Regularization particle size inversion of PCS based on fast algorithm

Tikhonov regularization is an effective method for particle size inversion of Photon Correlation Spectroscopy (PCS). Regularization parameter selection is the key point of Tikhonov regularization algorithm for solving the first kind integral problem. In order to obtain the optimal regularization parameter, according to the Morozov discrepancy principal, this paper proposes an efficient regularization inversion method based on fast algorithm for implementing the problem of particle size distribution. Computer simulation data of monodisperse particles and bi-dispersed particles are inversed by this method respectively. The inversion results show that, when the noise level is 0∼0.01, inversion results of fast algorithm are reasonable, however, when the noise level is greater than 0.005, discrepancy principal can't obtain correct inversion results. Therefore, Tikhonov regularization inversion method with fast algorithm has the advantages of high accuracy, tolerance of noises and fast speed in PCS inversion.