By analyzing how light interacts with matter, spectroscopy helps understand the properties of materials and their behaviors across scientific disciplines and industries.
The increasing complexity and volume of spectroscopic data pose challenges for data analysis. However, in recent years, artificial intelligence (AI) has offered transformative solutions to enhance the speed, accuracy, and efficiency of spectroscopic analysis.
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Introduction to Spectroscopy and AI
Spectroscopy involves a range of techniques that provide useful information about the chemical composition, molecular structure, and physical properties of substances based on how matter absorbs, emits, or scatters light.
The rise of AI in recent years has also made its way into the field of spectroscopy, leading to the development of approaches for data analysis and modeling solutions for a variety of applications, enabling the automation of processes and improving the interpretation of spectroscopic data.1
AI in Spectroscopic Data Analysis
Spectroscopic experiments generate massive datasets whose analysis and interpretation often involve integrating mathematical, statistical, and chemical measurement methods.
Algorithms like partial least squares regression (PLS), used to model the relationship between input and output variables, or random forest (RF), which classifies data through multiple decision trees, are proving highly effective in spectroscopic analysis. Support vector machine (SVM) and deep learning have also emerged as powerful tools for handling spectral data.2
For instance, deep learning algorithms can automatically extract relevant features from Raman spectra, significantly improving data modeling, classification, and regression tasks in spectral analysis.
The use of AI can therefore speed up the analysis process and improve accuracy, reducing human error and labor, and leading to better interpretation and faster results.
Applications of Spectroscopy in Agriculture
Key Benefits of AI in Spectroscopy
Traditional methods often involve manual interpretation of the data in a process that is labor-intensive and prone to errors. Conversely, AI algorithms can analyze data in real time, enhancing the efficiency of spectroscopic analysis.
This speed is particularly important where timely insights can lead to better outcomes, such as identifying contaminants in drug manufacturing or monitoring pollution levels.
AI can also help improve the accuracy of spectroscopic analysis by training algorithms to consistently detect discrepancies in spectral data, reducing the chances of misinterpretation, which is particularly relevant in industries like pharmaceuticals, where ensuring the quality and safety of products is paramount.
One of the most appealing benefits of AI is its predictive capability. Based on existing spectroscopic data, machine learning models can be trained to predict the outcomes of chemical reactions or material behaviors.
Applications of AI-Enhanced Spectroscopy
AI-enhanced spectroscopy can reduce the time required for quality checks of drug formulations while enhancing precision allowing to streamline the production process of pharmaceuticals. For instance, AI algorithms can be used to monitor the quality of active pharmaceutical ingredients (APIs) during production, ensuring that each batch meets the required specifications.
A sensor developed by combining machine learning with mid-infrared spectroscopy showed promising results for the integration into a pharmaceutical packaging line for quality control.
This innovative approach used machine learning with laser-based diffuse reflectance spectroscopy to verify the blister content of pharmaceutical pills, leading to their fast, non-invasive, and highly selective classification.3
Through the analysis of large datasets of spectroscopic information, it is possible to identify promising new materials with desirable properties. Particularly in applications such as energy storage or electronic devices, AI can predict how these materials will perform under different conditions, helping design better products and systems.
Deep learning via a convolutional neural network (CNN) was used to speed up 2D nanoscale NMR spectroscopy (crucial for molecular structure determination), enhancing the signal-to-noise ratio.
The AI algorithm enhanced the 2D nanoscale NMR protocol, suppressing the observation noise and thus improving sensitivity. A similar approach was used for the classification of edible oils.4
In environmental monitoring, spectroscopic techniques are widely used to detect pollutants in air, water, and soil. There are several reports where the integration of AI has helped analyze data in real time, allowing for quicker responses to environmental changes or pollution events.
