Espectrometria de massas com ionização por paper spray combinada a métodos quimiométricos para identificação de falsificações em cervejas

• Ano de defesa: 2016
• Autor(a) principal: Hebert Vinicius Pereira
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; UFMG
• Palavras-chave em Português: análise forense; análise de alimentos; paper spray; mínimos quadrados parciais; seleção de preditores ordenados; análise discriminante; Autenticidade de cervejas; espectrometria de massas com ionização por; Química analítica; Alimentos Adulteração e inspeção; Validação de metodo; Analise multivariada; Análise discriminante; Espectrometria de massa
• Link de acesso: http://hdl.handle.net/1843/SFSA-AEXNC8

Food authenticity has been a worldwide concern because of the potential health risks for consumers and important economic losses caused by fraud and adulteration. In Brazil, several cases of beer counterfeiting have been registered, in which the labels and bottle caps of beers with lower commercial prices were switched by labels and caps of beers that have higher prices and trade volume. These fraudsare not easily perceived by consumers due to similarity of the sensory characteristics of these beers. Currently, methods for detecting these frauds are not readily available for the regulatory agencies. On the other hand, the literature describes many methodologies for determining the authenticity of beers. However, most of them are focused on the differentiation of beers belonging to different styles. In this work,paper spray mass spectrometry (PS-MS) combined with chemometric tools were applied for the first time in a forensic context to a fast and effective differentiation of beers. A total of 141 samples of Standard American Lagers (distributed in different batches), belonging to three brands with higher market prices and five brands with lower market prices, were analyzed in the positive ion mode of PS-MS, resulting inreproducible mass spectra. The most intense signals obtained in the beers fingerprints were identified as sodium and potassium adducts of maltooligosaccharides. The fingerprints were classified using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The ordered predictors selection method (OPS) was used to variable selection, thus optimizing the supervised model. Figures of merit (FOM) were calculated in order to assess the trueness and accuracy of the developed method. The optimized model provided thecorrect classification of all samples with perfect results of FOM (100% reliability, 100% accuracy, 100% accordance and 100% concordance). The method has the potential to be employed in routine laboratories for detecting counterfeit beers.