This package aims to provide a workflow for users who want to perform chemical analyses and predict characteristics using the NIRS technique.
The process includes:
- sample selection - you can upload all your NIRS spectra and it'll help to select the samples to analyse chemically.
- sample selection - you can upload all your NIRS spectra and it'll help select the samples to analyse chemically.
- model creation - PLS regression is used to create prediction models with spectra and related chemical analysis.
More algorythms had been added like LWPLR from Jchemo (https://github.com/mlesnoff/Jchemo.jl), PLSR with wavelength selection, etc.
More algorithms had been added like LWPLR from Jchemo (https://github.com/mlesnoff/Jchemo.jl), PLSR with wavelength selection, etc.
- predictions - the models are used to predict chemical values for unknown samples. We provide information for confidence in the predicted values depending on the samples and the model used.
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@@ -39,6 +39,8 @@ You can then run (CLI): streamlit run ./app.py from within your src/ folder.
The app will open in your default browser.
Ensure you run the app in a browser that is compatible with WebGL (Web Graphics Library).
_If you encounter an "import pyodbcImportError: libodbc.so.2" error on linux OS, please, install __unixodbc__ with apt install unixodbc from the CLI._
