José Manuel Amigo

ChroMATHography: Solving Chromatographic Issues with Mathematical Models and Intuitive Graphics

1.3. Objectives of This Work 4584 2. Chromatographic Data 4584 2.1. The Chromatographic Signal 4584 2.2. Common Chromatographic Artifacts 4584 2.3. A Basic Model of Chromatographic Data 4585 3. Removing Artifacts by Preprocessing 4587 3.1. Baseline Offset/Background Contribution 4587 3.1.1. Curve-Fitting 4587 3.1.2. Factor Model Approach 4588 3.1.3. Limitations and Things To Consider 4589 3.2. Retention Time Shifts across Samples 4589 3.2.1. Synchronizing Signals 4589 3.2.2. Factor Model Approach 4592 3.2.3. Limitations and Things To Consider 4593 4. Co-eluting Peaks 4593 4.1. The Importance of Multichannel Detectors To Solve Overlapping Issues 4595

Practical issues of hyperspectral imaging analysis of solid dosage forms

Hyperspectral imaging techniques have widely demonstrated their usefulness in different areas of interest in pharmaceutical research during the last decade. In particular, middle infrared, near infrared, and Raman methods have gained special relevance. This rapid increase has been promoted by the capability of hyperspectral techniques to provide robust and reliable chemical and spatial information on the distribution of components in pharmaceutical solid dosage forms. Furthermore, the valuable combination of hyperspectral imaging devices with adequate data processing techniques offers the perfect landscape for developing new methods for scanning and analyzing surfaces. Nevertheless, the instrumentation and subsequent data analysis are not exempt from issues that must be thoughtfully considered. This paper describes and discusses the main advantages and drawbacks of the measurements and data analysis of hyperspectral imaging techniques in the development of solid dosage forms.

Direct quantification and distribution assessment of major and minor components in pharmaceutical tablets by NIR-chemical imaging

Near Infrared Chemical Imaging (NIR-CI) is an attractive technique in pharmaceutical development and manufacturing, where new and more robust methods for assessment of the quality of the final dosage products are continuously demanded. The pharmaceutical manufacturing process of tablets is usually composed by several unit operations such as blending, granulation, compression, etc. Having reliable, robust and timely information about the development of the process is mandatory to assure the quality of the final product. One of the main advantages of NIR-CI is the capability of recording a great amount of spectral information in short time. To extract the relevant information from NIR-CI images, several Chemometric methods, like Partial Least Squares Regression, have been demonstrated to be powerful tools. Nevertheless, these methods require a calibration phase. Developing new methods that do not need any prior calibration would be a welcome development. In this context, we study the potential usefulness of Classical Least Squares and Multivariate Curve Resolution models to provide quantitative and spatial information of all the ingredients in a complex tablet matrix composed of five components without the development of any previous calibration model. The distribution of the analytes in the surfaces, as well as the quantitative determination of the five components is studied and tested.

Comprehensive analysis of chromatographic data by using PARAFAC2 and principal components analysis.

The most straightforward method to analyze an obtained GC-MS dataset is to integrate those peaks that can be identified by their MS profile and to perform a Principal Component Analysis (PCA). This procedure has some important drawbacks, like baseline drifts being scarcely considered or the fact that integration boundaries are not always well defined (long tails, co-eluted peaks, etc.). To improve the methodology, and therefore, the chromatographic data analysis, this work proposes the modeling of the raw dataset by using PARAFAC2 algorithm in selected areas of the GC profile and using the obtained well-resolved chromatographic profiles to develop a further PCA model. With this working method, not only the problems arising from instrumental artifacts are overcome, but also the detection of new analytes is achieved as well as better understanding of the studied dataset is obtained. As a positive consequence of using the proposed working method human time and work are saved. To exemplify this methodology the aroma profile of 36 apples being ripened were studied. The benefits of the proposed methodology (PARAFAC2+PCA) are shown in a practitioner perspective, being able to extrapolate the conclusions obtained here to other hyphenated chromatographic datasets.

