Two Efficient Preprocess Algorithms of Traditional Chinese Herbal Medicine Fingerprint Signal

This paper is devoted to achieving two efficient algorithms for preprocessing Traditional Chinese Herbal Medicine (TCHM) fingerprint signals. One is to split the baseline of the TCHM fingerprint signal via the second-generation wavelet transform, while the other is to correct retention time shift by the local least squares fitting. Although the first generation wavelet transform has been successfully applied to the signal processing, the complexity of the calculation is troublesome. To overcome this problem, we apply the second-generation wavelet transform technology to processing the fingerprint signal and effectively remove the baseline from the original signal. The retention time shift is the other important issue of preprocessing the TCHM fingerprint signal. The ordinary least squares fitting algorithm is not robust enough to handle the problem of retention time shifting because of the complicated multi-component signals. The authors employ a practical linear fitting method, i.e. the local least squares fitting, to deal with this problem. Finally, experimental results on TIANJIHUANG, a kind of TCHMs, show that two algorithms could successfully preprocess fingerprint signals under different experimental conditions.