Photonic Data Science

Research group Prof. Dr. Thomas Bocklitz

Fig 3

Graphic: IPHT
Fig 3

Head of the group

Thomas Bocklitz, University Professor Dr

Head
Professorship of Photonic Data Science
Prof. Dr. Thomas Bocklitz
Image: Prof. Dr. Thomas Bocklitz
Link to download vCard
vCard
JenTower
Leutragraben 1
07743 Jena Google Maps site planExternal link

Scientific Profile

We explore the entire data life cycle of photonic data from generation to the data analysis and to data archiving. Following a holistic approach, we investigate procedures for experiment and sample size planning as well as data pretreatment and combine these procedures with chemometric procedures, model transfer methods and artificial intelligence methods in a data pipeline. In this way, data from various photonic processes can be used for analysis, diagnostics and therapy in medicine, life science, environmental sciences and pharmacy. The data pipeline are implemented in software components and are tested directly in the applicative environment, e.g. in clinical studies. Further focal points are data fusion of different heterogeneous data sources, the simulation of different measurement procedures in order to optimize correction procedures, methods for the interpretation of analysis models and the construction of data infrastructures for different photonic measurement data, which ensure the FAIR principles.

Research Topics

  • Machine learning for photonic image data
    Fig 1

    Fig 1

    Graphic: IPHT
  • Chemometrics / machine learning for spectral data
  • Correlation of different measurement methods and data fusion

Areas of application

  • Bio-medical diagnostics using spectral measurement methods and imaging techniques
  • Extraction of higher information from photonic measurement data
  • Simulation- und data-driven correction of photonic data
  • Guarantee of FAIR principles for photonic data

Staff

  15. Vulchi, Ravi Teja

    PhD Student Professorship of Photonic Data Science
    Link to download vCard
    vCard

    JenTower
    Leutragraben 1
    07743 Jena

  16. Yogita, Yogita

    PhD student Professorship of Photonic Data Science
    Link to download vCard
    vCard

    JenTower, Room 15S01
    Leutragraben 1
    07743 Jena

Filter 107 publications

Filter publications

Highlighted authors are members of the research group.

  1. Siamese networks in Raman spectroscopy: Towards a better performance against replicate variability

    Authors
    S. Guo, T. Bocklitz
    Year of publication
    Published in:
    Talanta: the international journal of pure and applied analytical chemistry
    The power of Raman spectroscopy is largely enhanced by machine learning and chemometrics, which extract and translate the spectral features into high-level biological or clinical knowledge by constructing classical or deep learning models. The generalizability of such models, however, is often degraded due to the large variations between the training data and the data to be predicted. Model transfer showed great potential in this regard, which improved the prediction on the test data without re-building a new model from scratch. We developed a method based on Siamese neural network (SNet) and compared it with two basis models as well as two model transfer methods score movement (MS) and extensive multiplicative scattering correction (EMSC). The performance was systematically verified with a Raman spectral dataset measured from four bacterial species, each consisting of nine biological replicates. Its generalizability was further tested on a second Raman dataset from mice tissue samples. Siamese network was demonstrated to outperform the MS and EMSC, especially given large training datasets. The load on training data, however, is substantially lower than conventional networks and can be slightly reduced when variability between training and test data is properly incorporated into the loss function. Unlike MS and EMSC, more importantly, Siamese network does not require information of test data for model adjustment or data space adaptation, which makes it more advantageous in practice.
    University Bibliography Jena:
    fsu_mods_00030099External link

  2. Harnessing Machine Learning and Deep Learning Approaches for Laser-Induced Breakdown Spectroscopy Data Analysis: A Comprehensive Review

