Process analytical technology: a critical view of the chemometricians

Pomerantsev, Alexey L.; Rodionova, Oxana Ye.

doi:10.1002/cem.2445

Cited by 99 publications

(58 citation statements)

References 100 publications

(146 reference statements)

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“…In order to demonstrate how the rPLS method performs on real data, two spectroscopic datasets with highly different levels of complexity were selected. A near‐infrared (NIR) spectroscopy dataset was selected as this is the most common spectroscopic technique in agricultural quality control and in the important areas of process analytical chemistry and process analytical technology . In addition, a nuclear magnetic resonance metabolomics dataset was selected, because metabolomics is the most important research field that makes extensive use of multivariate data analysis .…”

Section: Methodsmentioning

confidence: 99%

Recursive weighted partial least squares (rPLS): an efficient variable selection method using PLS

Rinnan

Andersson²,

Ridder³

et al. 2013

Journal of Chemometrics

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Variable selection is important in fine tuning partial least squares (PLS) regression models. This study introduces a novel variable weighting method for PLS regression where the univariate response variable y is used to guide the variable weighting in a recursive manner—the method is called recursive weighted PLS or just rPLS. The method iteratively reweights the variables using the regression coefficients calculated by PLS. The use of the regression vector to make up the weights is a reasonable idea from the fact that the weights in the regression vector ideally reflect the importance of the variables. In contrast to many other variable selection methods, the rPLS method has the advantage that only one parameter needs to be estimated: the number of latent factors used in the PLS model. The rPLS model has the fascinating output that it, under normal conditions, converges to a very limited number of variables (useful for interpretation), but it will exhibit optimal regression performance before convergence, normally including covarying neighbor variables. This study examines the properties of rPLS by application to a near‐infrared spectroscopy dataset of feed samples predicting the protein content and to a metabolomics dataset modeling a reference metabolic parameter (creatinine) from nuclear magnetic resonance spectra of human urine. Copyright © 2013 John Wiley & Sons, Ltd.

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Section: Methodsmentioning

confidence: 99%

Recursive weighted partial least squares (rPLS): an efficient variable selection method using PLS

Rinnan

Andersson²,

Ridder³

et al. 2013

Journal of Chemometrics

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“…LVMs, in fact, are powerful tools to relate highly correlated input variables (such as spectroscopic, X-ray, chromatography data) to process and product quality [5]. Several reviews on the use of LVMs as tools for product quality estimation in the pharmaceutical industry are available [33][34][35].…”

Section: Latent Variable Models In the Pharmaceutical Industrymentioning

confidence: 99%

Advanced Process Decision Making Using Multivariate Latent Variable Methods

Ottavian

Tomba²,

Barolo

2016

Methods in Pharmacology and Toxicology

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This chapter is intended to show how latent variable modeling techniques can be used to support\ud several pharmaceutical development and manufacturing activities by exploitation of historical databases\ud deriving from experiments, ongoing manufacturing processes or historical products already developed.\ud Basic theoretical concepts about latent variable modeling and latent variable model inversion are first\ud introduced. Then, some applications are reviewed to show how the pharmaceutical industry can benefit\ud from these modeling techniques to support decision-making activities in process development,\ud formulation design, process scale-up, product transfer, process control, and raw materials acceptability\ud assessment

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“…These qualitative methods can be used to monitor a bioprocess as defined by PAT . High process reproducibility and product safety can be provided by this type of process supervision .…”

Section: Bioprocess Monitoring Systemsmentioning

confidence: 99%

Sensor systems for bioprocess monitoring

Biechele

Busse

Solle

et al. 2015

Engineering in Life Sciences

173

158

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The ability to measure all process variables is of great importance in the field of bioprocess monitoring and control, and continuous, real‐time measurements are highly desired. The more complete and real‐time the measurements are, the more stable, reproducible, and efficient the process can be, leading to reproducibly high‐quality products. This additional information allows the operator to better document the entire process. The process analytical technologies initiative of the US Food and Drug Administration is strongly related to the analysis and control of biopharmaceutical processes. The aim of the initiative is to create processes, generating products of ensured quality by measuring quality‐related process variables. The quality of the product is enhanced by a deep understanding of the process, which is enabled by an effective and suitable sensor system. The aim of this review is to provide an overview of current and emerging sensors for bioprocess monitoring. Sensors directly interfaced to bioreactors for measuring important variables from the gas phase, such as oxygen and carbon dioxide concentration, are discussed, as well as sensors for the monitoring of the biomass concentration and morphology and of the changing medium composition. Furthermore, sensor systems are discussed. These involve sensors (especially biosensors) that are not implemented directly inside the bioreactor but rather are used in conjunction with sample‐taking systems such as flow injection analysis. A major focus is given to spectroscopic sensors, which are noninvasive and offer interesting options for a simultaneous analysis of various compounds. Since data handling is extremely important for this kind of sensor, chemometrics are also included. Soft sensors are discussed as technology that allows a user to incorporate more process data as it become available. Finally, the current state of disposable sensor technology is presented. These sensors are needed for the growing area of disposable and continuous biomanufacturing.

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Process analytical technology: a critical view of the chemometricians

Cited by 99 publications

References 100 publications

Recursive weighted partial least squares (rPLS): an efficient variable selection method using PLS

Recursive weighted partial least squares (rPLS): an efficient variable selection method using PLS

Advanced Process Decision Making Using Multivariate Latent Variable Methods

Sensor systems for bioprocess monitoring

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