Machine learning can be used to improve the prediction capability of NIR spectroscopy for the accurate assessment of water pollution. The least squares support vector machine (LSSVM) method was used to establish NIR calibration models for the quantitative determination of chemical oxygen demand – a critical indicator of water pollution levels.5
Also, an effective and rapid method for the identification of soils contaminated with petroleum was developed by combining mid-infrared laser spectroscopy with AI. First, the presence of traces of petroleum was determined via remote sensing and the SVM algorithm. Then, principal component analysis (PCA) and SVM were applied to differentiate between different soil types (i.e., sea sand, red soil, and brown soil).6
Learn more about spectroscopy in forensic science
Challenges and Future of AI in Spectroscopy
Despite the several benefits, a significant challenge in the use of AI is the need for large, high-quality datasets to train AI models effectively. Collecting and curating these datasets can often be costly and time-consuming.
Moreover, integrating AI systems into existing spectroscopic setups requires technical expertise and investment, which may be a barrier for smaller organizations.
There are also concerns about the lack of transparency and explainability of how AI models produce their outcomes.
To address this challenge and build trust in the model’s decisions, explainable artificial intelligence (XAI) is emerging as a critical research area with the aim of providing insights into how AI models generate their predictions.7
Conclusion
Artificial intelligence is transforming spectroscopy by making the technology faster, more accurate, and more powerful. By automating data analysis, enhancing accuracy, and enabling predictive capabilities, AI is revolutionizing the use of spectroscopy in fields like pharmaceuticals, materials science, and environmental monitoring.
As AI continues to expand, its role in spectroscopy will grow accordingly, contributing to advancing various scientific fields.
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References
- Workman, J. J. M., Howard (2023). Artificial Intelligence in Analytical Spectroscopy, Part II: Examples in Spectroscopy. Spectroscopy, 38, 10-15.https://doi.org/10.56530/spectroscopy.js8781e3. Available: https://www.spectroscopyonline.com/view/artificial-intelligence-in-analytical-spectroscopy-part-ii-examples-in-spectroscopy
- Houhou, R. & Bocklitz, T. (2021). Trends in artificial intelligence, machine learning, and chemometrics applied to chemical data. Analytical Science Advances, 2, 128-141.https://doi.org/10.1002/ansa.202000162. Available: https://chemistry-europe.onlinelibrary.wiley.com/doi/abs/10.1002/ansa.202000162
- Flores, Y. V., Polak, A., Jambet, J., Stothard, D. & Haertelt, M. (2023). Point of Interest Mid-Infrared Spectroscopy for Inline Pharmaceutical Packaging Quality Control. IEEE Sensors Journal, 23, 16115-16122.10.1109/JSEN.2023.3281972.
- Kong, X., Zhou, L., Li, Z., Yang, Z., Qiu, B., Wu, X., Shi, F. & Du, J. (2020). Artificial intelligence enhanced two-dimensional nanoscale nuclear magnetic resonance spectroscopy. npj Quantum Information, 6, 79.10.1038/s41534-020-00311-z. Available: https://doi.org/10.1038/s41534-020-00311-z
- Chen, H., Xu, L., Ai, W., Lin, B., Feng, Q. & Cai, K. (2020). Kernel functions embedded in support vector machine learning models for rapid water pollution assessment via near-infrared spectroscopy. Science of The Total Environment, 714, 136765.https://doi.org/10.1016/j.scitotenv.2020.136765. Available: https://www.sciencedirect.com/science/article/pii/S0048969720302758
- Galán-Freyle, N. J., Ospina-Castro, M. L., Medina-González, A. R., Villarreal-González, R., Hernández-Rivera, S. P. & Pacheco-Londoño, L. C. (2020). Artificial Intelligence Assisted Mid-Infrared Laser Spectroscopy In Situ Detection of Petroleum in Soils. Applied Sciences, 10, 1319. Available: https://www.mdpi.com/2076-3417/10/4/1319
- Contreras, J. & Bocklitz, T. (2024). Explainable artificial intelligence for spectroscopy data: a review. Pflügers Archiv - European Journal of Physiology.10.1007/s00424-024-02997-y. Available: https://doi.org/10.1007/s00424-024-02997-y
Further Reading