AIMC Topic: Chromatography

Clear Filters Showing 1 to 10 of 24 articles

Evolution of chromatographic modeling: From mechanistic models to hybrid models with physics-based deep learning.

Journal of chromatography. A
Hybrid modeling based on physics-based deep learning (PBDL) represents a transformative approach that unifies mechanistic understanding and data-driven learning, offering a pathway beyond the limitations of traditional chromatographic models. This re...

Small sample data-driven interpretable artificial neural network computation for two-component chromatographic separation process.

Journal of chromatography. A
The design and calculation of chromatographic separation processes are often achieved by chromatographic models. When the adsorption mechanism is complex and the adsorption relationship is difficult to determine, the application effect of mechanism-d...

Recent Development of Methods and Techniques in the Detection of Mycotoxins in Agricultural Products.

Journal of agricultural and food chemistry
Mycotoxins are produced by fungi and possess cytotoxic properties that cause extensive cellular damage. Mycotoxins pose a significant threat to the harvesting and storage of crops as well as potential carcinogenic, teratogenic, and mutagenic risks to...

A parameter estimation method for chromatographic separation process based on physics-informed neural network.

Journal of chromatography. A
Chromatographic separation processes are most often modeled in the form of partial differential equations (PDEs) to describe the complex adsorption equilibria and kinetics. However, identifying parameters in such a model requires substantial computat...

Physics-informed neural networks to solve lumped kinetic model for chromatography process.

Journal of chromatography. A
Numerical method is widely used for solving the mechanistic models of chromatography process, but it is time-consuming and hard to response in real-time. Physics-informed neural network (PINN) as an emerging technology combines the structure of neura...

Advances in AI-Driven Retention Prediction for Different Chromatographic Techniques: Unraveling the Complexity.

Critical reviews in analytical chemistry
Retention prediction through Artificial intelligence (AI)-based techniques has gained exponential growth due to their abilities to process complex sets of data and ease the crucial task of identification and separation of compounds in most employed c...

Using artificial neural networks to accelerate flowsheet optimization for downstream process development.

Biotechnology and bioengineering
An optimal purification process for biopharmaceutical products is important to meet strict safety regulations, and for economic benefits. To find the global optimum, it is desirable to screen the overall design space. Advanced model-based approaches ...

Digital by design approach to develop a universal deep learning AI architecture for automatic chromatographic peak integration.

Biotechnology and bioengineering
Chromatographic data processing has garnered attention due to multiple Food and Drug Administration 483 citations and warning letters, highlighting the need for a robust technological solution. The healthcare industry has the potential to greatly ben...

Retention time prediction for small samples based on integrating molecular representations and adaptive network.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences
Retention time (RT) can provide orthogonal information different from that of mass spectrometry and contribute to identifying compounds. Many machine learning methods have been developed and applied to RT prediction. In application, the training data...