Food structure and its effect on deformation behaviour and sensory perception is still a challenge in food science. Small-Amplitude Oscillatory Shear (SAOS) is a common method for the determination of the linear viscoelastic properties and stability of food materials, but it is not applicable to large deformations, such as those occurring during food processing and consumption. Large-Amplitude Oscillatory Shear (LAOS) extends this limitation by giving insights into the nonlinear phenomena like yielding, strain hardening, strain softening and structural recovery. In addition to these rheological methods, food tribology investigates friction and lubrication mechanisms at the contact surfaces of the mouth, including complementary methods that are related to the sensory attributes of creaminess, smoothness, roughness and astringency. This review suggests a common framework to incorporate the fields of SAOS, LAOS, and food tribology, to understand the relationship between structure-function-sensory in a wide variety of food systems. The latest analytical techniques like Fourier-transform rheology, Chebyshev polynomial analysis, Lissajous-Bowditch plots, biomimetic tribological techniques and machine-learning-based predictive tools are explained in detail. This approach is illustrated with examples of dairy products, plant-based protein foods, dysphagia-friendly formulations, reduced fat foods and 3D-printed foods. The review highlights that the use of nonlinear measurements of the rheological parameters is essential to augment the prediction of sensory properties of these materials in order to overcome limitations of the application of conventional linear rheology. Furthermore, the capabilities of AI and data modelling techniques for texture forecasting and food design are examined. In general, the proposed framework is useful for the predictive food engineering and the development of high quality, sustainable and consumer friendly food products.
Drug discovery is widely known to be an extremely complex and costly process due to its cost framework, long cycles, and high turnover rates. Robotics and Artificial Intelligence (AI) introduce the world to a disruptive, mechanized, and data analytics paradigm that can accelerate the initial stages of drug development.
accelerate the initial stages of drug development.
Materials and Methods
The results show that AI and robotics improve assay reproducibility, optimize chemical reactions, and enable the conversion of a hit into a lead. Automated procedures lead to greater data reliability, less experimental time and cost, and higher success rates. Opening new chemical and biological spaces that were previously unexplored, faster discovery timelines, and an even more efficient allocation of resources are demonstrated in both industrial and academic examples.
The collaboration of robotics, AIs, and human expertise creates a hybrid workflow, where routine work is automated, freeing investigators to focus on creative and decision-making work. The ongoing barriers include technical constraints, model interpretation issues, financial constraints, legal compliance and ethical considerations, and personnel adjustments.
The field of drug discovery is being radically transformed by robotics and AI, allowing scalable, efficient and innovative approaches. A trend towards fully autonomous, self-learning laboratories, including generative AI, quantum computing and digital biology, is anticipated, likely to develop new therapeutics faster, more accurately and at significantly lower cost.
Aadarsh Kumar, Md Moidul Islam, Abhishek Kumar et al.· Current Artificial Intellige...· 0 citations
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