Forensic Mass Spectrometry Imaging

BMRC - Mass Spectrometry Imaging

• Dissecting the Suitability of DESI-MSI for Molecular Fingerprinting. Krishna R, Millard G, Bradshaw R, Cole LM, Francese S. Anal Chem. 2026 doi: 10.1021/acs.analchem.5c08143.

• A multimodal mass spectrometry imaging workflow for ballpoint pen ink analysis and "forgery" detection. Tibljas V, Francese S, Da Costa Abreu M, Bradshaw R. Analyst. 2025;  doi: 10.1039/d5an00217f.

• A multi-modal mass spectrometry approach for the detection and mapping of date rape drugs in fingermarks. R. Krishna, K. Hamer, S. Bleay, Bradshaw, L. Cole, J. Langridge, E. Claude, J. Bucek, S. Francese, 2025. doi: https://doi.org/10.1039/D5AN00328H

• Multimodal analytical approach applied to whisky labels for authenticity determination. Tibljas V, Francese S, Clark J, Goodall I, Birch F, Bradshaw R. 2025; doi: https://doi.org/10.1016/j.forc.2025.100709. 

• Statistical Modelling Investigation of MALDI-MSI-Based Approaches for Document Examination. Kjeldbjerg Lassen J, Bradshaw R, Villesen P, Francese S. Molecules. 2023; doi: 10.3390/molecules28135207.

• Francese S. and Heaton C. Emerging Technologies: Use of Matrix Assisted Laser Desorption Ionisation Mass Spectrometry for the analysis of fingermark and blood evidence IN: Applications of mass spectrometry for the provision of forensic intelligence - state of the art and perspectives Francese S and Bleay SM (eds) 2023

• Detection and mapping of haemoglobin variants in blood fingermarks by MALDI MS for suspect "profiling". Heaton C, Witt M, Cole L, Eyre J, Tazzyman S, McColm R, Francese S.Analyst. 2021; doi: 10.1039/d1an00578b.

• "MALDI-CSI": A proposed method for the tandem detection of human blood and DNA typing from enhanced fingermarks.  Kennedy K, Bengiat R, Heaton C, Herman Y, Oz C, Levin Elad M., Cole L, Francese S, 2021 https://doi.org/10.1016/j.forsciint.2021.11

• R. Bradshaw and S. Francese - MALDI-MSI in the Forensic arena, IN: MALDI Mass Spectrometry Imaging: From Fundamentals to Spatial Omics 978-1-83916-520-7 T Porta (Ed) 2021

• Application of MALDI MS imaging after sequential processing of latent fingermarks Bradshaw R, Wilson G, Denison N, Francese S, 2021. doi: https://doi.org/10.1016/j.forsciint.2020.110643

• Implementation of MALDI MS profiling and imaging methods for the analysis of real crime scene fingermarks. Bradshaw R, Denison N, Francese S, 2017; doi: https://doi.org/10.1039/C7AN00218A

• Optimization of sample preparation and instrumental parameters for the rapid analysis of drugs of abuse in hair samples by MALDI-MS/MS imaging. Flinders B., Beasley E., Verlaan R.M., Cuypers E., Francese S, Bassindale T, Clench MR, Heeren RMA, 2017. doi: https://doi.org/10.1007/s13361-017-1766-0

Biomedical and Pharmaceutical Imaging 

BMRC - Centre for Mass Spectrometry Imaging

• Narciso Couto, Jillian R.A. Newton, Cristina Russo, Esther Karunakaran, Brahim Achour, Zubida M. Al-Majdoub, James Sidaway, Amin Rostami-Hodjegan, Malcolm R. Clench, Jill Barber, Label-Free Quantitative Proteomics and Substrate-Based Mass Spectrometry Imaging of Xenobiotic Metabolizing Enzymes in Ex Vivo Human Skin and a Human Living Skin Equivalent Model, Drug Metabolism and Disposition, Volume 49, Issue 1, 2021, Pages 39-52, ISSN 0090-9556, https://doi.org/10.1124/dmd.120.000168.

• Roberto Angelini, Eylan Yutuc, Mark F. Wyatt, Jillian Newton, Fowzi A. Yusuf, Lauren Griffiths, Benjamin J. Cooze, Dana El Assad, Gilles Frache, Wei Rao, Luke B. Allen, Zeljka Korade, Thu T. A. Nguyen, Rathnayake A. C. Rathnayake, Stephanie M. Cologna, Owain W. Howell, Malcolm R. Clench, Yuqin Wang, William J. Griffiths; Visualizing Cholesterol in the Brain by On-Tissue Derivatization and Quantitative Mass Spectrometry Imaging. Anal. Chem. 23 March 2021; 93 (11): 4932–4943. https://doi.org/10.1021/acs.analchem.0c05399
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MicroCT and 3D Imaging

BMRC - SMIC MicroCT Facility 

• Doolan, O., Lewsey, M.G., Peirats-Llobet, M., Bricklebank, N. and Aberdein, N. (2024). Micro computed tomography analysis of barley during the first 24 hours of germination. Plant Methods, 20(1), 142.

• Williamson, A., da Silva, A., do Carmo, J., Le Maitre, C., Hall, J.E. and Aberdein, N. (2023). Impact of leptin deficiency on male tibia and vertebral body 3D bone architecture independent of changes in body weight. Physiological Reports, 11(19), 1–18. DOI: 10.14814/phy2.15832.

