Grease Analysis Tool

Software for analysing grease absorption based on the infrared intake and identifying potential anomalies in samples by comparing with baseline data.

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Inspiration

Inspired by MRG Labs’ automation efforts like the Grease Monkey and the 2025 Schneider Prize challenge, we wanted to build a fast, visual, and intuitive solution that transforms CSV data into actionable graphs with minimal effort.

What it does

The Grease Analysis Tool automatically reads a baseline CSV file and multiple sample CSV files, superimposes them into comparative graphs, and batch-exports those plots as .png images to a chosen directory.

How we built it

Frontend: React.js and TypeScript Backend: Python Flask and fastapi Database: AWS Aurora RDS Cloud: Docker

Challenges we ran into

Ensuring consistent parsing when CSV formats varied slightly between instruments. Handling large batches of sample files efficiently without overloading memory. Designing a clean, intuitive GUI while keeping all graphs synchronized. Automating batch export while still allowing live sample preview on the frontend.

Accomplishments that we're proud of

Built a working prototype that meets all the Schneider Prize requirements. Added a live preview mode that updates graphs instantly as samples are selected. Implemented automated file-naming and saving logic for seamless batch export. Deployed a full containerized version to AWS App Runner integrated with Aurora RDS for data persistence.

What we learned

We deepened our understanding of how lab data automation can bridge physical instrumentation and software interfaces.

We also learned: How to structure clean REST APIs between Flask and React. How to optimize data visualization pipelines for real-time feedback. How CSV data from lab devices reflects real grease condition metrics.

What's next for Grease Analysis Tool

Add statistical overlays (mean deviation, trend lines) for each sample batch. Integrate directly with MRG LIMS for automatic data upload. Develop a mobile dashboard for engineers to view reports remotely. Explore real-time hardware connections (e.g., Grease Monkey sensors) via HTTP streams.

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