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- ItemFLIM data analysis based on Laguerre polynomial decomposition and machine-learning(Bellingham, Wash. : SPIE, 2021) Guo, Shuxia; Silge, Anja; Bae, Hyeonsoo; Tolstik, Tatiana; Meyer, Tobias; Matziolis, Georg; Schmitt, Michael; Popp, Jürgen; Bocklitz, ThomasSignificance: The potential of fluorescence lifetime imaging microscopy (FLIM) is recently being recognized, especially in biological studies. However, FLIM does not directly measure the lifetimes, rather it records the fluorescence decay traces. The lifetimes and/or abundances have to be estimated from these traces during the phase of data processing. To precisely estimate these parameters is challenging and requires a well-designed computer program. Conventionally employed methods, which are based on curve fitting, are computationally expensive and limited in performance especially for highly noisy FLIM data. The graphical analysis, while free of fit, requires calibration samples for a quantitative analysis. Aim: We propose to extract the lifetimes and abundances directly from the decay traces through machine learning (ML). Approach: The ML-based approach was verified with simulated testing data in which the lifetimes and abundances were known exactly. Thereafter, we compared its performance with the commercial software SPCImage based on datasets measured from biological samples on a time-correlated single photon counting system. We reconstructed the decay traces using the lifetime and abundance values estimated by ML and SPCImage methods and utilized the root-mean-squared-error (RMSE) as marker. Results: The RMSE, which represents the difference between the reconstructed and measured decay traces, was observed to be lower for ML than for SPCImage. In addition, we could demonstrate with a three-component analysis the high potential and flexibility of the ML method to deal with more than two lifetime components.
- ItemRaman spectroscopy-based identification of toxoid vaccine products(Berlin : Nature Publishing, 2018) Silge, Anja; Bocklitz, Thomas W.; Becker, Bjoern; Matheis, Walter; Popp, Jürgen; Bekeredjian-Ding, IsabelleVaccines 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.