Chroma Fusion: Integrating AI with Chromatography for Advanced Analysis

S. Archana, B. Poornima, G. Bhavani,

doi:10.5281/zenodo.15645988

Review Paper | Open Access
Volume 03 | Issue 06 | Article Id IJPS/250306168

Chroma Fusion: Integrating AI with Chromatography for Advanced Analysis
S. Archana* B. Poornima G. Bhavani
Department of Pharmaceutical Analysis, Vijaya Institute of Pharmaceutical Sciences for Women, Enikepadu, Vijayawada, AP

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) are transforming analytical chemistry, especially chromatographic techniques. AI contributes to enhancing method development, optimizing separation conditions, and automating data analysis. This review explores recent advances in the application of AI in chromatography, including retention time prediction, peak detection, data alignment, and integration with chemometric models. Challenges such as data quality and model interpretability are discussed, along with future directions that highlight AI’s role in achieving smart, automated chromatographic systems. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into chromatography has transformed the field of analytical chemistry, offering unprecedented opportunities for method development, optimization, and data analysis. This review provides a comprehensive overview of the recent advances in AI applications in chromatography, highlighting the potential of AI-driven approaches to enhance the efficiency, accuracy, and robustness of chromatographic analyses. In addition, AI-driven data analysis has revolutionized the field, enabling automated peak detection, integration, and quantification. Advanced ML algorithms, such as deep learning and convolutional neural networks, have been applied to chromatographic data, allowing for the extraction of valuable insights and patterns that may have gone undetected using traditional data analysis methods.

Keywords

Artificial Intelligence, Machine Learning, Chromatography, Method Development, Optimization, Data Analysis, Predictive Modeling

Introduction

Chromatography is a core analytical technique in pharmaceutical, environmental, and food analysis. It enables the separation and quantification of compounds in complex mixtures. Traditionally, chromatographic method development and data interpretation require significant expertise and are time-intensive. AI technologies such as neural networks, support vector machines (SVM), and genetic algorithms now offer data-driven solutions to automate and optimize various stages of chromatographic analysis. It might be difficult to consistently get accurate findings from the laborious, time-consuming, and error-prone process of chromatography data analysis.
This is the point at which chromatography analysis can be greatly enhanced by artificial intelligence (AI). AI is capable of perceiving, reasoning, and learning—tasks that normally require human intelligence. Chromatography analysis can be automated, simplified, and improved in accuracy and efficiency by utilizing AI approaches. Creating machine learning models that can forecast the characteristics of unknown samples based on historical data is one method artificial intelligence (AI) can be used in chromatography analysis. For instance, a machine learning model can be taught to identify the peaks that correspond to each chemical and forecast its characteristics, including molecular weight, polarity, and solubility, if a chromatography apparatus is used to separate a mixture of compounds. This can improve the quality and dependability of the results while drastically cutting down on the time and effort needed to interpret chromatography data. The creation of automated systems that can optimize the chromatography process is another area in which artificial intelligence (AI) can be useful in chromatography analysis. For instance, in order to attain the best resolution and sensitivity, AI algorithms can be used to construct the ideal separation parameters, including the selection of the stationary phase, mobile phase, and gradient elution conditions. Additionally, AI can be used to continuously monitor and manage the chromatographic process, modifying the settings as necessary to guarantee peak performance. AI can also be used to create data processing algorithms that can sift through the massive volumes of chromatography data produced in contemporary analytical labs and extract relevant information. AI algorithms, for instance, can be used to find patterns and trends in data, including relationships between the characteristics of various chemicals or variations in a mixture's composition over time. This can assist detect any pollutants or impurities and offer insightful information about the sample's underlying chemistry. Because AI makes data processing faster, more precise, and more efficient, it has the potential to completely transform the field of chromatographic analysis. AI can save time and lower the possibility of human error by automating the chromatography analysis process, freeing up scientists to work on more difficult and sophisticated analytical issues. This review also explores the applications of AI in various chromatographic techniques, including gas chromatography, liquid chromatography, and mass spectrometry. Case studies and examples are provided to illustrate the successful implementation of AI-driven approaches in real-world chromatographic applications, such as pharmaceutical analysis, environmental monitoring, and food safety testing. The future perspectives of AI-chromatography integration are also discussed, including the potential for real-time monitoring, smart chromatography, and the development of hybrid AI-chromatography systems. By providing a detailed examination of the current state of AI in chromatography, this review aims to inspire further innovation and collaboration between analytical chemists and AI researchers, ultimately enhancing the efficiency and accuracy of chromatographic analyses.