Nir-chemical imaging study of acetylsalicylic acid in commercial tablets

Near Infrared Chemical Imaging (NIR-CI) is demonstrating an increasing interest in pharmaceutical research since it meets the challenging analytical needs of pharmaceutical quality and may serve as a versatile adjunct to conventional NIR spectroscopy in many fields. The direct analysis of samples by using hyperspectral imaging techniques, which provide a NIR spectrum in each pixel of the image, generates a big amount of information from one sample. Focusing the interest in pharmaceutical research, several chemometric algorithms are demonstrating their usefulness extracting the relevant information (i.e. quantitative determination of the component in one sample) in tablets with only one sample and without damaging it. In this work, a quantitative method to analyze different commercial Acetylsalicylic acid tablets is proposed by using Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) method to the hyperspectral image and without any previous calibration model. For this purpose, a large concentration range of active pharmaceutical ingredient (ASA, Acetylsalicylic acid in this work), between 82% and 12%, was covered depending on the manufacturer. MCR-ALS allowed obtaining a concentration maps for acetylsalicylic acid and therefore, consequent analysis of the ASA distribution in the tablet was developed by using the histograms of the distribution of concentration. Results certified the good distribution of ASA despite the different origins of the tablets. Moreover, the obtained values of concentration showed a very good concordance with the nominal value of ASA. As a matter of fact, the quality of the results demonstrated the useful of encompassing NIR-CI techniques with MCR-ALS and, consequently, the well development on the production of Acetylsalicylic acid tablets.

Hyperspectral image analysis. A tutorial

This tutorial aims at providing guidelines and practical tools to assist with the analysis of hyperspectral images. Topics like hyperspectral image acquisition, image pre-processing, multivariate exploratory analysis, hyperspectral image resolution, classification and final digital image processing will be exposed, and some guidelines given and discussed. Due to the broad character of current applications and the vast number of multivariate methods available, this paper has focused on an industrial chemical framework to explain, in a step-wise manner, how to develop a classification methodology to differentiate between several types of plastics by using Near infrared hyperspectral imaging and Partial Least Squares - Discriminant Analysis. Thus, the reader is guided through every single step and oriented in order to adapt those strategies to the users case.

Beer fermentation: monitoring of process parameters by FT-NIR and multivariate data analysis.

This work investigates the capability of Fourier-Transform near infrared (FT-NIR) spectroscopy to monitor and assess process parameters in beer fermentation at different operative conditions. For this purpose, the fermentation of wort with two different yeast strains and at different temperatures was monitored for nine days by FT-NIR. To correlate the collected spectra with °Brix, pH and biomass, different multivariate data methodologies were applied. Principal component analysis (PCA), partial least squares (PLS) and locally weighted regression (LWR) were used to assess the relationship between FT-NIR spectra and the abovementioned process parameters that define the beer fermentation. The accuracy and robustness of the obtained results clearly show the suitability of FT-NIR spectroscopy, combined with multivariate data analysis, to be used as a quality control tool in the beer fermentation process. FT-NIR spectroscopy, when combined with LWR, demonstrates to be a perfectly suitable quantitative method to be implemented in the production of beer.

Fast assessment of the surface distribution of API and excipients in tablets using NIR-hyperspectral imaging.

The inclusion of hyperspectral imaging systems in the manufacturing and development of pharmaceutical products is allowing a successful improvement in the quality control of solid dosage forms. The correct distribution not only of active pharmaceutical ingredient (API) but also of the rest of excipients is essential to assure the correct behavior of the tablet when ingested. This is especially relevant in tablets with low content of potent APIs, in which the prescribed intake dosage frequently corresponds to half-a-tablet. Therefore, the aim of this work is to study the surface distribution of the compounds in tablets with low API content. The proposed procedure includes the scanning of the tablet surface using near infrared hyperspectral spectroscopy in association with multivariate curve resolution (MCR) techniques to obtain selective pictures for each individual compound and to allow the fast assessment of their distribution in the measured surface. As an example, a set of commercial Lorazepam tablets (approximately 1% mass fraction of API, and four excipients) were analyzed. The results obtained show the capacity of the proposed methodology as an expedite approach to evaluate the uniformity of the contents between and within tablets. A method to estimate the homogeneity distribution of API in the two halves of the tablet is also proposed.