    Authors
    P. Dehbozorgi, L. Duponchel, V. Motto-Ros, T. Bocklitz
    Year of publication
    Published in:
    Analysis & Sensing
    Laser-induced breakdown spectroscopy (LIBS) is a rapid, accurate technique for material analysis, offering real-time, minimally destructive, and in situ detection capabilities with broad application potential. LIBS extends its applications across various fields, from geology to biomedicine. However, barriers like matrix effects, reproducibility, self-absorption, and spectral noise often restrict the proper interpretation of the spectra. This review paper examines literature from 2015 to 2025, focusing on the evolution of machine learning (ML) and deep learning (DL) techniques, in LIBS analysis. It evaluates the advancement of these techniques, assessing both the qualitative and quantitative performance of LIBS analysis. These observations support the complementary roles of ML and DL methodologies. ML captures general patterns, while DL, through convolutional neural networks (CNNs), excels at identifying high-level features. This literature review reveals that no single ML or DL tool consistently provides optimal solutions for LIBS applications. The analysis pipeline needs to be tailored based on the LIBS data and the goal of the study. Designing such a framework requires the incorporation of preprocessing techniques to enhance the quality of raw signals. This step should then be followed by integrating the data into predictive models, whether ML or DL, to accomplish tasks like classification or concentration prediction.
    University Bibliography Jena:
    fsu_mods_00027407External link

  3. Blood cancer differentiation based on IR spectroscopy and chemometrics

    Authors
    L. Xie, S. Guo, T. Liu, X. Tang, R. Ji, X. Shen, Y. Xu, L. Chen, S. Wang, T. Bocklitz
    Year of publication
    Published in:
    Computer methods and programs in biomedicine
    Background and Objective White blood cells (WBCs) and their subpopulations play critical roles in detecting blood cancers due to their distinct biological and biochemical characteristics. Infrared (IR) spectroscopy offers a rapid, label-free, and non-destructive approach to probe molecular composition, making it a promising tool for biomedical diagnostics. The objective of this proof-of-principle study is to investigate the possibility of IR spectroscopy combined with chemometrics to differentiate leukemia from lymphoma, and to assess the capability of whole WBCs and their subpopulations in distinguishing the two diseases. Methods We based our study on 21 pediatric patients including 11 leukemia and 10 lymphoma cases, with in total 86,016 IR spectra measured from whole WBCs and the subpopulations. Data pipeline was established, including steps of spectral preprocessing, classification, and data fusion. Particularly, data fusion was implemented via low-, middle-, and high-level strategies, with the aim of combining spectra from different cell types and investigating their capability of differentiating the two blood cancers. Results The classification, both with and without data fusion, was benchmarked via the patient-wise cross-validation. A balanced accuracy of 80.0% was achieved based on IR spectra of whole WBCs. Further improvement was observed when combining whole WBCs and its subpopulations, with the best performance of 90.0% from combining whole WBCs and granulocytes with high-level data fusion strategy. The performance was observed consistent for both linear and nonlinear classifications based on linear discriminant analysis (LDA) and support vector machine (SVM), respectively. Conclusions The results indicate the promising potential of IR spectroscopy of blood samples to distinguish leukemia and lymphoma with the help of chemometric approaches. Further, WBC subpopulations, particularly granulocytes, were proven to contain complementary information to whole WBCs for differentiating leukemia from lymphoma. This provides critical insights for biomedical practice in blood cancer diagnostics.
    University Bibliography Jena:
    fsu_mods_00036197External link