2. AI Applications in Chromatography

2.1 Retention Time Prediction

Retention time is a critical factor in chromatographic separation. AI models, especially Artificial Neural Networks (ANNs), have shown improved predictive capabilities over traditional regression-based models by learning non-linear relationships between molecular structure and retention behavior. QSRR models combined with machine learning can accurately estimate retention for unknown compounds, reducing the experimental load in method development. Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), has shown great promise in modeling the complex, nonlinear relationships involved in retention time prediction. AI models learn patterns from experimental data and molecular descriptors to make accurate predictions.

Machine Learning Approaches

These include supervised ML algorithms trained on large datasets of compounds with known retention times.

Commonly Used Algorithms:

Random Forest (RF): Robust to overfitting, handles high-dimensional data
Support Vector Machines (SVM): Effective in high-dimensional spaces
Gradient Boosting Machines (e.g., XGBoost, LightGBM): Excellent performance on structured data
k-Nearest Neighbors (kNN): Simple and intuitive for small datasets

Workflow:

Data Collection: Chromatographic conditions + chemical structures + experimental RTs
Feature Extraction: Molecular descriptors (e.g., logP, topological polar surface area), or fingerprints (e.g., ECFP, MACCS)
Model Training: Fit ML models to learn the mapping from molecular descriptors to RT
Validation: Evaluate models using cross-validation (e.g., R², RMSE, MAE)
Prediction: Apply trained model to new compounds

Example Tools and Libraries:

RDKit (for descriptor calculation)
scikit-learn
XGBoost
WEKA

Deep Learning Approaches

DL models, especially Neural Networks (NN), offer higher accuracy by automatically learning complex features from raw data.

Types of DL Models:

Fully Connected Neural Networks (FCNN): Use traditional molecular descriptors
Graph Neural Networks (GNN): Directly operate on molecular graphs without predefined descriptors
Convolutional Neural Networks (CNN): Can be applied to molecular images or 2D representations
Recurrent Neural Networks (RNN): Useful for SMILES-based sequence learning

Key Libraries:

TensorFlow / PyTorch
DeepChem
Chemprop (for molecular property prediction)
DGL (Deep Graph Library)

Hybrid and Transfer Learning Models

These combine traditional ML/DL with domain-specific knowledge (e.g., chromatographic theory). Transfer learning involves pre-training a model on a large dataset and fine-tuning it on a specific chromatographic method.

AI-Based Software and Platforms

Several platforms integrate AI models for RT prediction:

AutoRT: A deep learning-based RT predictor using SMILES strings
RT-Transformer: Combines molecular graph embeddings with transformer architectures
RetentionTime.org: An online tool for QSRR modeling and prediction
OpenMS and KNIME workflows: Provide modular tools for integrating AI in chromatography pipelines

2.2 Peak Detection and Alignment

Peak detection and alignment are crucial for accurate quantification and identification. Algorithms such as Chrom Align Net use deep learning to identify and align peaks across multiple chromatograms, accounting for retention time drifts and shape distortions. These models outperform classical algorithms in noisy or highly complex datasets. In chromatographic analysis, peak detection and alignment are fundamental processes that ensure the accurate interpretation of complex chemical mixtures. Peak detection involves identifying the retention times at which chemical components elute from the chromatographic column, while alignment ensures that peaks corresponding to the same compound are consistently matched across multiple chromatograms. These tasks are vital in fields like metabolomics, proteomics, pharmaceuticals, and environmental monitoring, where even minor deviations in chromatographic data can lead to significant analytical errors.

