RamApp, a modern hyperspectral imaging toolbox for processing and analysis

An intuitive, user-friendly and open-source software to explore Raman maps

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Main Features

RamApp is a web application that aims to provide a user-friendly and open-source solution allowing researchers from various backgrounds to easily and successfully explore hyperspectral data, with a focus on Raman imaging


Import data obtained from different sources, including the most diffused commercial software


RamApp covers the most-used tools and techniques for Raman maps: from cropping and smoothing to spikes detection and baseline correction

Map analysis

Analyse Raman maps using statistical data analysis methods such as clustering, PCA and multivariate curve resolution (MCR)

Stacked images composition

Create, as individual or stacked images, intensity maps with a high level of customization


Export publication-quality images or raw data for further analysis

User-friendly design

Interactively explore and visualise the data and the results of each processing step with a fast and easy work environment

Open source

The code will be made freely available to the public for possible modification and redistribution

Cloud-based solution

Access the app from any browser and operating system without requiring a local installation


Project developers


Any Questions? Answered

Don't hesitate to contact us if you have more questions

RamApp is currently finishing its developing phase and first a closed-beta version will be released soon. Contact us using the form below if you want to be updated on future developments of the app.
At the moment, RamApp can accept files from Renishaw™ grid map (*.wdf), Horiba™ LabSpec 5 (*.ngc), CSV tables (*.csv), Apache Parquet/Feather (*.parquet/*.feather) and also custom-defined formats. We plan to extend the compatibility with other file formats in the next months according to the users needs.
The first versions of RamApp will be free for everyone.
The following features are already present on RamApp: map rotation, spatial and spectral crop, spatial and spectral smooth (denoising), cosmic rays detection and removal, data resampling using a new spectral grid with equally-spaced nodes (helps to compare data having different calibration axes), data normalization, optical substrate identification and removal, cluster analysis, principal component analysis (PCA) and multivariate curve resolution (MCR).

For further details, contact us!

Our Location

Cairoli, Milan, Italy

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