siMPle: Freeware for Automated Microplastic Spectral Analysis

siMPle (Systematic Identification of MicroPLastics in the Environment) is a powerful, open software tool developed jointly by Aalborg University (Denmark) and the Alfred Wegener Institute (Germany) for automatic or semi-automatic microplastic (MP) analysis using IR spectral data.

It combines the user-friendly interface of MPHunter with the fully automated AWI pipeline, enabling researchers, citizen scientists, and labs to process large datasets more efficiently.

Key Features & Capabilities

• Spectral Matching & Automated Classification
  siMPle compares each pixel’s IR spectrum against a reference database (plastics and natural materials) and assigns material types based on probability scoring and threshold criteria.

• Integrated Interface + Automation
  The software merges MPHunter’s interface with AWI’s automatic pipeline, providing both manual review and batch processing paths in one application.

• Lightweight & Efficient
  The application itself is small (~5 MB) and designed to handle data from FPA detectors (e.g. 64×64, 128×128) efficiently.

• Data Handling & Quantification
  After identification, siMPle calculates particle dimensions (assuming elliptic shapes), volume (ellipsoid assumption), and derives mass estimates based on material density

• Noise Filtering & Adjacent Pixel Clustering
  It uses statistical filters and spatial clustering to ensure that contiguous pixels matching the same polymer type are grouped into single particles.

• Open Reference Libraries
  The software ships with a reference database of ~326 spectra, covering various plastics and natural materials that often confound plastic signals.

• Raman Beta Support
  A preliminary Raman preprocessing option is available (in beta) for users who have preprocessed Raman spectra.

Why siMPle Matters for Plastic Pollution Research

• Scalability & Throughput: Manual analysis of spectral maps can be prohibitively slow, especially with gigabytes of data per scan. siMPle’s automated pipeline speeds up analysis, improves reproducibility, and reduces human bias.

• Interoperability & Harmonization: By being independent of specific vendor tools, siMPle helps standardize MP spectral analysis across different labs and instruments, reducing fragmentation in methods

• Accessible to Non-Commercial Users: As freeware, it lowers the barrier for small labs, academic groups, or citizen-science programs to adopt advanced microplastic spectral analysis tools.

• Proven Use in Literature: siMPle has been validated and used in multiple peer-reviewed studies, including “Evaluation of a New Independent Software Tool (siMPle) for Spectroscopic Analysis” (Primpke et al.) SAGE Journals

How to Get Started with siMPle

1. Download & Installation
   siMPle (and its reference libraries) can be downloaded from its website. The software is Windows-based.

2. Learn via the How-To Guides
   Detailed manuals are available (printable) that explain how to run MP detection, convert OPUS data, and use the software’s automated pipeline (APP component).

3. Prepare Your Data
   Convert your spectral data (e.g., .dmd, .dx, .spe) into formats accepted by siMPle. Load the reference library for automated matching.

4. Run Analysis & Review Results
   Use the APP/MPAPP modules to perform automatic matching, then review the results, which include classification, particle counts, dimension metrics, and output files (CSV, images).

5. Interpret / Export Data
   The software produces histograms, particle-level CSVs, aggregated plastic counts, and images of the identified particles for downstream analysis.

Best Practices & Caveats

• Threshold tuning: The algorithm uses two probability thresholds. Users should validate threshold values especially when dealing with low signal-to-noise or overlapping spectra.

• Shape & Volume Assumptions: The minor/major axis and thickness assumptions (using ellipsoid model) are approximations; they may introduce error especially for irregular particle shapes.

• Database completeness: The reference library covers many but not all polymer types. For novel or composite plastics, users might need to expand or refine the library.

• Raman functionality is beta: Use Raman support cautiously — it is still under development and expects preprocessed data.

• Instrument differences: Performance may vary across FTIR instruments, detectors, or scanning settings; cross-validation is recommended.