Traditionally, peak detection and alignment have relied on rule-based algorithms and manual curation. These conventional methods often involve setting thresholds for signal intensity, retention time windows, or peak shape criteria. However, these approaches are sensitive to noise, baseline drifts, and peak overlaps. As the complexity and volume of chromatographic data have increased—especially with high-throughput techniques like LC-MS (liquid chromatography-mass spectrometry)—these traditional approaches have become inadequate. This is where Artificial Intelligence (AI), particularly machine learning and deep learning, has emerged as a transformative tool. In AI-based peak detection, models are trained to distinguish true chromatographic peaks from noise and artifacts. This is achieved using annotated datasets where known peaks have been labeled. Machine learning models such as support vector machines (SVMs), decision trees, and random forests can learn patterns associated with valid peaks, including their intensity, width, shape, and symmetry. More advanced deep learning models, such as convolutional neural networks (CNNs), can process raw chromatographic signals and automatically learn abstract representations that distinguish real peaks from background noise. CNNs are especially powerful for recognizing subtle patterns and resolving closely eluting or overlapping peaks, which are difficult to detect using rule-based methods. Another significant application of AI in chromatography is peak alignment. Chromatographic retention times often vary due to fluctuations in experimental conditions such as temperature, pressure, or mobile phase composition. These shifts make it difficult to compare chromatograms directly, especially in studies involving large sample batches. AI models can learn to correct these shifts by recognizing shared patterns between chromatograms. Techniques like dynamic time warping (DTW), when integrated with AI, can provide more accurate, non-linear alignment by considering both the shape and location of peaks. Furthermore, deep learning models can be trained to predict alignment functions directly, learning from large datasets of chromatograms where true peak correspondences are known. Beyond improving the accuracy of detection and alignment, AI also enhances the automation and scalability of chromatographic data processing. Traditional peak-picking workflows often require manual intervention, particularly for difficult datasets with high noise levels or non-standard peak shapes. AI can automate these tasks by continuously learning from expert-annotated data, reducing the reliance on manual curation and increasing throughput. Additionally, AI models can be adapted to different chromatographic methods (e.g., GC-MS, HPLC, UPLC) by fine-tuning them on domain-specific data. An exciting advancement is the integration of AI models into real-time chromatographic systems. Here, AI algorithms can process chromatographic data as it is generated, providing immediate feedback on peak quality and alignment. This is especially useful in process analytical technology (PAT) settings, where rapid decision-making is critical.

