Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 4 complete abstracts (741 words), 10 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
The use of statistical and mathematical methods to analyze chemical observations and phenomena, such as epidemiological analysis of spectroscopically identified bacteria or quantitative structure-activity relationship.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Chemometric Analysis; Analyses, Chemometric; Analysis, Chemometric; Chemometric Analyses; Chemometry.
Where it fits in the MeSH classification
- Broader topics
- Chemistry Techniques, Analytical; Statistics as Topic; Epidemiologic Measurements
Related concepts
Research landscape
PubMed indexes 1,158 records tagged with the MeSH heading “Chemometrics” (counts retrieved 2026-10-29). 1,155 of them (99.7%) were published from 2020 onward, and the busiest period so far is 2020 onward with 1,155 records.
Reviews account for 52 records (4.5%) and meta-analyses for 0. Randomized controlled trials number 0 (0.0%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 0 |
| 2000-2009 | 0 |
| 2010-2019 | 3 |
| 2020 onward | 1,155 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 52 | 4.5% |
| Meta-analyses | 0 | 0.0% |
| Randomized controlled trials | 0 | 0.0% |
Method: PubMed query "Chemometrics"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.
Reading the evidence in clinical research
Research methods determine how much a result can be trusted. Randomized trials guard against confounding, systematic reviews and meta-analyses combine many studies, and observational designs are valuable but can show only association. Reporting standards, trial registration and pre-specified outcomes reduce bias. When reading about a method, ask what problem it is designed to solve and which problems it does not solve.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Application and Progress of Chemometrics in Voltammetric Biosensing | 2022 | Review article Design not recorded in structured metadata | 30 | cc by PMID 35884297 · DOI · PMC9313226 |
| Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography | 2022 | Research article Design not recorded in structured metadata | 27 | cc by PMID 36318471 · DOI · PMC9685588 |
| Quantitative chemometric phenotyping of three-dimensional liver organoids by Raman spectral imaging | 2023 | Research article Design not recorded in structured metadata | 26 | cc by PMID 37159662 · DOI · PMC10162950 |
| Pharmabiome analyses in tandem with chemometrics can help trace the provenance of falsified medicines: A proof-of-concept study | 2026 | Research article Design not recorded in structured metadata | 1 | cc by PMID 41274226 · DOI · PMC7618506 |
Application and Progress of Chemometrics in Voltammetric Biosensing [1]
Abstract reading summary — source excerpts
- Opening excerpt
- The voltammetric electrochemical sensing method combined with biosensors and multi-sensor systems can quickly, accurately, and reliably analyze the concentration of the main analyte and the overall characteristics of complex samples.
- Closing excerpt
- Finally, smartphones connected with miniaturized voltammetric biosensors and chemometric methods provide a high-quality portable analysis platform that shows great potential in point-of-care testing.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (149 words)
The voltammetric electrochemical sensing method combined with biosensors and multi-sensor systems can quickly, accurately, and reliably analyze the concentration of the main analyte and the overall characteristics of complex samples. Simultaneously, the high-dimensional voltammogram contains the rich electrochemical features of the detected substances. Chemometric methods are important tools for mining valuable information from voltammetric data. Chemometrics can aid voltammetric biosensor calibration and multi-element detection in complex matrix conditions. This review introduces the voltammetric analysis techniques commonly used in the research of voltammetric biosensor and electronic tongues. Then, the research on optimizing voltammetric biosensor results using classical chemometrics is summarized. At the same time, the incorporation of machine learning and deep learning has brought new opportunities to further improve the detection performance of biosensors in complex samples. Finally, smartphones connected with miniaturized voltammetric biosensors and chemometric methods provide a high-quality portable analysis platform that shows great potential in point-of-care testing.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 35884297 · DOI · PMC9313226
Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography [2]
Abstract reading summary — source excerpts
- Opening excerpt
- The majority of liquid chromatography (LC) methods are still developed in a conventional manner, that is, by analysts who rely on their knowledge and experience to make method development decisions.
- Closing excerpt
- , previously published data handling algorithms).
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (197 words)
The majority of liquid chromatography (LC) methods are still developed in a conventional manner, that is, by analysts who rely on their knowledge and experience to make method development decisions. In this work, a novel, open-source algorithm was developed for automated and interpretive method development of LC(-mass spectrometry) separations ("AutoLC"). A closed-loop workflow was constructed that interacted directly with the LC system and ran unsupervised in an automated fashion. To achieve this, several challenges related to peak tracking, retention modeling, the automated design of candidate gradient profiles, and the simulation of chromatograms were investigated. The algorithm was tested using two newly designed method development strategies. The first utilized retention modeling, whereas the second used a Bayesian-optimization machine learning approach. In both cases, the algorithm could arrive within 4-10 iterations (i.e., sets of method parameters) at an optimum of the objective function, which included resolution and analysis time as measures of performance. Retention modeling was found to be more efficient while depending on peak tracking, whereas Bayesian optimization was more flexible but limited in scalability. We have deliberately designed the algorithm to be modular to facilitate compatibility with previous and future work (e.g., previously published data handling algorithms).
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 36318471 · DOI · PMC9685588
Quantitative chemometric phenotyping of three-dimensional liver organoids by Raman spectral imaging [3]
Abstract reading summary — source excerpts
- Opening excerpt
- Confocal Raman spectral imaging (RSI) enables high-content, label-free visualization of a wide range of molecules in biological specimens without sample preparation.
