Jarno Kohonen

Raman spectroscopic imaging of indomethacin loaded in porous silica

Loading of a poorly soluble drug such as indomethacin (IMC) into porous silica particles enhances its dissolution upon administration. The distribution of the different solid forms in which IMC may appear was studied using Raman spectroscopy. Raman mapping of the samples was performed with a Raman microscope equipped with an automated xy-stage. The spectral data were extracted in the range 1500–1750 cm−1, which represents the stretching of the CO bond in the IMC molecule. To alleviate the problem of overlapping peaks in the Raman spectra of the different IMC forms, the spectral data were analyzed using partial least squares (PLS) and principal component analysis (PCA). Despite the problems caused by fluorescence, the method gave valuable information about the occurrence and distribution of the solid forms of IMC. The same approach was utilized for analysis of the heterogeneity of recrystallized IMC samples, and PCA was shown to be capable of revealing the presence of solvates or polymorphs not included in the model.

Evaluation of variation in dynamic processes via online spectrometers

Theory of sampling offers powerful tools for process optimization. An adequate sampling interval can be determined for spectral measurements when utilizing a multivariate extension of variography by applying score vectors as independent sources of uncertainty. The traditional way is to apply variographic analyses into single process variables independently. In the multivariate extension, those process variables are replaced with score vectors of principal component analysis. The combined uncertainty found this way depends not only on the variance in the spectra, but also, for example, on the number of utilized score vectors and the preprocessing method. This approach is illustrated with a crystallization process continuously followed with an attenuated total reflectance Fourier transform infrared instrument. The results show that the approach is highly applicable but should only be utilized as an indicative tool. Copyright

On the effectiveness of cross-fitting in multi-block PLS (CF-MBPLS)

Multi‐block PLS is an extension of partial least squares or projection to latent structures (PLS), where the descriptor matrix is divided into meaningful blocks based on either process units or type of data. A typical application is using process variables as one block and spectral data on another block. It has been utilized in obtaining more information of processes and the effect of different types of variables. In comparison with priority or hierarchical PLS, in multi‐block PLS, there is no need to prioritize blocks in advance because they are iteratively calculated at the same time. With multi‐block PLS, however, it is easy to overfit data resulting in a poor predictive ability. A recent development called cross‐fitting has been reported to alleviate the problem of overfitting in PLS. This approach was adjusted to multi‐block PLS and is tested on two different data sets, where overfitting and sensitivity to outliers are issues. Copyright