Authentication of beef cuts by multielement and machine learning approaches.

BACKGROUND: Brazil has consolidated a relevant position in the world market, being the largest exporter and second producer of beef. Genetics, feeding system, geographic origin and climate influence the multielement profile of beef. The feasibility of combining classification algorithms with major and trace elements was evaluated as a tool for authentication of beef cuts. METHODS: Animals of Angus, Nelore and Wagyu crossbreeds, raised in a vertically integrated system, were sampled at the slaughterhouse for chuck steak, rump cap and sirloin steak. Supervised learning algorithms i.e. Classification and Regression Tree (CART), Multilayer Perceptron (MLP), Naïve Bayes (NB), Random Forest (RF) and Sequential Minimal Optimization (SMO) were used to build classification models based on the multielement profile of beef determined by neutron activation analysis. RESULTS: Br, Co, Cs, Fe, K, Na, Rb, Se and Zn were determined in the beef samples. The classification accuracy values obtained for the beef cuts were 96% (MLP), 95% (SMO), 91% (RF), 86% (NB) and 70% (CART). CONCLUSION: The Multilayer Perceptron algorithm provided the best classification performance towards authentication of beef cuts on basis of major and trace element mass fractions.

Elemental profile of dietary supplements and agricultural byproducts evaluated by neutron activation analysis.

Dietary supplements and agricultural byproducts were characterized by neutron activation analysis. The nutritional potential of supplements was evaluated according to alternative and commercial categories, using analysis of variance and cluster analysis, and recommended dietary intake for children. The results indicated statistically significant differences between both categories for the elements Cs, K, Na, and Rb. For the nutritional elements Ca, Co, Fe, K, Na, and Zn, the categories were similar in cluster analysis. The similarity between elemental profiles of alternative supplements and agricultural byproducts was calculated using a dissimilarity matrix, showing that rice and wheat are the predominant ingredients.

Trace elements and machine learning for Brazilian beef traceability.

Brazilian livestock with a herd of more than 215 million animals is distributed over a vast area of 160 million hectares, leading the country to the first position in the world beef exports and second in beef production and consumption. Animals risen in the biomes Amazônia, Caatinga, Cerrado, Pampa and Pantanal were selected for this study. Beef samples were analyzed for their elemental content by neutron activation analysis and classified according to their origin by three machine learning algorithms (Multilayer Perceptron, Random Forest and Classification and Regression Tree). Significant differences (p < 0.0001) were observed between the beef elemental content from the different biomes for all multivariate contrasts using NPMANOVA. The highest classification performance was obtained for the biomes Amazônia and Caatinga using Multilayer Perceptron. Results showed the feasibility of combining trace element content and machine learning approaches for the Brazilian beef traceability.

Elemental characterization of single-wall carbon nanotube certified reference material by neutron and prompt γ activation analysis.

Instrumental neutron activation analysis with both relative and k0 standardization was used in four experienced laboratories to determine element mass fractions in single-wall carbon nanotube certified reference material (CRM) SWCNT-1. Results obtained were evaluated using the National Institute of Standards and Technology (NIST) "Type B On Bias" approach and yielded consensus values in agreement with National Research Council Canada (NRCC) certified values for Fe, Co, Ni, and Mo and provided mass fraction values for 13 additional elements, namely, Na, Mg, Al, K, Ca, Ti, V, Cr, Mn, Br, La, W, and Au. In addition, prompt γ neutron activation analysis was employed to determine mass fractions of H, B, Co, Ni, and Mo. Results of this work provide a basis for the establishment of reference values of element mass fractions in CRM SWCNT-1, thus expanding its usability for more accurate characterization and benchmarking of similar nanotechnology materials.

Seleção de epífitas acumuladoras de elementos químicos na Mata Atlântica / Selection of epiphytes as accumulators of chemical elements in the Atlantic Forest

Epífitas são eficientes indicadores de poluição atmosférica devido à absorção de elementos químicos diretamente da atmosfera. Folhas de onze espécies de bromélias e uma espécie de orquídea foram coletadas no Parque Estadual Carlos Botelho, SP, para a determinação de dezesseis elementos químicos. A seleção foi baseada na diversidade e acumulação de elementos químicos encontrados nas folhas. Com um índice de acumulação de 0,7, a espécie Canistropsis billbergioides tem potencial para ser empregada como biomonitora de elementos químicos na Mata Atlântica.

Epiphytes are efficient indicators of atmospheric pollution because of their direct uptake of chemical elements from atmosphere. Leaves from eleven species of bromeliads and one species of orchid were collected in the Parque Estadual Carlos Botelho, SP, for determination of sixteen chemical elements. The selection of the best biomonitor species was based on the diversity and accumulation of chemical elements in the leaves. With an average accumulation index of 0.7, Canistropsis billbergioides has potential to be used as a biomonitor of chemical elements in the Atlantic Forest.