PAT Applications of NIR Spectroscopy in the Pharmaceutical Industry

Sacré, Pierre-Yves; Bleye, Charlotte De; Hubert, Philippe; Ziémons, Eric

doi:10.1002/9781119636489.ch4

Cited by 6 publications

(4 citation statements)

References 118 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the existing spectroscopic techniques, near‐infrared (NIR) spectroscopy (NIRS) is a frequently used tool in the 780–2500 nm spectral range, entrenched for analyzing compounds in various applications like food (Le, 2020; Porep et al, 2015), pharmaceutical (Sacré et al, 2021), biotech (Raghavachari, 2020) and agriculture sector (Vincent & Dardenne, 2021). In the biopharmaceutical industry, NIRS has been extensively employed for analyzing analytes such as glucose, acetate, lactate, glutamate, methanol, ammonium, and clavulanic acid.…”

Section: Introductionmentioning

confidence: 99%

NIR spectroscopy—CNN‐enabled chemometrics for multianalyte monitoring in microbial fermentation

Banerjee,

Mandal,

Jesubalan

et al. 2024

Biotech & Bioengineering

View full text Add to dashboard Cite

As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near‐infrared (NIR) spectroscopy, are finding increasing use for real‐time quantitative analysis. Yet detection of multiple low‐concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module‐based two‐dimensional (2D) CNN approach (I‐CNN) for calibrating multiple analytes using NIR spectral data. The I‐CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I‐CNN model result statistics depicted an average R2 values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I‐CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real‐time process monitoring and comparison with offline analytics. The proposed I‐CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy.

show abstract

Section: Introductionmentioning

confidence: 99%

NIR spectroscopy—CNN‐enabled chemometrics for multianalyte monitoring in microbial fermentation

Banerjee,

Mandal,

Jesubalan

et al. 2024

Biotech & Bioengineering

View full text Add to dashboard Cite

show abstract

“…However, to date, measurements have been conducted off-line, which involves removing the sample from the process flow prior to its analysis. To facilitate high throughput and streamlined production, all printlets in a single batch should instead be analysed immediately following production, using in-line technologies that have been integrated into the manufacture workflow ( Harms et al, 2019 ; Melocchi et al, 2021 ; Sacré et al, 2021 ). In this way, printlets would not need to be diverted from the process stream, enabling real-time release testing at the point-of-care.…”

Section: Introductionmentioning

confidence: 99%

A case study on decentralized manufacturing of 3D printed medicines

Seoane-Viaño,

Xu,

Ong

et al. 2023

International Journal of Pharmaceutics: X

View full text Add to dashboard Cite

“…Apart from its contributions in the agricultural and food industries, NIRS has also found many practical applications in other industries such as medicine, forensic science, and pharmaceuticals ( Heise, 2021 ; Sacré et al, 2021 ; Chen et al, 2022 ). Several authors have reported the application of NIRS for predicting the quality parameters of cassava and its products ( Sanchez et al, 2014 ; Fu et al, 2017 ; Ikeogu et al, 2017 ; 2019 ; Su and Sun, 2017 ; Alamu et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Prediction of functional characteristics of gari (cassava flakes) using near-infrared reflectance spectrometry

et al. 2023

View full text Add to dashboard Cite

Gari is a creamy, granular flour obtained from roasting fermented cassava mash. Its preparation involves several unit operations, including fermentation, which is essential in gari production. Fermentation brings about specific biochemical changes in cassava starch due to the actions of lactic acid bacteria. Consequently, it gives rise to organic acids and a significant reduction in the pH. Consumer preferences for gari are influenced by these changes and impact specific functional characteristics, which are often linked to cassava genotypes. Measurement of these functional characteristics is time-consuming and expensive. Therefore, this study aimed to develop high-throughput and less expensive prediction models for water absorption capacity, swelling power, bulk density, and dispersibility using Near-Infrared Reflectance Spectroscopy (NIRS). Gari was produced from 63 cassava genotypes using the standard method developed in the RTB foods project. The prediction model was developed by dividing the gari samples into two sets of 48 samples for calibration and 15 samples as the validation set. The gari samples were transferred into a ring cell cup and scanned on the NIRS machine within the Vis-NIR range of 400–2,498 nm wavelength, though only the NIR range of 800–2,400 nm was used to build the model. Calibration models were developed using partial least regression algorithms after spectra pre-processing. Also, the gari samples were analysed in the laboratory for their functional properties to generate reference data. Results showed an excellent coefficient of determination in calibrations (R2Cal) of 0.99, 0.97, 0.97, and 0.89 for bulk density, swelling power, dispersibility, and water absorption capacity, respectively. Also, the performances of the prediction models were tested using an independent set of 15 gari samples. A good prediction coefficient (R2 pred) and low standard error of prediction (SEP) was obtained as follows: Bulk density (0.98), Swelling power (0.93), WAC (0.68), Dispersibility (0.65), and solubility index (0.62), respectively. Therefore, NIRS prediction models in this study could provide a rapid screening tool for cassava breeding programs and food scientists to determine the food quality of cassava granular products (Gari).

show abstract

PAT Applications of NIR Spectroscopy in the Pharmaceutical Industry

Cited by 6 publications

References 118 publications

NIR spectroscopy—CNN‐enabled chemometrics for multianalyte monitoring in microbial fermentation

NIR spectroscopy—CNN‐enabled chemometrics for multianalyte monitoring in microbial fermentation

A case study on decentralized manufacturing of 3D printed medicines

Prediction of functional characteristics of gari (cassava flakes) using near-infrared reflectance spectrometry

Contact Info

Product

Resources

About