Turn experimental data into interpretable models and decision-ready outputs.
Microbial growth modelling, fermentation data analysis, statistical interpretation, process modelling and Python-based decision tools for food science, biotech and technical R&D projects.
Experimental data is only useful when it can be interpreted and applied
Research and development projects in food science, fermentation and biotech generate large amounts of experimental data — growth curves, fermentation profiles, stability measurements, process parameters. The challenge is rarely the data itself. It is turning that data into models, interpretations and outputs that inform decisions.
Gecko Innovation SL works at the interface of experimental science and applied modelling. We help research teams fit models to data, interpret results statistically, build decision-support tools and document the analysis clearly for technical reports, project deliverables or regulatory submissions.
Modelling and data analysis services
Predictive Microbiology Modelling
Primary and secondary modelling of microbial growth, inactivation and survival using established predictive microbiology models. We fit models to experimental data, estimate parameters and validate model performance.
Applicable to food safety assessments, product shelf-life analysis, process design, challenge testing interpretation and quantitative microbiological risk assessment (QMRA).
We can help with
- Primary model fitting — Gompertz, Baranyi, modified Weibull
- Secondary modelling — effect of temperature, pH, water activity
- Growth rate and lag time parameter estimation
- Model validation and goodness-of-fit analysis
- Shelf-life prediction under defined conditions
- QMRA inputs and model documentation
Model report with parameters and validation metrics, growth prediction tool, technical documentation for regulatory or project use.
Fermentation Data Analysis
Analysis and interpretation of fermentation process data — including batch and fed-batch profiles, substrate consumption, product formation kinetics and process parameter effects. We help characterise fermentation performance and identify optimisation opportunities.
We can help with
- Fermentation kinetics — growth, substrate, product profiles
- Yield and productivity calculations
- Comparison across fermentation runs
- Effect of process parameters on fermentation outcome
- Data cleaning and structured analysis workflow
- Visual summaries for reports or presentations
Fermentation data analysis report, kinetic parameter summary, visualisations, Python or Excel analysis workflow.
Statistical Analysis and Experimental Data Interpretation
Statistical analysis of experimental data from R&D projects — including regression, ANOVA, correlation analysis and uncertainty quantification. We help teams understand what their data shows and what conclusions it can support.
We can help with
- Descriptive statistics and data quality review
- Regression analysis and model selection
- ANOVA and significance testing
- Uncertainty and variability analysis
- Interpretation guidance — what the data does and does not show
- Clear documentation for reports or publications
Statistical analysis report, summary tables and figures, interpretation notes, documented analytical workflow.
Process Modelling and Scenario Simulation
Modelling of biological, chemical or physical processes to support product development, process optimisation or decision-making. We build Python or Excel-based models that can be run across scenarios and updated as data changes.
We can help with
- Process model design and parameterisation
- Scenario simulation and sensitivity analysis
- Integration of experimental data into process models
- Decision-support tool development in Python or Excel
- Model documentation for audit or handover
Process model (Python or Excel), scenario analysis output, sensitivity report, model documentation.
For scientific and technical teams with experimental data
This solution is most useful when a team has experimental data that needs to be analysed, modelled or interpreted — and where the output needs to be documented clearly for decisions, reports or project deliverables.
Have experimental data that needs analysis or modelling?
Tell us about the data, the questions you need to answer and the format of the output you need. We will help you understand what is possible and how to proceed.