ReviewChemical reviews2025
Exploring Generative Artificial Intelligence and Data Augmentation Techniques for Spectroscopy Analysis.
Review in Chemical reviews, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Application of Hyperspectral Imaging and Generative Adversarial Network for Powdery Mildew Severity Detection on Melon Leaves.Plants (Basel, Switzerland) · 2026Article
- Surface-Enhanced Raman Spectroscopy for Viral Diagnostics: Principles, Strategies, Clinical Challenges, and Future Directions.Chemical reviews · 2026Review
- Boosting identification of microsporidian spores originating from different hosts: single-cell Raman spectroscopy combined with self-attention mechanism-driven convolutional neural network.Analytical and bioanalytical chemistry · 2026Article
- Rapid Classification of Coffee Varieties Using Single-Bean Hot Gas Extraction Ion-Mobility Spectrometry with Machine Learning.ACS measurement science au · 2026Article
- Raman Spectroscopy in Cancer Diagnostics and Surgery: 25 Years of Progress from Surface-Enhanced Raman Spectroscopy to Artificial Intelligence─A Bibliometric and Visualized Study.Analytical chemistry · 2026Review
- Visible-Near-Infrared Hyperspectral Imaging Enables Nondestructive Identification of Bean Accessions via 1D Spectral Reflectance Analysis.ACS omega · 2026Article
- Training and validation of a 1D CNN model for accurate three-class classification of oral cancer using serum Raman spectroscopy.Discover oncology · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative artificial intelligence (AI) techniques are advancing rapidly and are becoming increasingly challenging to implement. Researchers, practitioners, and enthusiasts alike now require an understanding of complex concepts far beyond the scope of simple feed-forward neural networks to implement the current state-of-the-art methods for their research interests. In contrast, while data augmentation methods may not perform at the same level, they are easier to understand and implement, and are well demonstrated. For these reasons, this review aims to bridge the knowledge gap between the sciences of chemometrics and generative AI and provide a starting point for new researchers. In the context of spectroscopy, this work collects, categorizes, and describes the most popular preprocessing techniques and the state-of-the-art in generative AI and data augmentation, spanning over 104 peer-reviewed journals and proceedings across 32 publishers and organisations. We provide intuitive explanations of the methods, highlighting their strengths and weaknesses, and we include graphical and practical examples of their applications.
Identifiers
What OpenQuestion holds
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.