The wavelet transformation is a technique that represents a curve as a combination of basis functions. These are obtained by dilation and translation of a mother wavelet. If the dilation and translation use continuous numbers, they are called as Continuous Wavelet Transformation (CWT). The results of its transformation depend on the mother wavelet type. Mother wavelet Mexican Hat is very good in illustrating the properties of Continuous Wavelet Transformation. However, another mother wavelet often used in the application is Daubechies. This study compared the mother wavelet Daubechies and Mexican Hat in multivariate calibration modeling by using gingerol data. The results showed that the mother wavelet Daubechies was better than Mexican Hat.
continuous wavelet transformation, multivariate calibration, principal component regression, dimension reduction