Papers currently under review:

• Dascombe, L., Chorlton, D., Nichol, T., Le Maitre, C. and Aberdein, N. (2026). Translating In-silico micro-computed tomography bone scans into a 3D printable bone model. Under review at Frontiers in Bioengineering and Biotechnology.

• Luo, J., Liu, L., Yang, X., Doolan, O., Lyon, E., Griffi, J., Shoesmith, J., Wu, W., Hamilton, R., Nuter, M., Katay-Fodor, M., Bricklebank, N., Aberdein, N., Tucker, M. and McKim, S.M. (2026). Control of grain dimensions via ALLENE OXIDE CYCLASE. Under review at New Phytologist. 
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Materials Characterisation and Engineering 

MERI - Microscopy and Material Analysis

• Grain size enhancement and grain boundary thickness reduction in low-carbon steel at sub-recrystalline temperatures via ultra-low-frequency electrical pulse-assisted tensile deformation. DOI: 10.1002/mawe.70113.

AI Hyperspectral Imaging and Food Quality

Research Activities - Computational Imaging 

• Ilo, B., Rippon, D., Singh, Y., Shenfield, A. and Zhang, H. (2025). Real-time rice milling morphology detection using hybrid framework of YOLOv8 instance segmentation and oriented bounding boxes. Electronics, 14(18), 3691.

•Ilo, B., Badjona, A., Singh, Y., Shenfield, A. and Zhang, H. (2025). Artificial Intelligence in Rice Quality and Milling: Technologies, Applications, and Future Prospects. Processes, 13(11), 3731.

•Rashvand, M., Paterna, G., Laveglia, S., Zhang, H., Shenfield, A., Gioia, T., et al. (2025). Quality detection of common beans flour using hyperspectral imaging technology: Potential of machine learning and deep learning. Journal of Food Composition and Analysis, 142, 107424.

•Nikzadfar, M., Rashvand, M., Zhang, H., Shenfield, A., Genovese, F., Altieri, G., et al. (2024). Hyperspectral imaging aiding artificial intelligence: A reliable approach for food qualification and safety. Applied Sciences, 14(21), 9821. 
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Medical Imaging and Digital Pathology

Research Activities - AI and Image Analysis

• Shiner, A., Kiss, A., Saednia, K., Jerzak, K.J., Gandhi, S., Lu, F.I., et al. (2023). Predicting patterns of distant metastasis in breast cancer patients following local regional therapy using machine learning. Genes, 14(9), 1768.

• Kasturi, S., Tran, W.T. and Shenfield, A. (2022). Accurate nuclei segmentation in breast cancer tumour biopsies. In 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, pp. 1–8.

• Lagree, A., Shiner, A., Alera, M.A., Fleshner, L., Law, E., Law, B., et al. (2021). Assessment of digital pathology imaging biomarkers associated with breast cancer histologic grade. Current Oncology, 28(6), 4298–4316.

• Lagree, A., Mohebpour, M., Meti, N., Saednia, K., Lu, F.I., Slodkowska, E., et al. (2021). A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks. Scientific Reports, 11(1), 8025.
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Clinical Imaging and Infrared Thermography

Clinical and Applied Health Imaging 

• Childs, C., Nwaizu, H., Bullivant, E., Willmott, J., Davies, M., Ousey, K., Soltani, H. and Jacques, R. (2023). ‘Cutaneous perfusion dynamics of the lower abdomen in healthy normal weight, overweight and obese women: Methods development using infrared thermography with applications for future wound management after caesarean section’. International Journal of Environmental Research and Public Health, 20, 5100. DOI: 10.3390/ijerph20065100. 

• Childs, C., Elliott, J., Khatab, K., Hampshaw, S., Fowler-Davis, S., Willmott, J.R. and Ali, A. (2020). ‘Thermal sensation in older people with and without dementia living in residential care: New assessment approaches to thermal comfort using infrared thermography’. International Journal of Environmental Research and Public Health, 17, 6932. DOI: 10.3390/ijerph17186932.

• Childs, C., Nwaizu, H., Voloaca, O. and Shenfield, A. (2023). ‘Segmentation agreement and AI-based feature extraction of cutaneous infrared images of the obese abdomen after caesarean section: Results from a single training session’. Applied Sciences, 13, 3992. DOI: 10.3390/app13063992. 

• Childs, C., Wright, N., Willmott, J., Davies, M., Kilner, K., Ousey, K., Soltani, H., Madhuvrata, P. and Stephenson, J. (2019). ‘The surgical wound in infrared: Thermographic profiles and early-stage test accuracy to predict surgical site infection in obese women during the first 30 days after caesarean section’. Antimicrobial Resistance and Infection Control, 8, 7. DOI: 10.1186/s13756-018-0461-7.

Other Computational Imaging Applications

Research Activities - Cross Disciplinary Imaging

• Haverson, K., Smith, R., Shenfield, A., Fortulan, R., Ćwiok, M., Dominik, W., et al. (2024). A ResNeXt50-based convolution neural network for nuclear reaction classification in an active target TPC detector. Il Nuovo Cimento, 48(1).

• Kasturi, S., Shenfield, A., Roast, C., Page, D.L. and Broome, A. (2023). Object Detection in Heritage Archives Using a Human-in-Loop Concept. In UK Workshop on Computational Intelligence, pp. 170–181.

PhD Projects

• Advancing the Molecular Fingerprint Capabilities within Drug Facilitated Sexual Assaults - PhD student Rohith Krishna 2024-2027 - Co-funded by Sheffield Hallam University Transforming Lives Collaborative studentship scheme and Waters Corporation.