  4. Toward cancer theranostics via multimodal nonlinear endomicroscopy, microspectroscopy and femtosecond laser ablation

    Author
    M. Calvarese
    Year of publication
    This thesis advances nonlinear multimodal imaging techniques, including Coherent Anti-Stokes Raman Scattering (CARS), Two-Photon Excited Fluorescence (TPEF), Second Harmonic Generation (SHG), and broadband Coherent Raman Scattering (BCARS), toward clinical applications to cancer diagnosis and treatment. A multimodal endomicroscopic platform integrating CARS, TPEF, SHG, indocyanine green (ICG) fluorescence, and femtosecond laser ablation was developed and validated for real-time cancer detection and treatment. The system enables a “seek-and-treat” approach combining imaging, AI-based tissue analysis, and selective laser ablation for image-guided surgery. Preclinical studies on head and neck cancer tissue demonstrated high-quality imaging and robust performance comparable to commercial systems. AI-based analysis achieved 88% sensitivity in distinguishing tissue requiring resection from healthy tissue, while automated image-guided laser ablation demonstrated precise selective tissue removal. The thesis also investigates broadband CARS for enhanced chemical specificity and spectral histopathology. BCARS systems were developed and applied to biological samples, showing strong potential for molecular characterization, tissue classification, and disease detection. Preliminary results further demonstrate the feasibility of integrating BCARS into endomicroscopy, supporting future real-time intraoperative spectral imaging. Overall, this work highlights the potential of advanced nonlinear imaging for image-guided cancer diagnosis and treatment.
    University Bibliography Jena:
    fsu_mods_00036802External link

  5. Robust Spectroscopic Analysis Through Image-Based Spectral Representation and Deep Learning Techniques

    Authors
    A. Mokari, O. Ryabchykov, T. Bocklitz
    Year of publication
    Published in:
    Journal of Raman spectroscopy
    Variability in instrument calibration, both between different devices and over time, remains a significant obstacle to Raman spectroscopy. Even minor shifts in wavenumber can substantially reduce classification accuracy. Traditional spectral models struggle to remain robust under such conditions, particularly due to the one-dimensional (1D) nature of spectral inputs and the absence of pretrained deep learning architectures for Raman spectra. In this study, we systematically investigate the robustness of a previously introduced spider plot–based spectral representation specifically with respect to wavenumber calibration drift in test data. In this approach, each Raman spectrum is converted into a radial spider plot in which the angle of each sector represents the wavenumber, and the color indicates the normalized intensity, enabling the use of powerful image-based deep learning models. We employed EfficientNetB7, a pretrained convolutional neural network (CNN), to classify transformed spectra. Using a dataset of 5420 spectra from six bacterial species, we introduced synthetic wavenumber shifts of ±9 cm −¹ to simulate realistic calibration drift in the test data. The traditional method, which combined principal component analysis with linear discriminant analysis (PCA-LDA), showed a significant drop in balanced accuracy from 0.90 to 0.77 when shifts were present. By contrast, our spider plot–based approach retained strong performance, achieving balanced accuracy scores of 0.88 without shifts and 0.83 with shifts. Although no spectral augmentation was done in the training of either classification workflow, we attribute this robustness to the rotational consistency of spider plots under spectral shift and the tolerance to image rotation in CNNs, which are pretrained on large, augmented image datasets. Our findings demonstrate the advantage of using image-based methods to mitigate the effects of calibration variability in Raman spectroscopy.
    University Bibliography Jena:
    fsu_mods_00037697External link

  7. Denoising and Baseline Correction of Low-Scan FTIR Spectra: a Benchmark of Deep Learning Models Against Traditional Signal Processing

    Authors
    A. Mokari, S. Raghunathan, A. Shydliukh, O. Ryabchykov, C. Krafft, T. Bocklitz
    Year of publication
    Published in:
    Bioengineering
    High-quality Fourier Transform Infrared (FTIR) imaging usually needs extensive signal averaging to reduce noise and drift, which severely limits clinical speed. Deep learning can accelerate imaging by reconstructing spectra from rapid, single-scan inputs. However, separating noise and baseline drift simultaneously without ground truth is an ill-posed inverse problem. Standard black-box architectures often rely on statistical approximations that introduce spectral hallucinations or fail to generalize to unstable atmospheric conditions. To solve these issues, we propose a physics-informed cascade Unet that separates denoising and baseline correction tasks using a new, deterministic Physics Bridge. This architecture forces the network to separate random noise from chemical signals using an embedded SNIP layer to enforce spectroscopic constraints instead of learning statistical approximations. We benchmarked this approach against a standard single Unet and a traditional Savitzky–Golay smoothing followed by SNIP baseline correction workflow. We used a dataset of human hypopharyngeal carcinoma cells (FaDu). The cascade model outperformed all other methods, achieving a 51.3% reduction in RMSE compared to raw single-scan inputs, surpassing both the single Unet (40.2%) and the traditional workflow (33.7%). Peak-aware metrics show that the cascade architecture eliminates spectral hallucinations found in standard deep learning. It also preserves peak intensity with much higher fidelity than traditional smoothing. These results show that the cascade Unet is a robust solution for diagnostic-grade FTIR imaging. It enables imaging speeds 32 times faster than current methods.
    University Bibliography Jena:
    fsu_mods_00035379External link

