F. Cuesta Sánchez

Orthogonal projection approach applied to peak purity assessment.

The orthogonal projection approach (OPA), a stepwise approach based on an orthogonalization algorithm, is proposed. The performance of OPA for the assessment of peak purity in HPLC-DAD is described and compared with that of SIMPLISMA. The occurrence of artifacts in both approaches under nonideal situations is discussed.

Resolution of multicomponent overlapped peaks by the orthogonal projection approach, evolving factor analysis and window factor analysis

Abstract The orthogonal projection approach (OPA), a stepwise approach developed for the determination of the number of compounds present in a multicomponent system, has been extended by including a step that allows the chromatographic and spectroscopic pure compound profiles to be determined. This is done using an alternating least-squares procedure. The initial estimations are the spectra selected in the first step of OPA. The performance of the OPA algorithm is compared with that of two window-based self-modelling curve resolution approaches: evolving factor analysis (EFA) and window factor analysis (WFA).

On-Line Monitoring of Powder Blending with Near-Infrared Spectroscopy

Powder blending was monitored on-line by taking near-infrared measurements at regular time intervals during the mixing process. The average standard deviation between the measurements taken at each time and the dissimilarity between each mixture spectrum and the ideal mixture spectrum were used to monitor the changes in the powder blend over time. The distribution of the pure compounds in the blend can be investigated by looking at the score plot for the first two principal components (PCs), the contribution of each variable to the dissimilarity and, in particular, the contrasts between two characteristic wavelengths for each compound. Statistical process monitoring charts were used to determine the blending time at which the mixture was within (spectroscopic) specifications. Shewhart charts monitor the blend at characteristic wavelengths for each substance separately. The Hotellings T2 test defines a multivariate confidence interval. For spectral data, feature reduction is needed. This procedure is accomplished by using characteristic wavelengths for the pure compounds or the significant PCs after performing principal components analysis (PCA).

MULTIVARIATE PEAK PURITY APPROACHES

Abstract The mathematical basis and interpretation of the results of several multivariate techniques for the determination of the number of compounds present in an evolving multicomponent system are presented. All the techniques described are applicable to bilinear data matrices obtained from evolutionary processes, i.e., the concentration of each compound evolves with an ordered variable such as time or pH. Their performance for the detection of a minor compound in the presence of a main one is discussed. The application to systems with more than 2 compounds and the effect of smoothing are also considered.

Monitoring powder blending by NIR spectroscopy

A strategy is proposed for the monitoring of powder blending. Samples are taken throughout the blender vessel and scanned by diffuse reflectance spectroscopy in the NIR range. The NIR spectra are first subjected to the Standard Normal Variate transformation (SNV). The first approach consists of overlaying the transformed spectra taken from different locations at each time. To quantify the differences between the spectra, the standard deviation spectrum at each time is calculated and the mean standard deviation value is plotted as a function of time. This plot indicates the time at which the blend is most homogeneous. Each individual spectrum can be compared with the “mixture” spectrum, which is an approximation of the spectrum of a true homogeneous sample. For that purpose several approaches, i.e. determination of the dissimilarity, Principal Component Analysis, are proposed. As an alternative approach to monitoring the blending the use of SIMPLISMA is recommended.

Algorithm for the assessment of peak purity in liquid chromatography with photodiode-array detection

Abstract Two modifications of the algorithm based on the Gram-Schmidt orthogonalization technique for the assessment of peak purity are presented. The performance of this approah is investigated for liquid chromatography with photodiode-array detection (LC-DAD) data, although its applicability is not restricted to this experimental model. This method is applied to simulated and experimental data where two compounds are eluting, but can be applied when more compounds are eluting. The results are compared with the ones obtained previously with the first version of this algorithm.

Application of the needle algorithm for exploratory analysis and resolution of HPLC-DAD data

Abstract The needle algorithm, based on a uniqueness test in target factor analysis, has been applied to several HPLC-DAD simulated data sets in both chromatographic and spectral directions. Information regarding the number of analytes in the system, the presence of minor compounds, the location of peak maxima and eventually some characteristics of the spectral shapes have been obtained. The location of peak maxima has also been used as a starting point to build a new kind of initial estimates to be input in iterative curve resolution methods, such as ALS (alternating least squares). The resolution results have been compared with the ones computed using initial estimations based on evolving factor analysis.

Application of SIMPLISMA for the assessment of peak purity in liquid chromatography with diode array detection

Abstract The application of SIMPLISMA for the investigation of peak purity with liquid chromatography and diode array detection (LC-DAD) is proposed. SIMPLISMA is applied in the Chromatographic direction, and is used to detect pure zones in the chromatogram. The performance of SIMPLISMA and of an approach based on the Gram-Schmidt orthogonalization technique are compared.

Influence and correction of temperature perturbations on NIR spectra during the monitoring of a polymorph conversion process prior to self-modelling mixture analysis

The influence of temperature variations on the rank of a NIR dataset, has been investigated by comparing the results of principal component analysis (PCA) and evolving factor analysis (EFA), applied to two datasets measured at constant temperature and varying temperature. After temperature correction, the concentration profiles and spectra were obtained with PCA, SIMPLISMA and the orthogonal projection approach (OPA). The same resolution methods were used on the dataset measured at constant temperature.

Effect of different preprocessing methods for principal component analysis applied to the composition of mixtures: Detection of impurities in HPLC—DAD

Abstract Principal component analysis is applied to the detection of impure and pure zones in the chromatograms and the spectra obtained by high-performance liquid chromatography coupled with diode-array detection. Transformation of the raw data is one of the most important steps in multivariate analysis techniques. Different preprocessing methods: no transformation, column centering, column standardization, selective normalization, log column centering, log row centering, log double centering, and double closure have been applied to the data. The biplots obtained for the two first principal components are discussed and particular attention is paid to the results obtained by the logarithmic transformation methods.