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Xpectrass : an evaluation-driven preprocessing and interpretable machine learning platform for large-scale FTIR polymer classification

Khanam, M. Maksuda ; Younus, Saleena LU ; Mousafi Alasal, Laila LU ; Uddin, M. Khabir and Kazi, Julhash U. LU orcid (2026) In Digital Discovery
Abstract

Fourier transform infrared (FTIR) spectroscopy is widely used for polymer identification in microplastics research. However, raw spectra are often affected by noise, baseline drift, and other acquisition-related artifacts that are not chemically meaningful. Since preprocessing steps can strongly influence multivariate analysis and machine learning (ML) classification, we developed Xpectrass, an open-source framework designed for systematic evaluation of FTIR preprocessing, exploratory analysis, and interpretable ML. A dataset covering multiple polymer classes was used to compare different denoising, baseline correction, and normalization strategies. A preprocessing workflow based on wavelet denoising, adaptive smoothness parameter... (More)

Fourier transform infrared (FTIR) spectroscopy is widely used for polymer identification in microplastics research. However, raw spectra are often affected by noise, baseline drift, and other acquisition-related artifacts that are not chemically meaningful. Since preprocessing steps can strongly influence multivariate analysis and machine learning (ML) classification, we developed Xpectrass, an open-source framework designed for systematic evaluation of FTIR preprocessing, exploratory analysis, and interpretable ML. A dataset covering multiple polymer classes was used to compare different denoising, baseline correction, and normalization strategies. A preprocessing workflow based on wavelet denoising, adaptive smoothness parameter penalized least squares (asPLS) baseline correction, and either standard normal variate (SNV) or spectral moments normalization provided a harmonized feature space for downstream analyses. Starting from a harmonized collection of 12 189 spectra, we selected a labeled, non-redundant subset of 4018 spectra across eight major polymer classes for the main exploratory and machine-learning analyses. Unsupervised methods such as PCA, t-SNE, and UMAP moderately separated polymer classes with partial overlap among chemically similar polyolefins. We further evaluated 41 ML model configurations across 8 algorithmic families and present detailed results for a representative model based on XGBoost. A reduced leave-one-dataset-out validation for PP versus PE+ showed mixed dataset-level transfer, with high performance for three hold-out datasets but poor performance for the Frond hold-out fold. Model interpretation using SHapley Additive exPlanations (SHAP) associated predictions with wavenumber regions, facilitating chemical interpretability. In summary, Xpectrass provides a structured and scalable workflow that links heterogeneous FTIR spectra to reproducible polymer classification in microplastics research.

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Contribution to journal
publication status
in press
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Digital Discovery
article number
d6dd00137h
publisher
Royal Society of Chemistry
external identifiers
  • scopus:105044713459
ISSN
2635-098X
DOI
10.1039/d6dd00137h
language
English
LU publication?
yes
additional info
Publisher Copyright: This journal is © The Royal Society of Chemistry, 2026.
id
4fb885b9-99fd-4f76-9e90-e6b48b427b27
date added to LUP
2026-08-01 19:36:24
date last changed
2026-08-03 08:43:18
@article{4fb885b9-99fd-4f76-9e90-e6b48b427b27,
  abstract     = {{<p>Fourier transform infrared (FTIR) spectroscopy is widely used for polymer identification in microplastics research. However, raw spectra are often affected by noise, baseline drift, and other acquisition-related artifacts that are not chemically meaningful. Since preprocessing steps can strongly influence multivariate analysis and machine learning (ML) classification, we developed Xpectrass, an open-source framework designed for systematic evaluation of FTIR preprocessing, exploratory analysis, and interpretable ML. A dataset covering multiple polymer classes was used to compare different denoising, baseline correction, and normalization strategies. A preprocessing workflow based on wavelet denoising, adaptive smoothness parameter penalized least squares (asPLS) baseline correction, and either standard normal variate (SNV) or spectral moments normalization provided a harmonized feature space for downstream analyses. Starting from a harmonized collection of 12 189 spectra, we selected a labeled, non-redundant subset of 4018 spectra across eight major polymer classes for the main exploratory and machine-learning analyses. Unsupervised methods such as PCA, t-SNE, and UMAP moderately separated polymer classes with partial overlap among chemically similar polyolefins. We further evaluated 41 ML model configurations across 8 algorithmic families and present detailed results for a representative model based on XGBoost. A reduced leave-one-dataset-out validation for PP versus PE+ showed mixed dataset-level transfer, with high performance for three hold-out datasets but poor performance for the Frond hold-out fold. Model interpretation using SHapley Additive exPlanations (SHAP) associated predictions with wavenumber regions, facilitating chemical interpretability. In summary, Xpectrass provides a structured and scalable workflow that links heterogeneous FTIR spectra to reproducible polymer classification in microplastics research.</p>}},
  author       = {{Khanam, M. Maksuda and Younus, Saleena and Mousafi Alasal, Laila and Uddin, M. Khabir and Kazi, Julhash U.}},
  issn         = {{2635-098X}},
  language     = {{eng}},
  publisher    = {{Royal Society of Chemistry}},
  series       = {{Digital Discovery}},
  title        = {{Xpectrass : an evaluation-driven preprocessing and interpretable machine learning platform for large-scale FTIR polymer classification}},
  url          = {{http://dx.doi.org/10.1039/d6dd00137h}},
  doi          = {{10.1039/d6dd00137h}},
  year         = {{2026}},
}