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Professional Chemometric
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PCA, Data Handling, MLR & Design of Experiments - all free and interactive.

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Modular Architecture

Independent calculation modules with organized folder structure

📁 Data Handling

Complete data import, management, and export system.

  • modules/data_handling/
  • loaders.py - CSV, Excel, RAW support
  • exporters.py - Data export & backup
  • transformations.py - Data operations
  • validators.py - Input validation
Try Data Handling

📁 PCA Analysis

Principal Component Analysis with advanced diagnostics.

  • modules/pca/
  • calculations.py - Core PCA & rotations
  • diagnostics.py - T² & Q statistics
  • plots.py - 2D/3D visualizations
  • statistics.py - PCA metrics
Try PCA Analysis

📁 MLR & DoE

Multiple Linear Regression & Design of Experiments.

  • modules/mlr_doe/
  • doe_generator.py - Factorial matrices
  • mlr_model.py - Model computation
  • diagnostics.py - VIF & residuals
  • response_surface.py - RS analysis
Try MLR & DoE

📁 Preprocessing

Data preprocessing and spectral transformation.

  • modules/preprocessing/
  • scaling.py - Standardization & normalization
  • centering.py - Mean centering
  • spectral.py - Spectral preprocessing
  • missing_data.py - Missing value handling
Try Preprocessing

📁 Visualization

Unified visualization and theming system.

  • modules/visualization/
  • colors.py - Color theme management
  • plots_common.py - Shared plot functions
  • themes.py - Consistent styling
View Visualizations

📁 Multivariate Calibration (PLS)

Partial Least Squares regression for quantitative analysis.

  • modules/calibration/
  • pls_regression.py - PLS model computation
  • calibration.py - Calibration & validation
  • predictions.py - Sample prediction & uncertainty
  • diagnostics.py - Model quality & outliers
Try PLS Calibration

📁 Classification

Supervised classification and pattern recognition.

  • modules/classification/
  • models.py - Classification algorithms
  • training.py - Model training pipeline
  • evaluation.py - Performance metrics
  • plots.py - Classification plots
Try Classification
7
Core Modules
21+
Calculation Files
100%
Modular Design
∞
Scalability

Technical Architecture

📂 Project Structure

chemometricsolutions/
├── streamlit_app.py         # Main entry point
├── requirements.txt
├── pages/                    # Streamlit pages
│   ├── 1_Data_Handling.py
│   ├── 2_PCA_Analysis.py
│   ├── 3_MLR_DoE.py
│   ├── 4_Calibration_PLS.py
│   └── 5_Classification.py
└── modules/              # Calculation modules
    ├── data_handling/       # Data I/O and transformations
    ├── pca/                 # PCA calculations & diagnostics
    ├── mlr_doe/             # MLR & DoE computations
    ├── calibration/         # PLS multivariate calibration
    ├── preprocessing/       # Data preprocessing
    ├── visualization/       # Unified theming & plots
    └── classification/      # Classification algorithms

✓ Benefits

  • • Independent, testable modules
  • • Easy maintenance & debugging
  • • Scalable architecture
  • • Reusable calculations

🔧 Technologies

  • • Python 3.9+ (NumPy, SciPy)
  • • Streamlit for web interface
  • • Plotly for visualizations
  • • scikit-learn for ML algorithms

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