Evidence map›Paper›PMID 41890044›Full record

ArticlebioRxiv : the preprint server for biology2026

Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples.

Aman Yadav, Arlin Birkby, Noah Armstrong, Assame Arnob, Ming-Hsun Chou, Alma Fernandez, Aart J Verhoef, Zhenhuan Yi, Siddhant Gulati, Siddhi Kotnis and 3 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Aman YadavArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, USA.ORCID 0009-0002-9126-6754
Arlin BirkbyArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, USA.
Noah ArmstrongArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, USA.
Assame ArnobArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, USA.ORCID 0009-0005-6985-9117
Ming-Hsun ChouInstitute for Quantum Science and Engineering, Texas A&M University, College Station, TX 77843, USA.
Alma FernandezInstitute for Quantum Science and Engineering, Texas A&M University, College Station, TX 77843, USA.
Aart J VerhoefInstitute for Quantum Science and Engineering, Texas A&M University, College Station, TX 77843, USA.ORCID 0000-0003-2691-214X
Zhenhuan YiInstitute for Quantum Science and Engineering, Texas A&M University, College Station, TX 77843, USA.ORCID 0000-0003-4827-1013
Siddhant GulatiArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, USA.
Siddhi KotnisArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, USA.
Qing SunArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, USA.ORCID 0000-0002-7518-644X
Katy C KaoDepartment of Chemical and Materials Engineering, San Jose State University, San Jose 95112-3613, California, USA.ORCID 0000-0002-1184-276X
Hung-Jen WuArtie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, USA.ORCID 0000-0003-3082-7431

Funding

Role of Membrane Dynamics In Cell Surface Glycan RecognitionR35GM156609 · NIGMS · TEXAS ENGINEERING EXPERIMENT STATION · PI Hung-Jen Wu · 2025 to 2026
$768k
NIGMS NIH HHS R35 GM156609
6 · The paper itself

Abstract

Machine learning (ML)-assisted Raman spectroscopy has become a powerful analytical tool for the classification and identification of analytes; however, technical challenges impacting its detection accuracy have not been investigated. This study explores experimental factors affecting classification performance. Among the evaluated ML models, ML algorithms show minimal impacts on classification accuracy. Instead, experimental factors, including spectral similarity between tested samples and the data quality, dominate detection performance. Increases in spectral noises and spectral similarity significantly reduce classification accuracy. In well-controlled samples with low experimental noise, ML-assisted Raman spectroscopy can discriminate lipid mixtures with a composition difference of 1.85 mol%. To assess the effect of biological heterogeneity, we analyzed single-cell Raman spectra from

Indexed as

Lipid analysisMachine LearningRaman SpectroscopySaccharomyces cerevisiaeSingle cell analysis

Identifiers

PMID41890044
PMCPMC13014161

What OpenQuestion holds

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Registered trials

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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.