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In this work, multivariate statistical techniques are employed to determine patterns and conversion curves from time-resolved X-ray powder diffraction data. For these purposes, time-window statistical total correlation spectroscopy is introduced for the pattern matching of the crystalline phase and is shown to be effective even in the case of overlapping peaks. When combined with evolving factor analysis and multivariate curve resolution-alternating least squares, this technique allows a definite estimation of patterns and conversion curves. The procedure is applied to in situ synchrotron powder diffraction patterns to monitor the setting reaction of magnesium potassium phosphate ceramic (MKP) from magnesia (MgO) and potassium dihydrogen phosphate. It is shown that the phases involved in the reaction are clearly distinguished and their evolution is correctly described. The conversion curves estimated with the proposed procedure are compared with the ones determined with the peak integration method, leading to an excellent agreement (Pearson's correlation coefficient equal to 0.9995 and 0.9998 for MgO and MKP, respectively). The approach also allows for the detection and description of the evolution of amorphous phases that cannot be described through conventional analysis of powder diffraction data

The method here proposed based on the moving window principal component analysis can be employed in timevarying processes tracked through in situ spectroscopy. In the case under investigation, it aimed to detect the nucleation during crystallization processes. For this purpose, statistical indexes were employed, and the contribution plot helped identifying the spectral variables that were changing due to nucleation. Isonicotinamide was here considered as a model active pharmaceutical ingredient, and its cooling crystallization was monitored by means of in situ infrared spectroscopy. The procedure allowed to overcome issues that may be encountered with static principal component analysis, since it could distinguish the slow-varying changes due to external perturbations (temperature) from abnormal events such as the sudden concentration decrease related to the crystallization. The proposed method was demonstrated to correctly detect nucleation without any a priori knowledge of the peaks involved in the process, leading the false alarm rate from 77.38% (obtained with the static principal component analysis) to 6.9%.

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