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Raman spectroscopy-based identification of toxoid vaccine products

Title data

Silge, Anja ; Bocklitz, Thomas ; Becker, Bjoern ; Matheis, Walter ; Popp, Jürgen ; Bekeredjian-Ding, Isabelle:
Raman spectroscopy-based identification of toxoid vaccine products.
In: npj Vaccines. Vol. 3 (2018) Issue 1 . - 50.
ISSN 2059-0105

Official URL: Volltext

Abstract in another language

Vaccines are complex biomedicines. Manufacturing is time consuming and requires a high level of quality control (QC) to guarantee consistent safety and potency. An increasing global demand has led to the need to reduce time and cost of manufacturing. The evolving concepts for QC and the upcoming threat of falsification of biomedicines define a new need for methods that allow the fast and reliable identification of vaccines. Raman spectroscopy is a non-destructive technology already established in QC of classical medicines. We hypothesized that Raman spectroscopy could be used for identification and differentiation of vaccine products. Raman maps obtained from air-dried samples of combination vaccines containing antigens from tetanus, diphtheria and pertussis (DTaP vaccines) were summarized to compile product-specific Raman signatures. Sources of technical variance were emphasized to evaluate the robustness and sensitivity in downstream data analysis. The data management approach corrects for spatial inhomogeneities in the dried sample while offering a proper representation of the original samples inherent chemical signature. Reproducibility of the identification was validated by a leave-one-replicate-out cross-validation. The results highlighted the high specificity and sensitivity of Raman measurements in identifying DTaP vaccine products. The results pave the way for further exploitation of the Raman technology for identification of vaccines in batch release and cases of suspected falsification.

Further data

Item Type: Article in a journal
Refereed: Yes
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz
Result of work at the UBT: No
DDC Subjects: 500 Science > 530 Physics
Date Deposited: 12 May 2023 10:43
Last Modified: 12 May 2023 10:43