Causal Artificial Intelligence Models of Food Quality Data.

Research Background: The aim of this study is to emphasize the importance of artificial intelligence (AI) and causality modelling of food quality and analysis with 'big data'. AI with structural causal modelling (SCM), based on Bayesian networks and deep learning, enables the integration of theoretical field knowledge in food technology with process production, physicochemical analytics and consumer organoleptic assessments. Food products have complex nature and data are highly dimensional, with intricate interrelations (correlations) that are difficult to relate to consumer sensory perception of food quality.

Application of Causality Modelling for Prediction of Molecular Properties for Textile Dyes Degradation by LPMO.

The textile industry is one of the largest water-polluting industries in the world. Due to an increased application of chromophores and a more frequent presence in wastewaters, the need for an ecologically favorable dye degradation process emerged. To predict the decolorization rate of textile dyes with Lytic polysaccharide monooxygenase (LPMO), we developed, validated, and utilized the molecular descriptor structural causality model (SCM) based on the decision tree algorithm (DTM).

New approach to the evaluation of lignocellulose derived by-products impact on lytic-polysaccharide monooxygenase activity by using molecular descriptor structural causality model.

Lytic-polysaccharide monooxygenase (LPMO) is one of the most important enzyme involved in biocatalytic lignocellulose degradation, and therefore inhibition of LPMO has significant effects on all related processes. Structural causality model (SCM) were established to evaluate impact of phenolic by-products in lignocellulose hydrolysates on LPMO activity. The molecular descriptors GATS4c, ATS2m, BIC3 and VR2_Dzs were found to be significant in describing inhibition.

Current state and prospects of biotechnology in Central and Eastern European countries. Part II: new and preaccession EU countries(CRO, RO, B&H, SRB).

Innovation holds the potential for economic prosperity. Biotechnology (BT) has proved to be a viable vehicle for the development and utilization of technologies, which has brought not only advances to society, but also career opportunities to nation-states that have enabling conditions. In this review, we assess the current state of BT-related activities within selected new and preaccession EU countries (NPA) of CEE region namely Croatia, Romania, Bosnia and Herzegovina and Serbia, examining educational programs, research activity, enterprises, and the financing systems.

Near-Infrared Spectroscopic Analysis of Total Phenolic Content and Antioxidant Activity of Berry Fruits.

This study evaluates the feasibility of using near-infrared (NIR) spectroscopy as a rapid and environmentally friendly technique for validation and prediction of the total phenolic content (TPC) and antioxidant activity (AOA) indices (as 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging, inhibition time (IT) of the Briggs-Rauscher oscillating reaction, and relative antioxidant capacity (RAC)) of berry fruit extracts. The analysed berry samples originated from Croatia (blackberries, wild blueberries, raspberries, red currants and strawberries) and Bulgaria (wild blueberries, raspberries and strawberries). Principal component analysis and partial least squares (PLS) regression were used from the set of chemometric tools in distinguishing and validating the measured berry fruit extract.