Neutron activation analysis and data mining techniques to discriminate between beef cattle diets

Mazola, Yuniel T.; Fernandes, Elisabete A. De Nadai; Sarriés, Gabriel Adrián; Bacchi, M. A.; Gonzaga, Cláudio L.

doi:10.1007/s10967-019-06874-2

Cited by 10 publications

(3 citation statements)

References 27 publications

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“…For example, INAA and data mining techniques were combined to assess the mineral composition of beef cattle feeds for discrimination purposes, in which Br, Ca, Cs, La, Sc, Se, Sr, Th, and Zn showed statistically significant differences. Here INAA shows 100% prediction accuracy of classification performances; INAA was used to characterize several essential, nonessential, and toxic elements (As, Ba, Br, Ce, Cl, Co, Cr, Cs, Eu, Fe, Hg, K, Mn, Na, Rb, Sb, Se and Zn, and Sc) in medicinal plant seeds for baseline data, which is helpful for in vitro and in vivo medicinal research . Besides, agri-food, biological tissue, soil, plastic, medicine, and atmospheric particulate samples were reported to be analyzed by INAA. − However, INAA instrumentation is too expensive to attain and complicated to operate due to the radiometric risk; at the same time, the analysis time of traditional reactor INAA (Re-INAA) is too long due to the elemental decay period.…”

Section: Sensitivity and Multielemental Analysis In Agrometallomicsmentioning

confidence: 99%

Analytical Methodologies for Agrometallomics: A Critical Review

Liu

Chang

et al. 2021

J. Agric. Food Chem.

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Agrometallomics, as an independent interdiscipline, is first defined and described in this review. Metallic elements widely exist in agricultural plants, animals and edible fungi, seed, fertilizer, pesticide, feedstuff, as well as the agricultural environment and ecology, and even functional and pathogenic microorganisms. So, the agrometallome plays a vital role in molecular and organismic mechanisms like environmetallomics, metabolomics, proteomics, lipidomics, glycomics, immunomics, genomics, etc. To further reveal the inner and mutual mechanism of the agrometallome, comprehensive and systematic methodologies for the analysis of beneficial and toxic metals are indispensable to investigate elemental existence, concentration, distribution, speciation, and forms in agricultural lives and media. Based on agrometallomics, this review summarizes and discusses the advanced technical progress and future perspectives of metallic analytical approaches, which are categorized into ultrasensitive and high-throughput analysis, elemental speciation and state analysis, and spatial- and microanalysis. Furthermore, the progress of agrometallomic innovativeness greatly depends on the innovative development of modern metallic analysis approaches including, but not limited to, high sensitivity, elemental coverage, and anti-interference; high-resolution isotopic analysis; solid sampling and nondestructive analysis; metal chemical species and metal forms, associated molecular clusters, and macromolecular complexes analysis; and metal-related particles or metal within the microsize and even single cell or subcellular analysis.

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Section: Sensitivity and Multielemental Analysis In Agrometallomicsmentioning

confidence: 99%

Analytical Methodologies for Agrometallomics: A Critical Review

Liu

Chang

et al. 2021

J. Agric. Food Chem.

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“…Mathematical details of these algorithms were reported [13,14]. The classification performance of the models was evaluated by accuracy, precision, sensitivity and specificity [15,16], here defined as…”

Section: Chemometric Analysismentioning

confidence: 99%

Discriminating Beef Producing Countries by Multi-Element Analysis and Machine Learning

Fernandes¹,

Mazola²,

Sarriés³

et al. 2021

AAIML

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The growing awareness of the environmental impact of beef production is greatly influencing the consumption decision. Beef production is strongly criticized due to the remarkable environmental impact of this activity, associated with problems of deforestation, water consumption, global warming, and climate change. Despite this, livestock food products play an important role in food security, accounting for 33% of global protein consumption. Enhancing the transparency of the beef production chain is essential to increase consumer perception about its origin, safety for consumption, environmental and human aspects.A study was undertaken to assess if beef samples from different producing countries can be distinguished from another on basis of their contents of chemical elements. Beef samples from some of the top world exporters, Brazil (1 st ), Australia (2 nd ), Argentina (5 th ), Uruguay (8 th ), and Paraguay (9 th ), were analyzed by neutron activation analysis for multi-element determination. Five machine learning algorithms, Classification and Regression Tree (CART), Multilayer Perceptron (MLP), Naive Bayes (NB), Random Forest (RF), and Sequential Minimal Optimization (SMO), were used to analyze the measurement results and classify the beef producing countries. MLP model provided the best classification performance, with an accuracy of 100%, 98%, 98%, 96%, and 82% respectively for Paraguay, Uruguay, Australia, Argentina, and Brazil. Reducing the number of classes (each country against the remaining countries), the accuracy achieved for the Brazilian beef samples was improved to 94% without changing the performance for other countries. Multi-element compositional data and machine learning algorithms allowed for discriminating beef producing countries, providing an outlook of becoming a valuable tool for geographical origin traceability and transparency.

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“…Neste caso, todas as MSD foram superiores às incertezas analíticas médias, mostrando a viabilidade do uso da técnica de análise por ativação neutrônica para obter o perfil agrometalômico da carne bovina para discriminação entre país de origem, região do Brasil, corte de carne bovina, sexo e grupo genético dos animais. Considerando que quanto maior a MSD maior é a diferença entre os grupos de dados que estão sendo comparados[182], pode-se concluir que, exceto para K, Na e Zn, a diferença entre picanha bovina procedente de diferentes regiões do Brasil (Goiás, Minas Gerais, Mato Grosso do Sul, Pará, Pernambuco, Rio Grande do Sul e São Paulo) é maior que a diferença entre picanha bovina procedente de Argentina, Austrália, Brasil, Paraguai e Uruguai.Para todos os elementos químicos, os cortes apresentaram maior variação das frações de massa do que sexo e grupo genético, sendo obtido para eles maior MSD e maior coeficiente de Cramer.…”

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Rastreabilidade da carne bovina brasileira avaliada por técnicas isotópicas e quimiométricas

Mazola¹

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Neutron activation analysis and data mining techniques to discriminate between beef cattle diets

Cited by 10 publications

References 27 publications

Analytical Methodologies for Agrometallomics: A Critical Review

Analytical Methodologies for Agrometallomics: A Critical Review

Discriminating Beef Producing Countries by Multi-Element Analysis and Machine Learning

Rastreabilidade da carne bovina brasileira avaliada por técnicas isotópicas e quimiométricas

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