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      Milk fraud by the addition of whey using an artificial neural network Translated title: Identificação de fraude de leite por adição de soro de leite usando redes neurais artificiais

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          Abstract

          ABSTRACT: The adulteration of milk by the addition of whey is a problem that concerns national and international authorities. The objective of this research was to quantify the whey content in adulterated milk samples using artificial neural networks, employing routine analyses of dairy milk samples. The analyses were performed with different concentrations of whey (0, 5, 10, and 20%), and samples were analyzed for fat, non-fat solids, density, protein, lactose, minerals, and freezing point, totaling 164 assays, of which 60% were used for network training, 20% for network validation, and 20% for neural network testing. The Garson method was used to determine the importance of the variables. The neural network technique for the determination of milk fraud by the addition of whey proved to be efficient. Among the variables of highest relevance were fat content and density.

          Translated abstract

          RESUMO: A adulteração do leite pela adição de soro de leite é um problema que diz respeito às autoridades nacionais e internacionais. O objetivo deste trabalho foi quantificar o teor de soro em amostras de leite adulterado por meio de redes neurais artificiais, usando como variáveis de entrada os resultados de análises rotineiras em amostras de leite. As análises foram realizadas com diferentes concentrações em relação à adição de soro de leite (0, 5, 10 e 20%), e as amostras foram analisadas quanto à gordura, sólidos não gordurosos, densidade, proteína, lactose, minerais e ponto de congelamento, totalizando 164 ensaios, dos quais 60% foram utilizados para treinamento em rede, 20% para validação de rede e 20% para teste de rede neural. O método de Garson foi utilizado para determinar a importância das variáveis. A técnica de redes neurais para a determinação da fraude ao leite por adição de soro provou ser eficiente. Entre as variáveis de maior relevância estavam o teor de gordura e a densidade.

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          Most cited references20

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          An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data

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            Electrical conductivity of milk: measurement, modifiers, and meta analysis of mastitis detection performance.

            The physics, physiology, and pathology of electrical conductivity of milk are described. Based on a meta analysis, the use of electrical conductivity as a mastitis detection tool is discussed. Most reports were based on subclinical mastitis data. The gold standards of the different reports are discussed. With an overall sensitivity of 66% and an overall specificity of 94%, the predictive value of a positive electrical conductivity test remains low in a low prevalence population. The use of on-line systems for clinical mastitis detection is discussed. On-line systems that combine multiple data and perform multifactorial analyses will be of interest to the dairy industry.
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              Quantification of whey in fluid milk using confocal Raman microscopy and artificial neural network

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                Author and article information

                Journal
                cr
                Ciência Rural
                Cienc. Rural
                Universidade Federal de Santa Maria (Santa Maria, RS, Brazil )
                0103-8478
                1678-4596
                2020
                : 50
                : 7
                Affiliations
                [1] Barbacena Minas Gerais orgnameInstituto Federal do Sudeste de Minas Gerais Brazil
                Article
                S0103-84782020000700453 S0103-8478(20)05000700453
                10.1590/0103-8478cr20190312
                da3edbeb-8206-47f9-8c7b-1cf05a72d944

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 20, Pages: 0
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                Categories
                Microbiology

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