We are the Laboratory for Bioanalytical Spectroscopy, and the research team leader is Roy Goodacre.
We work within the Centre for Metabolomics Research.
We are a team of researchers and details of our multidisciplinary group can be found under the members tab. We are based in the Department of Biochemistry, Cell and Systems Biology, which is within the Institute of Systems, Molecular and Integrative Biology, at the University of Liverpool.
Our research interests are broadly within analytical biotechnology, including disease diagnostics, detection, imaging and food security, as well as systems and synthetic biology. We are excited to work at the trisection of analytical chemistry, biology and computation.
Below is an infographic summarising our research. Below this we provide more details on our main research activities.
Metabolomics in Health, Ageing and Disease
Our metabolomics research explores how small-molecule metabolic profiles reflect processes in health, ageing, and disease. Using mass spectrometry combined with advanced chemometric analysis, we have identified biomarkers associated with mammalian physiological states, age-related changes, as well as pathology. Our studies examine how metabolic pathways shift in conditions such as infectious disease, metabolic disorders, and chronic illness, while also tracking signatures linked to ageing and biological variation over time. Machine learning and multivariate statistics are applied to interpret complex datasets and improve classification accuracy. Our work supports earlier diagnosis, improved understanding of ageing mechanisms, and more personalised approaches to health monitoring and disease management.
Microbiology and Antimicrobial Resistance (AMR)
With a focus on bacterial identification and antimicrobial resistance (AMR) we have developed methods for rapid, accurate detection of pathogens and resistance profiles using advanced spectroscopic and chemometric methods. Our research includes Raman spectroscopy, SERS, and mass spectrometry-based approaches to characterise bacterial species at strain level and to distinguish resistant from susceptible phenotypes. Machine learning and multivariate analysis are used to interpret complex spectral data, enabling high-throughput classification and predictive modelling of AMR markers. Our single cell spectroscopic work supports development of culture-free or reduced-culture diagnostic tools for clinical and environmental samples, improving turnaround times, surveillance, and decision-making in antimicrobial stewardship and infection control strategies frameworks and outcomes.
Single Cell Analysis
Our single-cell analysis focuses on applying advanced Optical Photothermal infrared (O-PTIR) and Raman spectroscopic microscopy to resolve biochemical variation at the level of individual bacterial cells. These imaging-based methods are combined with chemometrics to interpret these highly complex, high-dimensional data. The research enables the identification of cellular heterogeneity in microbial populations and mammalian systems, supporting studies of phenotypic variation, stress responses, and disease progression. We also use stable isotope probing to follow metabolism dynamics in real-time. By moving beyond bulk measurements, our approaches improve resolution in biological interpretation and support applications in microbiology and antimicrobial resistance detection with greater diagnostic precision. We have also recently started translating these methods into cancer research.
Food Security and Environmental and Plant Systems
Our food security work focuses on improving food quality, authenticity, traceability, as well as agricultural resilience through advanced spectroscopic and metabolomic technologies. Research includes detecting food fraud and adulteration in products such as wheat flour, coconut water, olive oil, and palm oil, as well as developing portable methods for food authenticity testing across supply chains. We also study crop responses to nutrient limitation, drought tolerance in sorghum, and nitrogen use efficiency in oats and wheat, helping to support sustainable agricultural production. Together, our efforts contribute to safer, more resilient, and more secure food systems.
Data Science and Chemometrics
We have made significant contributions to data analysis and chemometrics through the development and application of advanced statistical, machine learning, and multivariate analytical methods for complex biological and chemical datasets. Our research integrates chemometric approaches with spectroscopy, metabolomics, and mass spectrometry to improve classification, authentication, quality control, and biomarker discovery. Key areas include spectral data interpretation, metabolomics workflow optimisation, machine learning for microbial identification, food authenticity testing, and predictive modelling of biological systems. We have also advanced quality assurance frameworks and best-practice guidelines, strengthening the reliability, reproducibility, and impact of analytical science, with a particular focus on metabolomics standards.
Surface enhanced Raman scattering (SERS)
SERS is a major research focus within our group, where it is routinely applied as a highly sensitive analytical platform for rapid chemical and biological detection. We have developed novel SERS substrates, nanomaterials, and data analysis approaches to improve sensitivity, reproducibility, and quantitative performance. Our applications span pathogen detection, food authentication, environmental monitoring, and biomedical diagnostics, including the identification of microbial species and disease-related biomarkers. By combining SERS with chemometric and machine learning methods, we enhance spectral interpretation and classification accuracy, as well as effect robust quantitative analysis. Our work supports the translation of SERS technologies into practical, portable, and real-world sensing applications.