Data Science & Engineering into Food Science: A novel Big Data Platform for Low Molecular Weight Gelators’ Behavioral Analysis - Cuello, Verónica (2020)
The objective of this article is to introduce a comprehensive end-to-end solution aimed at enabling the application of state-of-the-art Data Science and Analytic methodologies to a food science related problem. The problem refers to the automation of load, homogenization, complex processing and real-time accessibility to low molecular-weight gelators (LMWGs) data to gain insights into their assembly behavior, i.e. whether a gel can be mixed with an appropriate solvent or not. Most of the work within the field of Colloidal and Food Science in relation to LMWGs have centered on identifying adequate solvents that can generate stable gels and evaluating how the LMWG characteristics can affect gelation. As a result, extensive databases have been methodically and manually registered, storing results from different laboratory experiments. The complexity of those databases, and the errors caused by manual data entry, can interfere with the analysis and visualization of relations and patterns, limiting the utility of the experimental work. Due to the above mentioned, we have proposed a scalable and flexible Big Data solution to enable the unification, homogenization and availability of the data through the application of tools and methodologies. This approach contributes to optimize data acquisition during LMWG research and reduce redundant data processing and analysis, while also enabling researchers to explore a wider range of testing conditions and push forward the frontier in Food Science research.