NIRS_Workflow

Getting started

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.

• model creation - the PINARD (https://github.com/GBeurier/pinard) package creates a prediction model with spectra and related chemical analysis.-

• predictions - the PINARD package uses the model to predict chemical values for unknown samples.

If one wants to use data stored in a SQL database, the config file is in the config/ folder.

Installation

This package is written in python. You can clone the repository: git clone https://src.koda.cnrs.fr/CEFE/PACE/nirs_workflow.git

Then install the requirements: pip install -r requirements.txt (OPTIONNAL) To use Locally weighted PLS Regression for creation model, you will need to install Jchemo.jl (https://github.com/mlesnoff/Jchemo.jl), a Julia package. From the CLI: python

python
'>>> import julia
'>>> julia.install()
'>>> from julia import Pkg
'>>> Pkg.add(["Jchemo","DataFrames","Pandas"])

To check if Jchemo is installed without errors:

'>>> Pkg.status()

You can then run (CLI): streamlit run ./app.py from within your folder.

The app will open in your default browser.

Usage

The web app allows you to process sample selection, model creation and predictions.

Documentation

The doc is generated with mkDoc and Python DocStrings. From CLI, run

mkdocs serve

Authors and acknowledgment

Contributors:

• Nicolas Barthes (CNRS)

License

CC BY