  8. Systematic investigation of preprocessing pipeline for MALDI data

    Authors
    M. Adhikari, O. Ryabchykov, S. Guo, T. Bocklitz
    Year of publication
    Published in:
    Results in Chemistry
    Background Matrix-assisted laser desorption/ ionization Mass Spectrometry (MALDI-MS) is a powerful tool to detect and characterize biomolecules, making it particularly useful in different fields and applications such as proteomics, clinical diagnostics, and biomarker discovery. MALDI data is commonly contaminated by the artefacts originated from both chemical and electrical noise. Data preprocessing is hence important to remove these artefacts and improve the accuracy and reliability of the subsequent (quantitative and qualitative) analysis. A systematic investigation of different preprocessing steps is necessary to establish an effective preprocessing pipeline. Results In this study, we systematically investigated the different steps including interpolation, smoothing, baseline correction, peak alignment, and peak binning, along with normalization to establish a preprocessing pipeline of MALDI spectral data. The performance of the preprocessing steps and pipeline was benchmarked by the balanced accuracy of differentiating hepatocellular carcinoma (HCC) and healthy (normal) based on MALDI spectral data of liver tissue samples. The established preprocessing pipeline improved the balanced accuracy from 61.3% to 77.6% under the patient-level cross-validation, and from 92.9% to 94.7% under spectral-level cross-validation. Significance Our findings demonstrated that the classification performance can be greatly affected by the quality of MALDI data, which can be improved by preprocessing steps. The large improvement from the patient-level validations after preprocessing demonstrated well a satisfying performance of the classification against patient-to-patient variability with the help of our preprocessing pipeline. This study will potentially benefit the MS community.
    University Bibliography Jena:
    fsu_mods_00034772External link

  9. A comparative study of robustness to noise and interpretability in U-Net-based denoising of Raman spectra

    Authors
    A. Mokari, S. Eiserloh, O. Ryabchykov, U. Neugebauer, T. Bocklitz
    Year of publication
    Published in:
    Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
    University Bibliography Jena:
    fsu_mods_00026011External link

  10. Graph Neural Network in Raman Spectroscopy to Leverage the Performance and Interpretability of the Classification

    Authors
    S. Guo, S. Frempong, M. Salbreiter, A. Wagenhaus, P. Rösch, J. Popp, T. Bocklitz
    Year of publication
    Published in:
    Chemistry - Methods: new approaches to solving problems in chemistry
    Thanks to its noninvasive, nondestructive, and label-free features, Raman spectroscopy has gained intensive attention in biomedical applications over the last two decades. In combination with machine learning techniques, the rich molecular fingerprints contained in Raman spectra bring huge possibilities to early diagnosis and deeper understanding of biomedical processes. To bring it to real-world applications, however, remains a challenge mainly because spectral variations of interest are easily overwhelmed by replicate-to-replicate, patient-to-patient, and device-to-device variations. Machine learning models that are robust to these variations are in definite need. To fill this gap, we developed an approach based on graph neural networks (GNNs), which represent Raman spectra with graphs and achieve classification at the graph level. The method was tested on four Raman datasets, three from single-cell bacteria of four species measured on three devices, and one from oil of eight types. The GNN demonstrated robustness to unwanted variations and achieved promising across-device transferability, with significantly improved balanced accuracy compared to both linear discriminant analysis (LDA) and RamanNet. In addition, graph representation not only brings intrinsic advantage of model interpretation but also provides a new dimension that supports better spectral distinguishment. All the results and features endorsed the proposed method to highly benefit Raman-based biomedical applications.
    University Bibliography Jena:
    fsu_mods_00036113External link

