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Brazil annually produces around 43 million tons of fruits and vegetables. Therefore, large amounts of pesticides are needed to grow these foods. The use of unauthorized or indiscriminate pesticides can lead to the adherence of residues of these compounds to the product in a concentration above the maximum residue limit (MRL). Pesticide residues (PRs) monitoring is a continuous challenge due to several factors influencing the detection of these compounds in the food matrix. Currently, several adaptations to conventional techniques have been developed to minimize these problems. This systematic review presents the main information obtained from 52 research articles, taken from five databases, on changes and advances in Brazil in sample preparation methods for determining PRs in fruits and vegetables in the last nine years. We cover the preexisting ones and some others that might be suitable alternatives approaches. In addition, we present a brief discussion on the monitoring of PRs in different Brazilian regions, and we found that residues belonging to the organophosphate and pyrethroid classes were detected more frequently. Approximately 67% of the residues detected are of irregular use in 28 types of fruits and vegetables commonly consumed and exported by Brazil.
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Resíduos de Praguicidas , Praguicidas , Resíduos de Praguicidas/análise , Verduras/química , Frutas/química , Brasil , Praguicidas/análise , Contaminação de Alimentos/análiseRESUMO
This work proposes a novel technology for environmental remediation based on mesoporous silica spheres, which were successfully synthesized by the solvothermal method using the cetyltrimethylammonium bromide as a structuring agent. The adsorbent was designed to remove cationic dyes at strong acidic conditions. The surface was modified by a careful thermal treatment aiming at the condensation of silanol to siloxane groups. The adsorbent was characterized by XRD, SEM, FTIR, N2 adsorption/desorption and the equilibrium technique to determine the pHpzc. The kinetic of the adsorption followed a pseudo-second-order model and the process was ruled by physical forces. The isotherms were fitted to Freundlich and Temkin models, indicating that the physisorption occurred with multilayer formation, with the interaction adsorbate-adsorbate being relevant to the whole process. The adsorption capacity was approximately 60â mgâ g-1 and the adsorbents performance in the fast-contact system showed removal of 65%wt. of a 93â mgâ L-1 methylene blue (MB) solution in a single application.
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Azul de Metileno , Poluentes Químicos da Água , Adsorção , Dióxido de Silício , ÁguaRESUMO
Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed.
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Inteligência Artificial , Qualidade dos Alimentos , Algoritmos , Nariz Eletrônico , Humanos , Língua/fisiologiaRESUMO
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.
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The growing demand for authentic products that provide sensory characteristics combined with health benefits has been the focus of current studies. This study developed a Red Ale style craft beer with spices such as turmeric (T), black pepper (P) and aroma hops (H), used isolated or in mixtures. A mixture design was employed to evaluate the total phenolic compounds and the antioxidant activity in the green and aged beers formulations. The spice extracts influenced the product's shelf-life. The addition of spices into the beers did not affect the physicochemical parameters that classify the Red Ale style, according to the hierarchical cluster analysis, except for aroma hops. A multiresponse optimization approach simultaneously maximized the antioxidant activity and the phenolic compounds in beers. The ideal formulation obtained for green beers was 25% T and 37.5% P and H; for aged beers, the formulation was 50% T, 20% P and 30% H.
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Antioxidantes , Humulus , Antioxidantes/análise , Cerveja/análise , Fenóis/análise , EspeciariasRESUMO
Essential oils (EOs) are commercially important products, sources of compounds with antioxidant and antimicrobial activities considered indispensable for several fields, such as the food industry, cosmetics, perfumes, pharmaceuticals, sanitary and agricultural industries. In this context, this systematic review and meta-analysis, a novel approach will be presented using chemometric tools to verify and recognize patterns of antioxidant, antibacterial, and antifungal activities of EOs according to their geographic, botanical, chemical, and microbiological distribution. Scientific papers were selected following the Preferred Reporting Items for Systematic Review and Meta-Analyses statement flow diagram, and the data were evaluated by the self-organizing map and hierarchical cluster analysis. Overall, this novel approach allowed us to draw an overview of antioxidants and antimicrobials activities of EOs reported in 2019, through 585 articles evaluated, obtaining a dataset with more than 10,000 data, distributed in more than 80 countries, 290 plant genera, 150 chemical compounds, 30 genera of bacteria, and 10 genera of fungi. The networks for geographic, botanical, chemical, and microbiological distribution indicated that Brazil, Asia, the botanical genus Thymus, species Thymus vulgaris L. "thyme," the Lamiaceae family, limonene, and the oxygenated monoterpene class were the most representative in the dataset, while the species Escherichia coli and Candida albicans were the most used to assess the antimicrobial activity of EOs. This work can be seen as a guide for the processing of metadata using a novel approach with non-conventional statistical methods. However, this preliminary approach with EOs can be extended to other sources or areas of food science.
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Lamiaceae , Óleos Voláteis , Thymus (Planta) , Candida albicans , Testes de Sensibilidade Microbiana , Óleos Voláteis/farmacologiaRESUMO
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.
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COVID-19/epidemiologia , Redes Neurais de Computação , Aprendizado de Máquina não Supervisionado , Brasil/epidemiologia , COVID-19/mortalidade , COVID-19/transmissão , Humanos , SARS-CoV-2 , Análise Espaço-TemporalRESUMO
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.