RESUMEN
Wavelet analysis (WT) was conducted on the simulated near infrared spectra (NIR) obtained by adding simulated background and simulated noise into diesel NIR Results show that the background components are mainly located in the low frequency region, while noise in the high frequency region, and useful signal in the middle frequency region. Background and noise components can be simultaneously subtracted from the spectra by WT. The WT coefficients in mid-frequency details can be selected as variables to build the multivariate calibration model, which can improve analytic accuracy and reduce the analysis time.
Asunto(s)
Simulación por Computador , Modelos Teóricos , Espectroscopía Infrarroja Corta/métodos , Espectroscopía Infrarroja Corta/normas , Algoritmos , Calibración , Reproducibilidad de los Resultados , Procesamiento de Señales Asistido por ComputadorRESUMEN
Derivative can correct baseline effects and also increase the level of noise. Wavelet transform has been proven an efficient tool for de-noising. This paper is directed to the application of wavelet transfer and derivative in the NIR analysis of octane number (RON). The derivative parameters, as well as their effects on the noise level and analytic accuracy of RON, have been studied in detail. The results show that derivative can correct the baseline effects and increase the analytic accuracy. Noise from the derivative spectra has great detriment to the analysis of RON. De-noising of wavelet transform can increase the S/N and improve the analytical accuracy.