Oxana Ye. Rodionova

Process analytical technology: a critical view of the chemometricians

The role of chemometrics in process analytical technology (PAT) solutions development is presented in the review on the basis of publications from 1993 to 2011. Main areas of application, stages of implementation, instruments, and chemometric methods used for the PAT implementations are reviewed. Generally speaking, PAT is considered to be an approach applicable not only in pharmaceutical industry but also in any production area such as food industry and biotechnology. PAT is claimed to be a new flexible manufacturing concept that accounts for variability and adapts the process to fit it. Copyright

Concept and role of extreme objects in PCA/SIMCA

For the construction of a reliable decision area in the soft independent modeling by class analogy (SIMCA) method, it is necessary to analyze calibration data revealing the objects of special types such as extremes and outliers. For this purpose, a thorough statistical analysis of the scores and orthogonal distances is necessary. The distance values should be considered as any data acquired in the experiment, and their distributions are estimated by a data‐driven method, such as a method of moments or similar. The scaled chi‐squared distribution seems to be the first candidate among the others in such an assessment. This provides the possibility of constructing a two‐level decision area, with the extreme and outlier thresholds, both in case of regular data set and in the presence of outliers. We suggest the application of classical principal component analysis (PCA) with further use of enhanced robust estimators both for the scaling factor and for the number of degrees of freedom. A special diagnostic tool called extreme plot is proposed for the analyses of calibration objects. Extreme objects play an important role in data analysis. These objects are a mandatory attribute of any data set. The advocated dual data‐driven PCA/SIMCA (DD‐SIMCA) approach has demonstrated a proper performance in the analysis of simulated and real‐world data for both regular and contaminated cases. DD‐SIMCA has also been compared with robust principal component analysis, which is a fully robust method. Copyright

Noninvasive detection of counterfeited ampoules of dexamethasone using NIR with confirmation by HPLC-DAD-MS and CE-UV methods

Application of near-infrared (NIR) measurements together with chemometric data processing is widely used for counterfeit drug detection. The most difficult counterfeits to detect are the “high quality fakes”, which have the proper composition but are produced in violation of technological regulations by underground manufacturers. This study uses such forgeries and addresses important issues. The first is the possibility of applying the NIR/chemometric approach to the detection of injectable formulations of drugs (in this case dexamethasone), which are aqueous solutions with low concentration of active ingredients, directly in the closed ampoules. The second issue is the comparison of NIR/chemometric conclusions with detailed chemical analysis.

Trends in Chemometrics: Food Authentication, Microbiology, and Effects of Processing: Trends in chemometrics…

In the last decade, the use of multivariate statistical techniques developed for analytical chemistry has been adopted widely in food science and technology. Usually, chemometrics is applied when there is a large and complex dataset, in terms of sample numbers, types, and responses. The results are used for authentication of geographical origin, farming systems, or even to trace adulteration of high value-added commodities. In this article, we provide an extensive practical and pragmatic overview on the use of the main chemometrics tools in food science studies, focusing on the effects of process variables on chemical composition and on the authentication of foods based on chemical markers. Pattern recognition methods, such as principal component analysis and cluster analysis, have been used to associate the level of bioactive components with in vitro functional properties, although supervised multivariate statistical methods have been used for authentication purposes. Overall, chemometrics is a useful aid when extensive, multiple, and complex real-life problems need to be addressed in a multifactorial and holistic context. Undoubtedly, chemometrics should be used by governmental bodies and industries that need to monitor the quality of foods, raw materials, and processes when high-dimensional data are available. We have focused on practical examples and listed the pros and cons of the most used chemometric tools to help the user choose the most appropriate statistical approach for analysis of complex and multivariate data.

