ReviewAnalytical science advances2021
Trends in artificial intelligence, machine learning, and chemometrics applied to chemical data.
Review in Analytical science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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
32 citing papers in PubMed.
- The Role of Artificial Intelligence and Machine Learning in Revolutionizing Probiotic Research.Microorganisms · 2026Review
- Validated Near-Infrared Spectroscopy and Chemometric Modelling for Rapid Quantification of Essential Oil Yield and α/β-Santalol inGlobal challenges (Hoboken, NJ) · 2026Article
- Gold- or Silver-Nanoparticle SERS Platforms for Plasma-Based Diagnostics and AI-Driven Analysis.Sensors (Basel, Switzerland) · 2026Review
- Article
- From pen to paper-based screening: an early-stage colorimetric µPAD with desktop-app readout for lead and nitrite.RSC advances · 2026Article
- Artificial Intelligence in Analytical Chemistry: Towards Yet Undiscovered Opportunities.Analytical chemistry · 2026Review
- How Artificial Intelligence Can Advance Electrochemical Science and Identify Water Molecule Orientation on Platinum Electrodes.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Review
- Assessment of the effectiveness of serum-infrared spectroscopy in conjunction with multivariate analysis methods for atherosclerosis diagnosis.Scientific reports · 2026Article
- The Role of Artificial Intelligence in Modern Analytical Chemistry: Current Trends and Future Directions.International journal of analytical chemistry · 2026Review
- Artificial Intelligence as a Scientific Copilot in Analytical Chemistry: Transforming How We Write, Review, and Publish.Analytical chemistry · 2025Review
- Artificial Intelligence-Based Methods and Omics for Mental Illness Diagnosis: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Antimicrobial agents based on metal-ion zeolite materials: a multivariate approach to microbial growth inhibition.RSC advances · 2025Article
- Raman Spectroscopy for Nitrate Detection in Water: A Review of the Current State of Art.ACS measurement science au · 2025Review
- Prediction of Soil Properties Using Vis-NIR Spectroscopy Combined with Machine Learning: A Review.Sensors (Basel, Switzerland) · 2025Review
- Quantification of Phenolic Compounds in Olive Oils by Near-Infrared Spectroscopy and Multiple Regression: Effects of Cultivar, Hydroxytyrosol Supplementation, and Deep-Frying.Antioxidants (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Advancing Algal Bioactive Ingredients: Production, Characterization, and Application.Foods (Basel, Switzerland) · 2025Review
- Advancements in SERS: Revolutionizing Biomedical Analysis and Applications.Nanotheranostics · 2025Review
- The Central Composite Design and Artificial Neural Network Coupled with Genetic Algorithm in Optimization and Modeling of the Radiolabeling Process ofCurrent radiopharmaceuticals · 2025Article
- The Role of Artificial Intelligence and Machine Learning in Polymer Characterization: Emerging Trends and Perspectives.Chromatographia · 2025Article
- Simultaneous Recognition and Detection of Adenosine Phosphates by Machine Learning Analysis for Surface-Enhanced Raman Scattering Spectral Data.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence-based methods such as chemometrics, machine learning, and deep learning are promising tools that lead to a clearer and better understanding of data. Only with these tools, data can be used to its full extent, and the gained knowledge on processes, interactions, and characteristics of the sample is maximized. Therefore, scientists are developing data science tools mentioned above to automatically and accurately extract information from data and increase the application possibilities of the respective data in various fields. Accordingly, AI-based techniques were utilized for chemical data since the 1970s and this review paper focuses on the recent trends of chemometrics, machine learning, and deep learning for chemical and spectroscopic data in 2020. In this regard, inverse modeling, preprocessing methods, and data modeling applied to spectra and image data for various measurement techniques are discussed.
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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.