- Closing excerpt
- Quantitative chemometric phenotyping constitutes an important step in developing quantitative label-free interrogation of 3D biological specimens.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (128 words)
Confocal Raman spectral imaging (RSI) enables high-content, label-free visualization of a wide range of molecules in biological specimens without sample preparation. However, reliable quantification of the deconvoluted spectra is needed. Here we develop an integrated bioanalytical methodology, qRamanomics, to qualify RSI as a tissue phantom calibrated tool for quantitative spatial chemotyping of major classes of biomolecules. Next, we apply qRamanomics to fixed 3D liver organoids generated from stem-cell-derived or primary hepatocytes to assess specimen variation and maturity. We then demonstrate the utility of qRamanomics for identifying biomolecular response signatures from a panel of liver-altering drugs, probing drug-induced compositional changes in 3D organoids followed by in situ monitoring of drug metabolism and accumulation. Quantitative chemometric phenotyping constitutes an important step in developing quantitative label-free interrogation of 3D biological specimens.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 37159662 · DOI · PMC10162950
Pharmabiome analyses in tandem with chemometrics can help trace the provenance of falsified medicines: A proof-of-concept study [4]
Abstract reading summary — source excerpts
- Opening excerpt
- A lack of robust analytical approaches limits our ability to investigate the clandestine manufacturing origins of falsified medicines.
- Closing excerpt
- Collectively, our research provides experimental support for the development of integrated, multifaceted tools for tracing the origin of falsified medicines, advancing efforts to combat this pervasive but neglected global health problem.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (267 words)
A lack of robust analytical approaches limits our ability to investigate the clandestine manufacturing origins of falsified medicines. We conducted a proof-of-concept study to test the feasibility of geolocating the production sites of falsified medicines, based on the identification of site-specific biological and chemo-isotopic features using a combination of environmental DNA metabarcoding, Direct Analysis in Real Time - Mass Spectrometry and Isotope Ratio Mass Spectrometry as profiling techniques. We produced tablets at two distant locations (England vs. Thailand), using controlled manufacturing methods, excipient composition and environmental conditions. Sets of tablets produced at separate locations showed distinct bacterial and eukaryotic diversity, particularly influenced by the incorporation of water used during tableting and the background environmental biosignatures of the production site. Tablets showed corresponding site-specific chemometric profiles, but the factors contributing to the observed chemical differences were unclear. When reference samples of known origin are available, our study suggests that site-specific biological and chemical features can be used in modelling approaches to successfully predict product origin. We developed a new mapping approach to exploit the geographic information within the eukaryotic pharmabiome of the falsifications; based on eDNA-derived species identification and the integration of publicly available species distribution data. In the absence of reference samples of known origin, the application of this workflow to our dataset provided partial clues about the product's origin, with limitations likely due to taxonomic resolution and the presence of species with wide distribution ranges. Collectively, our research provides experimental support for the development of integrated, multifaceted tools for tracing the origin of falsified medicines, advancing efforts to combat this pervasive but neglected global health problem.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41274226 · DOI · PMC7618506
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Chemometric analysis in Raman spectroscopy from experimental design to machine learning-based modeling [5]
- Research article A comprehensive overview of emerging techniques and chemometrics for authenticity and traceability of animal-derived food [6]
- Research article Chemometrics for Raman Spectroscopy Harmonization [7]
- Research article Recent Advances in GC×GC and Chemometrics to Address Emerging Challenges in Nontargeted Analysis [8]
- Research article Chemometric approach in environmental pollution analysis: A critical review [9]
- Research article Successful combination of benchtop nuclear magnetic resonance spectroscopy and chemometric tools: A review [10]
References
- Liu J, Xu Y, Liu S, Yu S, Yu Z, Low SS. Application and Progress of Chemometrics in Voltammetric Biosensing. . 2022. PMID 35884297 · DOI · PMC9313226
- Bos TS, Boelrijk J, Molenaar SRA, van 't Veer B, Niezen LE, van Herwerden D, et al.. Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography. . 2022. PMID 36318471 · DOI · PMC9685588
- LaLone V, Aizenshtadt A, Goertz J, Skottvoll FS, Mota MB, You J, et al.. Quantitative chemometric phenotyping of three-dimensional liver organoids by Raman spectral imaging. . 2023. PMID 37159662 · DOI · PMC10162950
- Perez-Mon C, Roncone A, Abrahim A, Islam M, Hauk C, Caillet C, et al.. Pharmabiome analyses in tandem with chemometrics can help trace the provenance of falsified medicines: A proof-of-concept study. . 2026. PMID 41274226 · DOI · PMC7618506
- Guo S, Popp J, Bocklitz T. Chemometric analysis in Raman spectroscopy from experimental design to machine learning-based modeling. Nat Protoc. 2021. PMID 34741152 · DOI
- Ye H, Yang J, Xiao G, Zhao Y, Li Z, Bai W, et al.. A comprehensive overview of emerging techniques and chemometrics for authenticity and traceability of animal-derived food. Food Chem. 2023. PMID 36152561 · DOI
- Barton B, Thomson J, Lozano Diz E, Portela R. Chemometrics for Raman Spectroscopy Harmonization. Appl Spectrosc. 2022. PMID 35622984 · DOI
- Trinklein TJ, Cain CN, Ochoa GS, Schöneich S, Mikaliunaite L, Synovec RE. Recent Advances in GC×GC and Chemometrics to Address Emerging Challenges in Nontargeted Analysis. Anal Chem. 2023. PMID 36625122 · DOI
- Inobeme A, Nayak V, Mathew TJ, Okonkwo S, Ekwoba L, Ajai AI, et al.. Chemometric approach in environmental pollution analysis: A critical review. J Environ Manage. 2022. PMID 35176568 · DOI
- Galvan D, de Aguiar LM, Bona E, Marini F, Killner MHM. Successful combination of benchtop nuclear magnetic resonance spectroscopy and chemometric tools: A review. Anal Chim Acta. 2023. PMID 37423658 · DOI
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-29.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.