  11. Multimodal nonlinear optical microscopy image denoising with physics-inspired deep learning

    Authors
    Y. Yogita, E. Corbetta, T. Meyer-Zedler, M. Schmitt, O. Guntinas–Lichius, J. Popp, T. Bocklitz
    Year of publication
    Published in:
    JPhys Photonics
    Multimodal imaging has significantly transformed biomedical and scientific research, providing profound insights into the study of biological samples. However, imaging modalities often encounter inherent noise and artifacts owing to the statistical nature of photon detection, which compromises image quality and impedes correct analysis. Traditional methods lack scalability and exhibit suboptimal performance in denoising complex structures. In recent years, deep learning (DL) has made substantial progress in image denoising. However, the training of DL models requires extensive datasets and may yield inaccurate results when the data are limited, posing challenges in areas such as medical diagnostics. To address the challenges of standard DL methods, we propose a physics-inspired DL model, specifically a modified incSRCNN with a physics-inspired loss function, designed to enhance denoising performance with limited datasets. Our method outperforms the conventional incSRCNN model, representing a significant advancement in denoising capabilities without the need for large datasets. The proposed methodology not only improves the quality of image denoising but also reduces the dependence on extensive datasets, making it particularly valuable in biomedical applications and diagnostics, where large data collection is challenging or constrained.
    University Bibliography Jena:
    fsu_mods_00037692External link

  12. Label-Free Differentiation of Antimicrobial Resistance Groups Using Raman Spectroscopy

    Authors
    A. Pistiki, O. Ryabchykov, A. Wagenhaus, T. Bocklitz, S. Deinhardt-Emmer, B. Löffler, P. Rösch, J. Popp
    Year of publication
    Published in:
    Analytical chemistry
    Increasing antimicrobial resistance (AMR) has developed into an enormous health burden. Here, a systematic investigation was conducted to evaluate the discriminative performance of Raman spectroscopy between different resistance classes (Susceptible, ESBL, CRE, VRE, VSE) in common clinical isolates (Escherichia coli, Klebsiella pneumoniae, Klebsiella oxytoca, Citrobacter freundii, Acinetobacter baumanii, Enterococcus faecium). Two different Raman spectroscopic methods (UVRR in bulk and 785 nm excitation directly on the Petri dish) and four different machine learning algorithms (PCA-LDA, PLS-DA, PCA-SVM, PCA-RF) were tested aiming the application of a decision-tree using a 3-step approach composing of species classification, differentiation of susceptible from resistant strains within the species and differentiation of ESBL and CRE as AMR subclasses within the class of antibiotic-resistant strains. In species classification, the two Raman methods yield similar results in all applied models. When attempting the differentiation of susceptible vs resistant strains in the intraspecies level, 785 nm overall outperformed UVRR and PCA-SVM and PLS-DA provided higher discriminative power compared to PCA-LDA and PCA-RF. For the discrimination of ESBL vs CRE isolates UVRR was not suitable as a method and 785 nm excitation provided correct identification of all 9 strains when using PCA-SVM and PLS-DA, confirming stability over replicate-to-replicate variations. Raman spectra from 785 nm excitation directly on the Petri dish combined with PCA-SVM and PLS-DA are suitable for diagnostic application of Raman spectroscopy in hospital settings. These results are the first step of a long journey in the development of Raman spectroscopy for microbiological documentation and extraction of AMR-related information in infectious diseases.
    University Bibliography Jena:
    fsu_mods_00034840External link
Pagination Page 1