2.3 Method Optimization

AI assists in optimizing experimental parameters, such as mobile phase composition, flow rate, and temperature. Genetic algorithms and reinforcement learning can model and explore the parameter space to suggest optimal conditions that yield better resolution and analysis time. Chromatographic method optimization is a critical phase in analytical chemistry, aimed at refining various operational parameters to achieve optimal separation, resolution, and detection of analytes in a complex mixture. This process involves adjusting numerous variables—such as the composition of the mobile phase, flow rate, column temperature, gradient profiles, pH, and the type of stationary phase—so as to minimize analysis time while maximizing peak resolution and sensitivity. Traditionally, method development has relied heavily on trial-and-error approaches guided by expert judgment, heuristics, and empirical knowledge. However, this approach is not only time-consuming but also inefficient, especially in high-throughput or multi-analyte environments where the number of possible parameter combinations is vast. The integration of Artificial Intelligence (AI) into chromatographic method optimization is rapidly transforming the way these challenges are addressed. AI systems, particularly those involving machine learning (ML) and deep learning (DL), offer a data-driven approach to modeling the complex, often non-linear relationships between chromatographic parameters and separation performance. These models can learn from historical experiments or simulated data, and subsequently predict outcomes for new, untested method conditions. As a result, AI facilitates a more rational, efficient, and automated approach to method development. In the early stages of method optimization, AI can be used to identify initial parameter settings by analyzing existing data or literature. For instance, supervised ML algorithms such as random forests, support vector machines (SVMs), or artificial neural networks (ANNs) can predict which mobile phase solvents or column chemistries are likely to be successful for a given class of analytes. These predictions are based on the structural and physicochemical properties of the target compounds, such as polarity, molecular weight, pKa, or hydrophobicity. AI models can also incorporate metadata about instrument configurations and experimental conditions to enhance the accuracy of their recommendations. Once an initial set of conditions is chosen, AI can be employed to fine-tune the method through adaptive optimization algorithms. Techniques such as Bayesian optimization, genetic algorithms, and reinforcement learning are particularly effective in navigating the high-dimensional optimization space inherent to chromatography. Bayesian optimization, for instance, builds a probabilistic model of the objective function—such as a function that scores separation quality based on peak resolution and retention time—and iteratively proposes new experiments that are most likely to improve performance. This dramatically reduces the number of experiments required compared to exhaustive grid searches. AI also excels in modelling multi-objective optimization, where trade-offs must be balanced—such as maximizing resolution while minimizing runtime or solvent consumption. These objectives often conflict, and traditional approaches require manual compromise. AI-driven methods can generate Pareto optimal solutions, providing analysts with a set of equally valid options that balance the competing goals in different ways. This empowers more informed decision-making, particularly in regulated industries like pharmaceuticals, where efficiency must coexist with regulatory compliance and robustness. Moreover, AI can be integrated into closed-loop experimental platforms, where the optimization process becomes fully automated. In such systems, AI algorithms analyse data from real-time chromatographic runs, update their predictive models, and autonomously suggest the next set of conditions to test. This enables continuous learning and refinement, significantly accelerating method development. Some advanced platforms combine chromatography instruments with robotic autosamplers and cloud-based AI systems to create an end-to-end automated method development pipeline. Beyond technical optimization, AI contributes to method robustness and transferability. By analyzing how small changes in parameters affect method performance, AI models can identify conditions that are less sensitive to variability—ensuring that the method performs consistently across different instruments, laboratories, or operators. This is especially critical in quality control settings, where method reproducibility is paramount. The use of AI in chromatographic method optimization represents a paradigm shift from manual, experience-driven development to intelligent, data-driven design. By learning from data and simulating experimental outcomes, AI enables faster, more precise, and more robust method development. It minimizes trial-and-error, enhances reproducibility, supports regulatory compliance, and opens the door to full automation. As datasets grow and algorithms become more sophisticated, AI’s role in chromatography will only deepen, ultimately enabling the design of methods that are not only optimized but also self-optimizing in response to changing analytical challenges.

2.4 Data Interpretation and Signal Processing

Deep learning models like CNNs are employed to interpret chromatograms by identifying patterns, deconvoluting overlapping peaks, and correcting baseline drifts. This reduces human bias and variability in manual peak integration. In chromatography, the accurate interpretation of data and effective signal processing are pivotal for obtaining meaningful analytical results. Chromatographic techniques produce complex datasets, often consisting of chromatograms that represent the separation of chemical mixtures over time. These chromatograms exhibit peaks corresponding to different components, and the quality of data interpretation directly influences the reliability of identification, quantification, and characterization of analytes. Artificial Intelligence (AI) has revolutionized the way chromatographic data is processed and interpreted. Traditional manual methods of peak detection and analysis can be time-consuming and prone to human error, especially when chromatograms contain overlapping peaks, baseline drifts, noise, or artifacts. AI-driven approaches leverage machine learning algorithms, neural networks, and advanced signal processing techniques to enhance the accuracy, efficiency, and automation of data interpretation. AI systems begin by preprocessing raw chromatographic signals to improve signal quality. This involves baseline correction, noise reduction, and smoothing to isolate true analytical peaks from background interference. Techniques such as wavelet transforms and adaptive filtering are employed to enhance signal clarity. AI models trained on large datasets can automatically distinguish between noise and valid peaks, reducing false positives and negatives. Once preprocessing is complete, AI algorithms perform peak detection and deconvolution. For example, deep learning models can be trained to recognize complex peak shapes and resolve overlapping peaks that traditional software might misinterpret. By learning from annotated chromatograms, these models predict peak start and end points with high precision. This leads to more accurate quantification of compounds even in challenging matrices. Beyond peak identification, AI also assists in peak alignment across multiple chromatograms. Variability in retention times due to slight differences in experimental conditions often complicates comparative studies. AI-powered alignment algorithms match peaks from different runs, ensuring consistency and facilitating batch analysis and quality control. Furthermore, AI enhances the interpretation of chromatographic data by linking signal features with chemical properties or biological relevance. For example, AI can integrate chromatographic data with chemometric analysis to classify samples, predict molecular structures, or detect anomalies such as contaminants or degradation products. This multi-dimensional data interpretation transforms chromatography from a purely analytical technique into a powerful tool for decision-making in research, pharmaceuticals, environmental monitoring, and food safety. AI applications in data interpretation and signal processing have transformed chromatography by automating complex tasks, improving accuracy, and enabling deeper insights. The integration of AI enables chromatographers to handle vast datasets with confidence, optimize analytical methods, and accelerate scientific discovery.