Plant metabolomics: Resolution and quantification of elusive peaks in liquid chromatography–mass spectrometry profiles of complex plant extracts using multi-way decomposition methods

Previous studies on LC-MS metabolomic profiling of 127 F2 Barbarea vulgaris plants derived from a cross of parental glabrous (G) and pubescent (P) type, revealed four triterpenoid saponins (hederagenin cellobioside, oleanolic acid cellobioside, epihederagenin cellobioside, and gypsogenin cellobioside) that correlated with resistance of plants against the insect herbivore, Phyllotreta nemorum. In this study, for the first time, we demonstrate the efficiency of the multi-way decomposition method PARAllel FACtor analysis 2 (PARAFAC2) for exploring complex LC-MS data. PARAFAC2 enabled automated resolution and quantification of several elusive chromatographic peaks (e.g. overlapped, elution time shifted and low s/n ratio), which could not be detected and quantified by conventional chromatographic data analysis. Raw LC-MS data of 127 F2 B. vulgaris plants were arranged in a three-way array (elution time point×mass spectra×samples), divided into 17 different chromatographic intervals and each interval were individually modeled by PARAFAC2. Three main outputs of the PARAFAC2 models described: (1) elution time profile, (2) relative abundance, and (3) pure mass spectra of the resolved peaks modeled from each interval of the chromatographic data. PARAFAC2 scores corresponding to relative abundances of the resolved peaks were extracted and further used for correlation and partial least squares (PLS) analysis. A total of 71 PARAFAC2 components (which correspond to actual peaks, baselines and tails of neighboring peaks) were modeled from 17 different chromatographic retention time intervals of the LC-MS data. In addition to four previously known saponins, correlation- and PLS-analysis resolved five unknown saponin-like compounds that were significantly correlated with insect resistance. The method also enabled a good separation between resistant and susceptible F2 plants. PARAFAC2 spectral loadings corresponding to the pure mass spectra of chromatographic peaks matched well with experimentally recorded mass spectra (correlation based similarity >95%). This enabled to extract pure mass spectra of highly overlapped and low s/n ratio peaks.

Comprehensive assessment of pine needles as bioindicators of PAHs using multivariate analysis. The importance of temporal trends

The importance of the annual and seasonal trends associated to the polycyclic aromatic hydrocarbons (PAHs) biomonitoring by pine needles are studied with a comprehensive use of univariate and multivariate analysis tools. For this purpose, four pine needle sampling campaigns (winter, spring, summer and autumn 2007) were carried out in 29 sites from Portugal. Needles from Pinus pinaster Ait. and Pinus pinea L. trees were collected from all year-classes available in each tree, corresponding to the different shoots of needles coming out every spring and the results of both species were treated separately. Annual trends of polycyclic aromatic hydrocarbons (PAHs) contamination indicate a general increase from the least to the most exposed year-classes, for all seasons. The mean values for the sum of 16PAHs ranged from 71 ± 33 ngg(-1) (dry weight - dw) for new year (2007) needles in the summer to 514 ± 317 ngg(-1) (dw) for 2-year needles (2005) in the spring for P. pinea, and between 90 ± 50 ngg(-1) (dw) for new year (2007) needles in the summer and 1212 ± 436 ngg(-1) (dw) for 3-year needles (2004) in summer for P. pinaster. The seasonal evolution shows the highest concentrations in the winter, then declining to the lowest levels in the summer and rising again from summer to autumn. Principal component analysis confirmed differences between seasons and needle year-classes, more visible for P. pinea samples. The cooler seasons have more affinity towards the lighter more abundant PAHs, as do the older needles. Differences between both pine species are also evident.