Module Overview

mioXpektron is organized into focused modules that can be used independently or combined through the Pipeline Reference.

Module	Description
Adaptive Parameterization	Data-driven estimation of pipeline parameters (opt-in `auto_tune`)
Baseline Correction	Baseline correction using 20+ algorithms, batch correction, and method evaluation
Denoising	Wavelet, Gaussian, median, and Savitzky-Golay denoising with method comparison
Peak Detection	Peak detection (local max and CWT), area integration, cross-sample alignment
Calibration	Channel-to-m/z calibration with linear, quadratic, and reflectron TOF models
Normalization	18 normalization methods including robust SNV, multi-ion reference, mass-stratified PQN, and method evaluation
Plotting	Publication-ready spectrum and peak visualization
Utilities	File I/O, data import, batch processing, and statistical analysis

Data Flow

A typical mioXpektron workflow follows this data flow:

Raw Spectrum Files
     |
     v
[Optional] Adaptive Parameter Estimation (auto_tune=True)
     |
     v
[Optional] Auto-Calibration (Channel -> m/z)
     |
     v
Denoising (wavelet / gaussian / median / savitzky_golay)
     |
     v
Baseline Correction (AirPLS / AsLS / ...)
     |
     v
Normalization (TIC / Poisson / SNV / PQN / ...)
     |
     v
Peak Detection (local max or CWT)
     |
     v
Cross-Sample m/z Alignment
     |
     v
Output: Intensity & Area Matrices