Mathematical models for prediction of active substance content in pharmaceutical tablets and moisture in wheat

In the prediction of active substance content in pharmaceutical tablets and moisture in wheat, a very large number of wavelengths were used. Hence, a method to identify a limited number of wavelengths was developed. We introduce a novel approach that uses the discrete cosine transform (DCT) for this purpose. The data was obtained using near infrared spectrometer. From the DCT coefficients, a limited number was chosen as predictor variables to be used in partial least square (PLS) regression. Likewise, a limited number of DFT coefficients were also used in the PLS regression. The performance of combining the DCT with PLS was compared with that of the PLS model using the full spectral data and with the discrete Fourier transform (DFT). The results showed that the PLS model using DCT coefficients produced lower root mean square error than using the full NIR spectral data with the PLS and also the DFT.