In this paper, a new algorithm for multivariate calibration named principle component regression based on wavelet (PCRW) is proposed. The algorithm is constructed by integrating wavelet transform with principal component regression. The results of theoretical analysis and simulated experiments demonstrate that the new algorithm can more effectively filter off noise and extract useful information from the actual spectral data. Applying this method to the practical analysis of Chloramphenicolum and Metronidazok leads to a decrease in the average of mean relative error (MRE) from 1.70% obtained by PCR to 0.90% obtained by PCRW. Furthermore, by combining statistical criteria and multiscale analysis of wavelet, a new method for determining the number of principle components is developed. The theoretical and experimental investigations show that the new method is more reliable than conventional ones.

Key words: PHOTOMETRIC ANALYSIS, STOICHIOMETRY, CHLORAMPHENICOLUM