3. Integration with Chemometrics

The hybrid use of AI with chemometric techniques—such as Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR)—enhances multivariate data interpretation. AI enables real-time prediction of chromatographic behavior based on structural or spectral data, a technique increasingly used in metabolomics and proteomics.

Integrating AI with chemometrics allows for more intelligent, automated, and robust interpretation of chromatographic data.

A. Data Preprocessing and Cleaning

AI algorithms can detect and correct issues such as:
- Baseline drift
- Peak overlap or distortion
- Retention time shifts
Deep learning models like Autoencoders can denoise complex signals.

B. Peak Detection and Deconvolution

Traditional peak picking methods struggle with co-eluting peaks.
AI models (e.g., CNNs) can detect and deconvolve overlapping peaks more accurately than classical chemometric methods.
Example: Use of 1D-CNNs to process raw chromatograms for peak identification.

C. Feature Extraction and Dimensionality Reduction

PCA and t-SNE are often used to reduce dimensionality.
AI can enhance this step by:
- Extracting non-linear patterns missed by linear methods
- Learning data representations automatically

D. Classification and Sample Discrimination

AI-enhanced chemometric models classify samples based on chromatographic fingerprints:
- Food authentication (e.g., honey, olive oil)
- Quality control in pharmaceuticals
- Environmental pollutant detection
Techniques: Random Forest, SVM, ANN, LSTM

E. Quantitative Analysis

Traditional calibration models (PLS, MLR) can be improved using:
- Support Vector Regression (SVR)
- XGBoost
- Deep Neural Networks
These offer better generalization in non-linear or noisy systems.

F. Real-Time Process Monitoring (PAT)

AI-chemometrics is integral to Process Analytical Technology (PAT).
Real-time chromatographic data is processed using AI models to:
- Monitor critical quality attributes (CQAs)
- Predict batch outcomes
- Control process parameters dynamically

Out comes:

i. Food Authentication

AI-chemometrics used to classify different brands or origins of olive oil using GC-MS or HPLC-DAD chromatograms.

ii. Pharmaceutical Quality Control

PLS combined with deep neural networks to predict drug content or impurities based on HPLC data.

iii. Environmental Monitoring

AI models trained on chromatographic profiles to detect pesticides or pollutants in soil and water samples.

Benefits of AI-Chemometric Integration

Higher Accuracy in peak resolution and compound identification
Faster Analysis through automation and real-time processing
Better Generalization across instruments, batches, or noise
Robustness against overlapping peaks and baseline fluctuations
Enhanced Interpretability when combined with explainable AI

Challenges and Considerations

Data Quality: AI models require high-quality, labelled data
Transferability: Models must be retrained for different instruments or methods.
Interpretability: Deep models may lack transparency.
Model Validation: Critical for regulatory environments.

4. Challenges in AI Adoption

Chromatography, a critical analytical technique in chemistry, pharmaceuticals, environmental science, and many other fields, has seen increasing interest in incorporating Artificial Intelligence (AI) to improve efficiency, accuracy, and automation. However, despite the promising potential of AI, several challenges hinder its widespread adoption in chromatography:

1. Complexity and Variability of Chromatographic Data

Chromatographic data is often highly complex and variable. Factors such as retention times, peak shapes, and baseline noise can fluctuate due to changes in experimental conditions, column aging, mobile phase composition, and sample matrix complexity. This variability challenges AI models to generalize well and accurately interpret signals across different setups without overfitting to specific datasets.

