemometric Study with an Example from Near-Infrared Spectroscopy

Applied Spectroscopy - Tập 54 Số 3 - Trang 413-419 - 2000
Lars Nørgaard1, Arild Saudland1, Joachim Wagner1, Jens Peter Nielsen1, L. Munck1, Søren Balling Engelsen1
1The Royal Veterinary and Agricultural University, Food Technology, Chemometrics Group, Department of Dairy and Food Science, Rolighedsvej 30, DK-1958 Frederiksberg C, Denmark

Tóm tắt

A new graphically oriented local modeling procedure called interval partial least-squares ( iPLS) is presented for use on spectral data. The iPLS method is compared to full-spectrum partial least-squares and the variable selection methods principal variables (PV), forward stepwise selection (FSS), and recursively weighted regression (RWR). The methods are tested on a near-infrared (NIR) spectral data set recorded on 60 beer samples correlated to original extract concentration. The error of the full-spectrum correlation model between NIR and original extract concentration was reduced by a factor of 4 with the use of iPLS ( r = 0.998, and root mean square error of prediction equal to 0.17% plato), and the graphic output contributed to the interpretation of the chemical system under observation. The other methods tested gave a comparable reduction in the prediction error but suffered from the interpretation advantage of the graphic interface. The intervals chosen by iPLS cover both the variables found by FSS and all possible combinations as well as the variables found by PV and RWR, and iPLS is still able to utilize the first-order advantage.

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Applied Spectroscopy

Tập 54 Số 3

413-419

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