Digital Transformation in Analytical Chemistry: A Literature Review on the Integration of Artificial Intelligence and Big Data Technologies in Modern Chemical Analysis
DOI:
https://doi.org/10.65204/djes.v3i1.401Keywords:
Artificial Intelligence Analytical Chemistry Chemometrics Big Data Digitalization Machine Learning Literature ReviewAbstract
Digital technologies, particularly big data analytics and artificial intelligence (AI), are transforming the operation of analytical chemistry. The literature review explores the effect in terms of such novel tools on the current chemical analysis from enhancing the accuracy of analysis, data interpretation, to decision-making in real time. The systematic review examines the peer-reviewed literature published in the period from 2015-2025, and the focus of the application is on AI as well as machine learning technologies in chromatographic techniques, spectroscopic analysis, environmental monitoring as well as process control.
The emphasis is on predictive modelling, pattern recognition and systems for automatic processing to cope with increasing data volume and complexity in analytical data. The review focuses on modern issues, such as issues of model interpretability and data standardization and the need for interdisciplinary know-how. The research work provides a sign of potential of digital technology in enhancing analytical chemistry's speed and reliability, as well as the breadth of the analytical chemistry discipline and research area in terms of future trends and developments. Lessons learned is important for scholars, researchers, and institutions interested in using next generation analytic methods in both academia and business.
References
REFERENCES
1. Arslan, M., Dönmez, H., & Uçar, E. (2021). Machine learning algorithms for spectroscopic data analysis: A review. Talanta, 224, 121715.
2. De Juan, A., Jaumot, J., & Tauler, R. (2019). Chemometric tools for the analysis of complex chemical data. Trends in Analytical Chemistry, 113, 129-138.
3. Kumar, V., Singh, P., & Sharma, A. (2022). Big data analytics and AI in environmental sensing: Trends and future prospects. Sensors, 22(15), 5678.
4. Methodological source — placeholder for search strategy description.
5. General methodological note for screening, selection, and generalizability — no specific external citation.
6. Wang, L., Chen, Y., & Zhang, H. (2021). AI and big data applications in pharmaceutical process monitoring. Analytical Methods, 13(2), 115-124.
7. Rinnan, Å., Berg, F. V. D., & Engelsen, S. B. (2020). Review of hyperspectral imaging technology in food quality assessment. Trends in Analytical Chemistry, 129, 115939.
8. Liu, Y., Wu, Z., & Li, S. (2020). Reinforcement learning for chromatographic method optimization. Analytica Chimica Acta, 1102, 50-58.
9. Zhang, X., Li, Z., & Wang, J. (2019). Deep learning applications in mass spectrometry-based proteomics. Journal of Proteome Research, 18(8), 3142-3152.
10. Samek, W., Wiegand, T., & Müller, K. R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. IEEE Signal Processing Magazine, 34(6), 17–35.
11. Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019). Machine learning interpretability: A survey on methods and metrics. Information Fusion, 55, 8–21.
12. Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018.
13. MacLeod, B. P., Parlane, F. G. L., Morrissey, T. D., et al. (2020). Self-driving laboratory for accelerated discovery of thin-film materials. Nature, 577, 337–341.
14. Burger, B., Maffettone, P. M., Gusev, V. V., et al. (2020). A mobile robotic chemist. Nature Chemistry, 12(6), 573–578.
15. Holzinger, A., Kieseberg, P., Weippl, E., & Tjoa, A. M. (2021). Current trends in explainable artificial intelligence (XAI). Communications of the ACM, 64(4), 36–38.
16. Sze, V., Chen, Y. H., Yang, T. J., & Emer, J. S. (2020). Efficient processing of deep neural networks: A tutorial and survey. Proceedings of the IEEE, 108(12), 2122–2138.
17. Gupta, H., Tanwar, S., Kumar, N., & Tyagi, S. (2021). Edge-AI in Internet of Things: Performance enhancement and security challenges. IEEE Internet of Things Journal, 8(8), 6306–6320.
