Welcome to NIRS4ALL’s documentation!

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NIRS4ALL is a comprehensive machine learning library specifically designed for Near-Infrared Spectroscopy (NIRS) data analysis. It bridges the gap between spectroscopic data and machine learning by providing a unified framework for data loading, preprocessing, model training, and evaluation.

🚀 Getting Started

New to nirs4all? Start here with installation and your first pipeline.

Beginner 5 min

Getting Started
📖 User Guide

Step-by-step guides for preprocessing, stacking, export, and more.

How-to Task-oriented

User Guide
📚 Reference

Complete API reference, pipeline syntax, and operator catalog.

Reference Complete

Reference
🔧 Developer Guide

Architecture, internals, and contribution guidelines.

Advanced Contribute

Developer Guide

Quick Start

import nirs4all
from sklearn.preprocessing import MinMaxScaler
from sklearn.cross_decomposition import PLSRegression
from sklearn.model_selection import ShuffleSplit

# Define and run a pipeline in one step
result = nirs4all.run(
    pipeline=[
        MinMaxScaler(),
        ShuffleSplit(n_splits=3),
        {"model": PLSRegression(n_components=10)}
    ],
    dataset="path/to/data",
    verbose=1
)

print(f"Best RMSE: {result.best_rmse:.4f}")
result.export("exports/best_model.n4a")

Try Without Data

No dataset? Generate synthetic NIRS spectra to get started:

import nirs4all

# Generate realistic synthetic NIRS data
dataset = nirs4all.generate.regression(
    n_samples=500,
    components=["water", "protein", "lipid"],
    complexity="realistic",
    random_state=42
)

# Run a pipeline on synthetic data
result = nirs4all.run(
    pipeline=[MinMaxScaler(), {"model": PLSRegression(10)}],
    dataset=dataset
)
print(f"RMSE: {result.best_rmse:.4f}")

Tip

See Examples for 50+ working examples organized by topic.

Documentation

What is Near-Infrared Spectroscopy (NIRS)?

Near-Infrared Spectroscopy (NIRS) is a rapid and non-destructive analytical technique that uses the near-infrared region of the electromagnetic spectrum (approximately 700-2500 nm). NIRS measures how near-infrared light interacts with the molecular bonds in materials, particularly C-H, N-H, and O-H bonds, providing information about the chemical composition of samples.

Key advantages of NIRS:

  • Non-destructive analysis

  • Minimal sample preparation

  • Rapid results (seconds to minutes)

  • Potential for on-line/in-line implementation

  • Simultaneous measurement of multiple parameters

Common applications:

  • Agriculture: soil analysis, crop quality assessment

  • Food industry: quality control, authenticity verification

  • Pharmaceutical: raw material verification, process monitoring

  • Medical: tissue monitoring, brain imaging

  • Environmental: pollutant detection, water quality monitoring

Features

NIRS4ALL offers a wide range of functionalities:

  1. Spectrum Preprocessing:

    • Baseline correction

    • Standard normal variate (SNV)

    • Robust normal variate

    • Savitzky-Golay filtering

    • Normalization

    • Detrending

    • Multiplicative scatter correction

    • Derivative computation

    • Gaussian filtering

    • Haar wavelet transformation

    • And more…

  2. Data Splitting Methods:

    • Kennard Stone

    • SPXY

    • Random sampling

    • Stratified sampling

    • K-means

    • And more…

  3. Model Integration:

    • Scikit-learn models

    • TensorFlow/Keras models

    • Pre-configured neural networks dedicated to the NIRS: nicon & decon (see publication below)

    • PyTorch models (via extensions)

    • JAX models (via extensions)

  4. Model Fine-tuning:

    • Hyperparameter optimization with Optuna

    • Grid search and random search

    • Cross-validation strategies

  5. Visualization:

    • Preprocessing effect visualization

    • Model performance visualization

    • Feature importance analysis

    • Classification metrics

    • Residual analysis

  6. Synthetic Data Generation:

    • Physically-motivated NIRS spectra (Beer-Lambert law)

    • Predefined spectral components (water, protein, lipid, etc.)

    • Configurable complexity levels for testing

    • Classification and regression targets

    • Export to CSV for loader testing

Installation

Basic Installation

pip install nirs4all

This installs the core library with scikit-learn support. Deep learning frameworks are optional.

With Additional ML Frameworks

# With TensorFlow support (CPU)
pip install nirs4all[tensorflow]

# With TensorFlow support (GPU)
pip install nirs4all[gpu]

# With PyTorch support
pip install nirs4all[torch]

# With Keras support
pip install nirs4all[keras]

# With JAX support
pip install nirs4all[jax]

# With all ML frameworks
pip install nirs4all[all]

# With all ML frameworks and GPU support for TensorFlow
pip install nirs4all[all-gpu]

Development Installation

For developers who want to contribute:

git clone https://github.com/gbeurier/nirs4all.git
cd nirs4all
pip install -e .[dev]

Research Applications

NIRS4ALL has been successfully used in published research:

Houngbo, M. E., Desfontaines, L., Diman, J. L., Arnau, G., Mestres, C., Davrieux, F., Rouan, L., Beurier, G., Marie‐Magdeleine, C., Meghar, K., Alamu, E. O., Otegbayo, B. O., & Cornet, D. (2024). Convolutional neural network allows amylose content prediction in yam (Dioscorea alata L.) flour using near infrared spectroscopy. Journal of the Science of Food and Agriculture, 104(8), 4915-4921. John Wiley & Sons, Ltd.

How to Cite

If you use NIRS4ALL in your research, please cite:

@software{beurier2025nirs4all,
  author = {Gregory Beurier and Denis Cornet and Camille Noûs and Lauriane Rouan},
  title = {nirs4all is all your nirs: Open spectroscopy for everyone},
  url = {https://github.com/gbeurier/nirs4all},
  version = {0.2.1},
  year = {2025},
}

License

This project is licensed under the CECILL-2.1 License - see the LICENSE file for details.