advanced optical designs specializing in Raman spectroscopy
Software to Unlock Your Data
EmVision has partnered with Guillaume Sheehy to provide an open-sourced Raman spectroscopy data processing package named ORPL free of charge that can be used for advanced Raman spectroscopy analysis.
What is ORPL?
- ORPL, or Open Raman Processing Library, is an open-source software tool developed for processing and analyzing Raman spectroscopy data.
- It provides a comprehensive suite of functions and algorithms to facilitate spectral processing, peak identification, and quantitative analysis.
Key Features of ORPL
- Spectral Preprocessing: ORPL offers various preprocessing techniques such as baseline correction, noise reduction, and cosmic ray removal to improve data quality.
- Peak Fitting: Advanced peak fitting algorithms enable accurate identification and quantification of spectral peaks.
- Multivariate Analysis: ORPL supports multivariate techniques like principal component analysis (PCA) and partial least squares regression (PLS) for data interpretation.
- Customization: Users can customize analysis pipelines and algorithms to suit specific analytical requirements.
Advantages of ORPL
- Open-Source: ORPL is freely available and open-source, fostering collaboration and innovation within the Raman spectroscopy community.
- User-Friendly: ORPL features a user-friendly interface with documentation and tutorials, making it accessible to both novice and experienced users.
- Scalability: ORPL is designed to handle large datasets efficiently, allowing for scalable analysis across various sample types and experimental conditions.
Future Directions
- Community Engagement: Continued community involvement and contributions to expand ORPL’s functionality and usability.
- Integration: Integration with other analytical techniques and software platforms for seamless data exchange and interoperability.
- Performance Optimization: Ongoing optimization of algorithms and workflows to enhance processing speed and efficiency.
Here are all the links you need,
– ORPL windows installation guide (on github)
https://github.com/mr-sheg/orpl/blob/main/documentation/Installation%20guide%20-%20Windows.md
– ORPL windows installation guide (PDF to download)
https://github.com/mr-sheg/orpl/blob/main/documentation/Installing%20ORPL%20-%20Windows.pdf
– Python tutorial repo