ReviewiScience2025
A review on spectral data preprocessing techniques for machine learning and quantitative analysis.
Review in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
36 citing papers in PubMed.
- VIS-NIR-SWIR Proximal Spectroradiometry Coupled with Machine Learning and Deep Learning for Ornamental Plant Identification and Classification.ACS omega · 2026Article
- Interpretable Multi-Feature Optical Analysis for Stage- and Batch-Aware Quality Assessment of Brain-Organoid Cultures.Sensors (Basel, Switzerland) · 2026Article
- Evaluation of a Pre-Transplant Serum FTIR Rejection-Risk Score Across Time Horizons and Rejection Phenotype-Defined Endpoints in Kidney Transplantation.Journal of personalized medicine · 2026Article
- Bridging data and theory: hybrid modelling for pharmaceutical crystallisation process development.Digital discovery · 2026Review
- Towards Efficient Lignin Removal: A Box-Behnken Design Approach to Corncob Delignification Using Deep Eutectic Solvents.Molecules (Basel, Switzerland) · 2026Article
- SERS-Enabled Direct Detection of Furfural in a Complex Oil Mixture.Sensors (Basel, Switzerland) · 2026Article
- Method Adaptation Patterns and a Selection Framework for Near-Infrared Spectroscopy-Based Moisture Prediction of Major Grain Crops.Foods (Basel, Switzerland) · 2026Article
- Smart Chemical Sensors for Monitoring and Detection of Spoilage in Fermented and Non-Fermented Food Products.Sensors (Basel, Switzerland) · 2026Review
- Label-free Raman spectroscopic imaging enables high-content molecular phenotyping of respiratory diseases from bronchoalveolar lavage fluid.Journal of translational medicine · 2026Article
- Near-Infrared Spectroscopy Coupled with Chemometrics for Rapid Determination of pH, Moisture and Lycopene Content in Lycopene Liquid Beverages.Foods (Basel, Switzerland) · 2026Article
- Feasibility of Hyperspectral Imaging and Machine Learning for Rapid Prescreening of Aflatoxin BToxins · 2026Article
- Near-Infrared Hyperspectral Imaging to Predict Intact Sweet Tamarind Fruit Quality.Foods (Basel, Switzerland) · 2026Article
- Risk Phenotyping Before Graft Implantation: FTIR Spectroscopy and Machine Learning for Complementary Risk Stratification in Kidney Transplantation.Medical sciences (Basel, Switzerland) · 2026Article
- AI/ML-Assisted SERS Biosensing for Biomolecular Detection: From Direct Spectral Response to Integrated Diagnostic Systems.Biosensors · 2026Review
- Raman Spectroscopy for Probing Pathological Protein Aggregates: Potential and Perspectives for Advanced Diagnostic Applications.International journal of molecular sciences · 2026Review
- TRaP: An Open-source, Reproducible Framework for Raman Spectral Preprocessing across Heterogeneous Systems.bioRxiv : the preprint server for biology · 2026Article
- From Flow to Feature Using a Proof-of-Concept Spectral-Driven Machine Learning Approach Using Smart Urinary and Drainage Catheter Systems: Algorithm Development and Validation.JMIR medical informatics · 2026Article
- Review
- Integrating Metabolomics and Machine Learning for Advanced Chemical Detection.Sensors (Basel, Switzerland) · 2026Review
- GA-SVR Optimized Surface-Enhanced Raman Spectroscopy for Rapid Detection of Ciprofloxacin Residues in Chicken Blood.Biosensors · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spectroscopic techniques are indispensable for material characterization, yet their weak signals remain highly prone to interference from environmental noise, instrumental artifacts, sample impurities, scattering effects, and radiation-based distortions (e.g., fluorescence and cosmic rays). These perturbations not only significantly degrade measurement accuracy but also impair machine learning-based spectral analysis by introducing artifacts and biasing feature extraction. This review provides a systematic evaluation of critical spectral preprocessing methods-encompassing cosmic ray removal, baseline correction, scattering correction, normalization, filtering and smoothing, spectral derivatives, and advanced techniques like 3D correlation analysis-highlighting their theoretical underpinnings, performance trade-offs, and optimal application scenarios. The field is undergoing a transformative shift driven by three key innovations: context-aware adaptive processing, physics-constrained data fusion, and intelligent spectral enhancement. These cutting-edge approaches enable unprecedented detection sensitivity achieving sub-ppm levels while maintaining >99% classification accuracy, with transformative applications spanning pharmaceutical quality control, environmental monitoring, and remote sensing diagnostics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.