Recent Applications of Mixture Designs in Beverages, Foods, and Pharmaceutical Health: A Systematic Review and Meta-Analysis.

Design of Experiments (DoE) is a statistical tool used to plan and optimize experiments and is seen as a quality technology to achieve products excellence. Among the experimental designs (EDs), the mixture designs (MDs) stand out, being widely applied to improve conditions for processing, developing, or formulating novel products. This review aims to provide useful updated information on the capacity and diversity of MDs applications for the industry and scientific community in the areas of food, beverage, and pharmaceutical health. Recent works were selected following the Preferred Reporting Items for Systematic Review and Meta-Analyses statement (PRISMA) flow diagram. Data analysis was performed by self-organizing map (SOM) to check and understand which fields of application/countries/continents are using MDs. Overall, the SOM indicated that Brazil presented the largest number of works using MDs. Among the continents, America and Asia showed a predominance in applications with the same amount of work. Comparing the MDs application areas, the analysis indicated that works are prevalent in food and beverage science in the American continent, while in Asia, health science prevails. MDs were more used to develop functional/nutraceutical products and the formulation of drugs for several diseases. However, we briefly describe some promising research fields in that MDs can still be employed.

The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks.

Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country's measures, which were implemented to contain the virus' spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus' spread in these cities, states, and regions.

Can Socioeconomic, Health, and Safety Data Explain the Spread of COVID-19 Outbreak on Brazilian Federative Units?

Infinite factors can influence the spread of COVID-19. Evaluating factors related to the spread of the disease is essential to point out measures that take effect. In this study, the influence of 14 variables was assessed together by Artificial Neural Networks (ANN) of the type Self-Organizing Maps (SOM), to verify the relationship between numbers of cases and deaths from COVID-19 in Brazilian states for 110 days. The SOM analysis showed that the variables that presented a more significant relationship with the numbers of cases and deaths by COVID-19 were influenza vaccine applied, Intensive Care Unit (ICU), ventilators, physicians, nurses, and the Human Development Index (HDI). In general, Brazilian states with the highest rates of influenza vaccine applied, ICU beds, ventilators, physicians, and nurses, per 100,000 inhabitants, had the lowest number of cases and deaths from COVID-19, while the states with the lowest rates were most affected by the disease. According to the SOM analysis, other variables such as Personal Protective Equipment (PPE), tests, drugs, and Federal funds, did not have as significant effect as expected.

Mathematical modeling of multicomponent NaCl and KCl diffusion process during the salting of pre-cooked champignon mushrooms.

Wet salting is an old preservation method however the NaCl has been frequently associated to high blood pressure issues. The KCl has been used as partial replacement without affecting the acceptability of the final product. In this study, the simultaneous diffusion of NaCl and KCl in champignon mushrooms in static brine and brine under agitation was modeled based on the generalization of Fick's second diffusion law and simulated using the finite element method. Main and crossed diffusion coefficients, as well as the Biot number, were determined using the super modified simplex optimization method by means of percentage error minimization. Deviations between the simulated and experimental data were 5.78% for NaCl and 4.78% for KCl in static brine and 4.95% for NaCl and 4.35% for KCl in brine with agitation and, the main diffusion coefficients 2.692 × 10-10 m2s-1 for sodium and 2.953 × 10-10 m2s-1 for potassium were obtained.

Application of self-organising maps towards segmentation of soybean samples by determination of amino acids concentration.

Soybeans are widely used both for human nutrition and animal feed, since they are an important source of protein, and they also provide components such as phytosterols, isoflavones, and amino acids. In this study, were determined the concentrations of the amino acids lysine, histidine, arginine, asparagine, glutamic acid, glycine, alanine, valine, isoleucine, leucine, tyrosine, phenylalanine present in 14 samples of conventional soybeans and 6 transgenic, cultivated in two cities of the state of Paraná, Londrina and Ponta Grossa. The results were tabulated and presented to a self-organising map for segmentation according planting regions and conventional or transgenic varieties. A network with 7000 training epochs and a 10 × 10 topology was used, and it proved appropriate in the segmentation of the samples using the data analysed. The weight maps provided by the network, showed that all the amino acids were important in targeting the samples, especially isoleucine. Three clusters were formed, one with only Ponta Grossa samples (including transgenic (PGT) and common (PGC)), a second group with Londrina transgenic (LT) samples and the third with Londrina common (LC) samples.

Application of self-organising maps towards segmentation of soybean samples by determination of inorganic compounds content.

BACKGROUND: In this study, 20 samples of soybean, both transgenic and conventional cultivars, which were planted in two different regions, Londrina and Ponta Grossa, both located at Paraná, Brazil, were analysed. In order to verify whether the inorganic compound levels in soybeans varied with the region of planting, K, P, Ca, Mg, S, Zn, Mn, Fe, Cu and B contents were analysed by an artificial neural network self-organising map. RESULTS: It was observed that with a topology 10 × 10, 8000 epochs, initial learning rate of 0.1 and initial neighbourhood ratio of 4.5, the network was able to differentiate samples according to region of origin. Among all of the variables analysed by the artificial neural network, the elements Zn, Ca and Mn were those which most contributed to the classification of the samples. CONCLUSION: The results indicated that samples planted in these two regions differ in their mineral content; however, conventional and transgenic samples grown in the same region show no difference in mineral contents in the grain.