AI models need to handle noisy, overlapping peaks and subtle baseline drifts, which require robust pre-processing and feature extraction.
Differences in chromatographic instruments and methods make it difficult to create universal AI models applicable across platforms.

2. Data Quality and Quantity

AI systems require large volumes of high-quality labeled data to train effectively. In chromatography:

Generating extensive datasets with accurate annotations (e.g., peak identification, compound labels) is labor-intensive and costly.
In some cases, proprietary or sensitive data restricts data sharing, limiting the availability of comprehensive datasets.
Experimental inconsistencies and errors in manual peak integration lead to noisy training labels, impacting model performance.

3. Lack of Standardization

The lack of standardized data formats and reporting protocols in chromatography hinders AI integration:

Different instruments and vendors produce data in diverse formats, requiring complex data harmonization.
Absence of universal protocols for sample preparation, method conditions, and peak reporting complicates AI model training and validation across datasets.

4. Interpretability and Trust

AI models, especially deep learning networks, often act as "black boxes," making it difficult for chromatographers to understand how decisions are made:

Lack of interpretability creates mistrust in automated AI-based decisions like peak detection, identification, or quantification.
Regulatory environments in pharmaceuticals and food industries demand explainability for analytical results, limiting AI adoption where transparency is critical.

5. Integration with Existing Workflows

Chromatography labs have well-established workflows involving hardware, software, and human expertise:

Integrating AI tools into existing chromatographic systems and Laboratory Information Management Systems (LIMS) can be technically challenging.
AI solutions must be user-friendly and compatible with legacy software/hardware to facilitate adoption by analysts without extensive retraining.
Resistance to change from trained personnel accustomed to manual or traditional automated methods slows AI acceptance.

6. Computational and Technical Resource Requirements

Some advanced AI models, especially deep learning architectures, require significant computational power for training and inference:

Smaller laboratories may lack the infrastructure to deploy such AI systems efficiently.
Real-time chromatographic analysis demands fast processing, which may not be feasible with complex AI models without optimization.

7. Regulatory and Validation Challenges

Chromatographic analysis in regulated industries must meet stringent validation criteria:

AI models must be rigorously validated for accuracy, reproducibility, and robustness before deployment.
Validation protocols for AI tools are not yet standardized, creating regulatory uncertainty.
Ensuring compliance with guidelines such as those from the FDA or EMA requires significant effort and documentation.

8. Data Privacy and Security

For pharmaceutical and clinical chromatography, data privacy is paramount:

Sharing chromatographic data for AI training may risk exposing proprietary or patient-sensitive information.
Ensuring secure data handling and anonymization techniques is crucial but adds complexity.

Despite promising results, challenges include:

Data scarcity and heterogeneity: High-quality, labeled chromatographic datasets are essential but scarce.
Model transparency: Many deep learning models lack interpretability, limiting their acceptance in regulatory settings.
Validation and standardization: Regulatory approval for AI-assisted methods requires standard validation guidelines, which are still evolving.

5. Future Perspectives

The future of AI in chromatography is promising. Potential advancements include:

Autonomous platforms for method development.
Integration with Internet of Things (IoT) devices for real-time feedback control.
Cloud-based AI services enabling shared model development and validation across laboratories.

anced Automation and Intelligent Instrument Control

Future chromatography systems will increasingly incorporate AI-driven automation, reducing manual intervention:

AI algorithms will dynamically optimize chromatographic parameters (e.g., flow rates, temperature gradients, solvent composition) in real time based on ongoing data analysis.
Intelligent instrument control will enable adaptive methods that respond to sample complexity, improving separation quality and throughput.
Automated troubleshooting and predictive maintenance guided by AI will reduce instrument downtime and improve lab productivity.

