title: Quantification of whey in fluid milk using confocal Raman microscopy and artificial neural network.
abstract: In this work, we assessed the use of confocal Raman microscopy and artificial neural network as a practical method to assess and quantify adulteration of fluid milk by addition of whey. Milk samples with added whey (from 0 to 100%) were prepared, simulating different levels of fraudulent adulteration. All analyses were carried out by direct inspection at the light microscope after depositing drops from each sample on a microscope slide and drying them at room temperature. No pre- or posttreatment (e.g., sample preparation or spectral correction) was required in the analyses. Quantitative determination of adulteration was performed through a feed-forward artificial neural network (ANN). Different ANN configurations were evaluated based on their coefficient of determination (R2) and root mean square error values, which were criteria for selecting the best predictor model. In the selected model, we observed that data from both training and validation subsets presented R2>99.99%, indicating that the combination of confocal Raman microscopy and ANN is a rapid, simple, and efficient method to quantify milk adulteration by whey. Because sample preparation and postprocessing of spectra were not required, the method has potential applications in health surveillance and food quality monitoring.
authors.affiliation: Departamento de Física, Instituto de Ciências Exatas, Universidade Federal de Juiz de Fora, Juiz de Fora, Minas Gerais, 36036-900, Brazil. Electronic address: mjbell@fisica.ufjf.br.
authors.forename: Roney
authors.initials: R
authors.lastname: Alves da Rocha
authors.affiliation: Departamento de Ciências Farmacêuticas, Faculdade de Farmácia, Universidade Federal de Juiz de Fora, Juiz de Fora, Minas Gerais, 36036-900, Brazil.
authors.forename: Igor Moura
authors.initials: IM
authors.lastname: Paiva
authors.affiliation: Departamento de Física, Instituto de Ciências Exatas, Universidade Federal de Juiz de Fora, Juiz de Fora, Minas Gerais, 36036-900, Brazil.
authors.forename: Virgílio
authors.initials: V
authors.lastname: Anjos
authors.affiliation: Departamento de Ciências Farmacêuticas, Faculdade de Farmácia, Universidade Federal de Juiz de Fora, Juiz de Fora, Minas Gerais, 36036-900, Brazil.
authors.forename: Marco Antônio Moreira
authors.initials: MA
authors.lastname: Furtado
authors.affiliation: Departamento de Física, Instituto de Ciências Exatas, Universidade Federal de Juiz de Fora, Juiz de Fora, Minas Gerais, 36036-900, Brazil. Electronic address: mjbell@fisica.ufjf.br.
authors.forename: Maria José Valenzuela
authors.initials: MJ
authors.lastname: Bell
mesh_terms: D000818:Animals; D005504:Food Analysis / Q000379:methods*; D063427:Food Quality; D018613:Microscopy, Confocal / Q000379:methods; D008892:Milk / Q000737:chemistry*; D016571:Neural Networks, Computer; D000067796:Whey / Q000737:chemistry*; D000067816:Whey Proteins
chemical_list: D000067816:Whey Proteins
keywords: Raman spectroscopy; artificial neural network; milk adulteration; whey