On the type II error in SIMCA method

A novel method for theoretical calculation of the type II (β) error in soft independent modeling by class analogy is proposed. It can be used to compare tentatively predicted and empirically observed results of classification. Such an approach can better characterize model quality and thus improve its validation. Method efficiency is demonstrated on the famous Fisher Iris dataset and on a real‐world example of quality control of packed raw materials in pharmaceutical industry. Copyright

The Influence of Fiber-Probe Accessories Application on the Results of Near-Infrared (NIR) Measurements

When several near-infrared instruments are used in a network and a common chemometric model is applied to spectral processing, comparison of the instruments is indispensable. Direct transferability often claimed by the producers should be treated with caution. It has been found experimentally that when measurements are performed with the help of a fiber optic probe, the main source of spectral discrepancy is related to probe sensitivity in contactless measurements. Here the influence of the probe-to-object distance on the acquired spectra is analyzed in detail. Special experimental setups are proposed to isolate various strongly influencing factors and to maintain stable measurement conditions. The application of an artificial standard instead of real-world objects helps to focus on the instrument/accessory characteristics.

Chemometrics in analytical chemistry—part I: history, experimental design and data analysis tools

Chemometrics has achieved major recognition and progress in the analytical chemistry field. In the first part of this tutorial, major achievements and contributions of chemometrics to some of the more important stages of the analytical process, like experimental design, sampling, and data analysis (including data pretreatment and fusion), are summarised. The tutorial is intended to give a general updated overview of the chemometrics field to further contribute to its dissemination and promotion in analytical chemistry.

Nonlinear multivariate curve resolution alternating least squares (NL‐MCR‐ALS)

Bilinearity is the basic principle of multivariate curve resolution. In this paper, we consider a case when this premise is violated. We demonstrate that the alternating least squares approach can still be used to solve the problem. The developed theory is applied to calibration of spectral data that includes the so‐called saturated peaks, which are flattened because of samples with ultrahigh absorbance. We demonstrate that in spite of serious violations of the Lambert–Beer law, the results of prediction are quite satisfactory, and the accuracy is better than in other competing methods. Copyright

Non-linear multivariate curve resolution applied to the spectrophotometric determination of cerium(III) in aqueous nitric acid solutions for process control

It is shown that the recently proposed Non-Linear Multivariate Curve Resolution (NL-MCR) method can be effectively employed to develop an accurate calibration of cerium(III) using spectrophotometry measurements of mixtures of rare earth elements in nitric acid. Spectroscopic techniques provide a unique opportunity for the in-line determination of critical concentrations rapidly and without serious risks to operating personnel and the environment. Cerium has no absorbance bands in the visual and near-infrared range. In the ultraviolet range cerium(III) has a unique large peak which overlaps with even larger peaks of nitric acid. In the case of in-line control, where conventional analytical means are limited, we encounter a peak flattening and, consequently, a Lambert–Beer law violation. Therefore, the conventional calibration methods, such as Partial Least Squares (PLS) and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS), yield useless results. Our previous attempt to overcome Beers law violation by introducing a non-linear constraint in the MCR-ALS procedure failed. Application of the NL-MCR method with specially selected transition function not only yields accurate cerium determination but also provides an opportunity to assess the unknown nitric acid concentration in new samples. It is shown that the established calibration models are stable to some extent for out-of-control cases.

Diffuse Reflectance Spectroscopy of Hidden Objects, Part I: Interpretation of the Reflection–Absorption-Scattering Fractions in Near-Infrared (NIR) Spectra of Polyethylene Films

Investigation of a sample covered by an interfering layer is required in many fields, e.g., for process control, biochemical analysis, and many other applications. This study is based on the analysis of spectra collected by near-infrared (NIR) diffuse reflectance spectroscopy. Each spectrum is a composition of a useful, target spectrum and a spectrum of an interfering layer. To recover the target spectrum, we suggest using a new phenomenological approach, which employs the multivariate curve resolution (MCR) method. In general terms, the problem is very complex. We start with a specific problem of analyzing a system, which consists of several layers of polyethylene (PE) film and underlayer samples with known spectral properties. To separate information originating from PE layers and the target, we modify the system versus both the number of the PE layers as well as the reflectance properties of the target sample. We consider that the interfering spectrum of the layer can be modeled using three components, which can be tentatively called transmission, absorption, and scattering contributions. The novelty of our approach is that we do not remove the reflectance and scattering effects from the spectra, but study them in detail aiming to use this information to recover the target spectrum.