2. Improved Data Interpretation and Peak Analysis

AI will evolve to provide more sophisticated data interpretation capabilities:

Advanced deep learning models will accurately deconvolute complex overlapping peaks, identify trace impurities, and quantify components with minimal human input.
AI will integrate multi-dimensional data (e.g., retention time, spectral information, mass spectrometry) for more confident compound identification.
Continuous learning models will improve performance over time by incorporating new chromatographic data and feedback from users.

3. Integration with Chemometrics and Multivariate Analysis

The synergy between AI and chemometrics will enable:

Better handling of complex sample matrices and co-eluting compounds through pattern recognition and predictive modeling.
Enhanced quality control by detecting subtle deviations in chromatographic profiles indicative of process changes or contamination.
Real-time monitoring and decision-making in pharmaceutical manufacturing and environmental analysis.

4. Personalized Chromatography Methods

AI will facilitate personalized and sample-specific chromatographic methods:

Machine learning models will predict optimal method conditions tailored to specific samples or batches, reducing method development time.
AI-driven simulations will model chromatographic behavior for novel compounds or formulations, accelerating research and development.

5. Cloud-Based AI Platforms and Collaborative Learning

The future will see cloud integration for chromatography-AI ecosystems:

Cloud platforms will enable sharing of large chromatographic datasets, allowing AI models to learn from diverse sources and improve robustness.
Collaborative AI models trained on global data will democratize access to cutting-edge analytical tools, benefiting smaller labs and developing regions.
Real-time remote monitoring and AI-assisted support services will enhance lab operations and troubleshooting.

6. Explainable AI and Regulatory Compliance

Future AI systems will prioritize transparency and regulatory acceptance:

Development of explainable AI (XAI) techniques will allow chromatographers and regulators to understand AI decision-making processes.
Standardized AI validation protocols tailored to chromatographic applications will facilitate regulatory approval and integration into GMP (Good Manufacturing Practice) workflows.
AI-driven documentation and reporting tools will ensure compliance with data integrity and audit trail requirements.

7. Integration with Other Analytical Technologies

AI will foster integrated multi-analytical platforms:

Combining chromatography with spectroscopy, mass spectrometry, and other techniques via AI will provide holistic sample characterization.
Multi-modal AI models will correlate data from different instruments to enhance compound identification and quantification accuracy.

8. Real-Time Process Analytical Technology (PAT)

In manufacturing and quality control, AI-enhanced chromatography will play a key role in PAT:

Real-time chromatographic data analyzed by AI will enable immediate process adjustments, ensuring consistent product quality.
AI-based predictive models will anticipate deviations and suggest corrective actions before defects occur.

9. Education and Skill Development

AI adoption will drive new training paradigms:

Chromatographers will increasingly learn AI and data science skills to harness new tools effectively.
Interactive AI tutors and virtual labs may facilitate hands-on learning and method optimization.

CONCLUSION

AI is redefining the landscape of chromatography by making processes faster, more accurate, and autonomous. Its integration into analytical laboratories offers potential for fully automated systems capable of intelligent decision-making, provided current challenges in data management and regulatory compliance are addressed.

CONFLICT OF INTERESTS:

Declared none.

