| DETECTING AND ADJUSTING FOR NON-LINEARITIES IN CALIBRATION OF NEAR-INFRARED DATA USING PRINCIPAL COMPONENTS |
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| 作者姓名： | SAMUEL D.OMAN TORMOD NAES ANAN ZUBE |
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| 作者单位： | Department of Statistics Hebrew University，Jerusalem 91905，Israel，Norwegian Food Research Institute，Oslovegen 1，N-1430 Aas-NLH，Norway，Department of Statistics，Hebrew University，Jerusalem 91905，Israel |
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| 摘 要： | A new regression method for non-linear near-infrared spectroscopic data is proposed.The technique isbased on a model which is linear in the principal components and simple functions(squares and products)of them.Added variable plots are used to determine which squares and products to incorporate into themodel.The regression coefficients are estimated by a Stein estimate which shrinks towards the estimatedetermined by the first several principal components and the selected non-linear terms.The technique isnot computationally intensive and is appropriate for routine predictions of chemical concentrations.Themethod is tested on three data sets and in all cases gives more accurate predictions than does linearprincipal components regression.
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DETECTING AND ADJUSTING FOR NON-LINEARITIES IN CALIBRATION OF NEAR-INFRARED DATA USING PRINCIPAL COMPONENTS |
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| SAMUEL D.OMAN,TORMOD NAES,ANAN ZUBE.DETECTING AND ADJUSTING FOR NON-LINEARITIES IN CALIBRATION OF NEAR-INFRARED DATA USING PRINCIPAL COMPONENTS[J].Journal of Geographical Sciences,1993(3). |
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| Authors: | SAMUEL DOMAN |
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| Institution: | SAMUEL D.OMAN Department of Statistics,Hebrew University,Jerusalem,IsraelTORMOD NAES Norwegian Food Research Institute,Oslovegen,N- Aas-NLH,NorwayANAN ZUBE Department of Statistics,Hebrew University,Jerusalem,Israel |
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| Abstract: | A new regression method for non-linear near-infrared spectroscopic data is proposed.The technique is based on a model which is linear in the principal components and simple functions(squares and products) of them.Added variable plots are used to determine which squares and products to incorporate into the model.The regression coefficients are estimated by a Stein estimate which shrinks towards the estimate determined by the first several principal components and the selected non-linear terms.The technique is not computationally intensive and is appropriate for routine predictions of chemical concentrations.The method is tested on three data sets and in all cases gives more accurate predictions than does linear principal components regression. |
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| Keywords: | Calibration Non-linearity Principal components Stein estimate |
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