Review

Artificial Intelligence in Analytical Chemistry: A Critical Review of Applications, Model Performance, Validation, and Emerging Challenges

Volume: 2026 Number: 2 October 1, 2026

Artificial Intelligence in Analytical Chemistry: A Critical Review of Applications, Model Performance, Validation, and Emerging Challenges

Abstract

Artificial intelligence (AI) is transforming analytical chemistry by making it even more efficient to process and interpret complex high-dimensional analytical data. Machine learning (ML) and deep learning (DL) approaches have huge potential in spectroscopy, chromatography, mass spectrometry, and other analytical applications and thus enhance classification, prediction, method optimization, and data interpretation. In this review we examine the current state of AI applications in analytical chemistry, especially for the commonly employed approaches (PLS, SVM, ANN, random forests, and CNN) and their features and limitations. In addition to predictive accuracy, the main challenges to reliable implementation are overfitting and dataset size and quality, preprocessing dependence, model complexity and interpretability, external validation, and transferability between different instruments and laboratories. To date, no single approach is more efficient, and hence, the selection of a model is based on the characteristics of analytical data used, application of the data, and validation strategy. The major knowledge gaps in this regard are: a lack of standardized datasets, limited and non-standardized external and inter-laboratory validation of models, and a lack of chemically meaningful model interpretation. Correcting these limitations is essential to implement high-performance AI models in a more realistic, transparent, and transferable analytical tool for modern analytical chemistry, and this is of great importance for their translation and successful application in the field of analytical chemistry.

Keywords

References

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Details

Primary Language

English

Subjects

Analytical Chemistry (Other)

Journal Section

Review

Publication Date

October 1, 2026

Submission Date

May 14, 2026

Acceptance Date

August 15, 2026

Published in Issue

Year 2026 Volume: 2026 Number: 2

APA
Jannat Doust, C., & Ozkan, E. (2026). Artificial Intelligence in Analytical Chemistry: A Critical Review of Applications, Model Performance, Validation, and Emerging Challenges. Journal of the Turkish Chemical Society Section A: Chemistry, 2026(2), 1-14. https://doi.org/10.18596/jotcsa.1951243
AMA
1.Jannat Doust C, Ozkan E. Artificial Intelligence in Analytical Chemistry: A Critical Review of Applications, Model Performance, Validation, and Emerging Challenges. JOTCSA. 2026;2026(2):1-14. doi:10.18596/jotcsa.1951243
Chicago
Jannat Doust, Celin, and Ece Ozkan. 2026. “Artificial Intelligence in Analytical Chemistry: A Critical Review of Applications, Model Performance, Validation, and Emerging Challenges”. Journal of the Turkish Chemical Society Section A: Chemistry 2026 (2): 1-14. https://doi.org/10.18596/jotcsa.1951243.
EndNote
Jannat Doust C, Ozkan E (October 1, 2026) Artificial Intelligence in Analytical Chemistry: A Critical Review of Applications, Model Performance, Validation, and Emerging Challenges. Journal of the Turkish Chemical Society Section A: Chemistry 2026 2 1–14.
IEEE
[1]C. Jannat Doust and E. Ozkan, “Artificial Intelligence in Analytical Chemistry: A Critical Review of Applications, Model Performance, Validation, and Emerging Challenges”, JOTCSA, vol. 2026, no. 2, pp. 1–14, Oct. 2026, doi: 10.18596/jotcsa.1951243.
ISNAD
Jannat Doust, Celin - Ozkan, Ece. “Artificial Intelligence in Analytical Chemistry: A Critical Review of Applications, Model Performance, Validation, and Emerging Challenges”. Journal of the Turkish Chemical Society Section A: Chemistry 2026/2 (October 1, 2026): 1-14. https://doi.org/10.18596/jotcsa.1951243.
JAMA
1.Jannat Doust C, Ozkan E. Artificial Intelligence in Analytical Chemistry: A Critical Review of Applications, Model Performance, Validation, and Emerging Challenges. JOTCSA. 2026;2026:1–14.
MLA
Jannat Doust, Celin, and Ece Ozkan. “Artificial Intelligence in Analytical Chemistry: A Critical Review of Applications, Model Performance, Validation, and Emerging Challenges”. Journal of the Turkish Chemical Society Section A: Chemistry, vol. 2026, no. 2, Oct. 2026, pp. 1-14, doi:10.18596/jotcsa.1951243.
Vancouver
1.Celin Jannat Doust, Ece Ozkan. Artificial Intelligence in Analytical Chemistry: A Critical Review of Applications, Model Performance, Validation, and Emerging Challenges. JOTCSA. 2026 Oct. 1;2026(2):1-14. doi:10.18596/jotcsa.1951243