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    32. Singh YR, Shah DB, Maheshwari DG, Shah JS, Shah S. Advances in AI-driven retention prediction for different chromatographic techniques: unraveling the complexity. Crit Rev Anal Chem. 2023 Aug 31:1.
    33. Aghili NS, Rasekh M, Karami H, Azizi V, Gancarz M. Detection of fraud in sesame oil with the help of artificial intelligence combined with chemometrics methods and chemical compounds characterization by gas chromatography-mass spectrometry. LWT. 2022 Sep 15; 167:113863.
    34. Leite MS, Santos MA, Costa EM, Balieiro A, Lima ÁS, Sanchez OL. Modeling of milk lactose removal by column adsorption using artificial neural networks: MLP and RBF. Chem Ind Chem Eng Q. 2019;25(4):369-82.
    35. Viejo CG, Fuentes S, Godbole A, Widdicombe B, Unnithan RR. Development of a low-cost e-nose to assess aroma profiles: an aIapplication to assess beer quality. Sens Actuators B. 2020 Apr 1; 308:127688.
    36. Warren Vega WM, Contreras Atrisco ZA, Ramirez Quezada MF, Romero Cano LA. A novel approach of artificial intelligence for the study of the relation of physicochemical profile and color acquired by Tequila 100% agave in its maturation process. J Food Compos Anal. 2023 Oct 1; 123:105533.
    37. Ghali UM, Usman AG, Chellube ZM, Degm MA, Hoti K. Advanced chromatographic technique for performance simulation of anti-Alzheimer agent: an ensemble machine learning approach. SN Appl Sci. 2020 Nov; 2:1-2.
    38. Satwekar A, Panda A, Nandula P, Sripada S, Govindaraj R, Rossi M. Digital by design approach to develop a universal deep learning AI architecture for automatic chromatographic peak integration. Biotechnol Bioeng. 2023 Apr 22;120(7):1822-43.
    39. Usman AG, Isik S, Abba SI, Mericli F. Artificial intelligence-based models for the qualitative and quantitative prediction of a phytochemical compound using HPLC method. Turk J Chem. 2020;44(5):1339-51.
    40. Liu D, Sun N. Prospects of artificial intelligence in the development of sustainable separation processes. Front Sustain. 2023; 4:1210209.
    41. Beal LD, Hill DC, Martin RA, Hedengren JD. Gekko optimization suite. Processes. 2018 Jul 31;6(8):106.

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Viejo CG, Fuentes S, Godbole A, Widdicombe B, Unnithan RR. Development of a low-cost e-nose to assess aroma profiles: an aIapplication to assess beer quality. Sens Actuators B. 2020 Apr 1; 308:127688.
Warren Vega WM, Contreras Atrisco ZA, Ramirez Quezada MF, Romero Cano LA. A novel approach of artificial intelligence for the study of the relation of physicochemical profile and color acquired by Tequila 100% agave in its maturation process. J Food Compos Anal. 2023 Oct 1; 123:105533.
Ghali UM, Usman AG, Chellube ZM, Degm MA, Hoti K. Advanced chromatographic technique for performance simulation of anti-Alzheimer agent: an ensemble machine learning approach. SN Appl Sci. 2020 Nov; 2:1-2.
Satwekar A, Panda A, Nandula P, Sripada S, Govindaraj R, Rossi M. Digital by design approach to develop a universal deep learning AI architecture for automatic chromatographic peak integration. Biotechnol Bioeng. 2023 Apr 22;120(7):1822-43.
Usman AG, Isik S, Abba SI, Mericli F. Artificial intelligence-based models for the qualitative and quantitative prediction of a phytochemical compound using HPLC method. Turk J Chem. 2020;44(5):1339-51.
Liu D, Sun N. Prospects of artificial intelligence in the development of sustainable separation processes. Front Sustain. 2023; 4:1210209.
Beal LD, Hill DC, Martin RA, Hedengren JD. Gekko optimization suite. Processes. 2018 Jul 31;6(8):106.

S. Archana

Corresponding author

Department of Pharmaceutical Analysis, Vijaya Institute of Pharmaceutical Sciences for Women, Enikepadu, Vijayawada, AP

B. Poornima

Co-author

Department of Pharmaceutical Analysis, Vijaya Institute of Pharmaceutical Sciences for Women, Enikepadu, Vijayawada, AP

G. Bhavani

Co-author

Department of Pharmaceutical Analysis, Vijaya Institute of Pharmaceutical Sciences for Women, Enikepadu, Vijayawada, AP

S. Archana*, B. Poornima, G. Bhavani, Chroma Fusion: Integrating AI with Chromatography for Advanced Analysis, Int. J. of Pharm. Sci., 2025, Vol 3, Issue 6, 2342-2358. https://doi.org/10.5281/zenodo.15645988

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Chroma Fusion: Integrating AI with Chromatography for Advanced Analysis

Abstract

Keywords

Introduction

Reference

S. Archana

B. Poornima

G. Bhavani

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