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(25) Optimized Noise Reduction in Extended Derivative Spectroscopy for NIR Calibration
¡¡¡¡¡¡of Rice Constituents ¡Ý a Preliminary Study Proceedings of Japan-Korea Joint Symposium
¡¡¡¡¡¡on Near Infrared Spectroscopy,pp.195-200,2006-6
¡¡Differentiation processing is sometimes used as effective pretreatment of NIR spectra. However, there are several drawbacks in ordinary derivative spectra; spectral peaks are shifted in first derivatives and inversed in second derivatives, not only weak absorption peaks but feeble noises are also enhanced, undesired side lobes are generated as by-produces, and the type of derivatives is practically limited only to those of first and second orders. To overcome these drawbacks and limitations, the conventional derivative has been modified on the basis of Fourier transform approach of numerical differentiation to define two types of extended derivatives with fractional order: fractional derivatives (FD) and fractional absolute derivatives (FAD). Although the extended derivatives provide us with a solution to the above problems, suitable reduction of noise is still necessary for the new derivatives to work in practice. In the present paper, the reduction of noise in
extended derivatives are discussed in the context of the calibration of rice constituents by means of PLS regressions. The reduction of noise is performed by a Gaussian filter in the Fourier domain of original spectra. It is shown that the width of the Gaussian filter should be optimized to yield best calibration result, depending upon target constituents and the fractional order of derivatives.
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