Ferramentas quimiométricas aplicadas no desenvolvimento de metodologia analítica para avaliação de adulteração em leite bovino por adição de soro do queijo por ATR-FTIR

No Brasil, o monitoramento da qualidade do leite é realizado pelo Ministério da Agricultura, Pecuário e Abastecimento (MAPA) através da Instrução Normativa n° 68 (IN 68). A qualidade do leite consumido é uma constante preocupação devido às fraudes descobertas nos últimos anos. Uma das fraudes mais comuns é a adulteração de leite por adição de soro de queijo; esta adição é proibida pela legislação brasileira para o leite destinado diretamente ao consumo. Essa fraude pode ser detectada através da determinação do índice de caseinomacropeptídeo (CMP). O CMP é um peptídeo específico do soro do queijo; deste modo é um marcador da adulteração. Neste trabalho foi desenvolvido um método analítico capaz de quantificar o CMP no leite empregando a Espectroscopia no Infravermelho Médio com Transformada de Fourier e Reflexão Total Atenuada (ATR-FTIR) e Quimiometria. A análise exploratória dos dados foi realizada através de Análise por Componentes Principais (PCA) e Análise de Agrupamentos Hierárquicos (HCA), os quais indicaram uma semelhança entre as amostras de leite cru (LC) e leite semidesnatado (LS) contaminadas com CMP, em virtude da presença de gordura nesses leites. Na PCA foi possível observar uma tendência de separação das amostras com distintas adições de CMP. Na análise dos modelos de regressão foram utilizados os algoritmos de Mínimos Quadrados Parciais (PLS), Mínimos Quadrados Parciais por Sinergismo de Intervalo (SIPLS) e Máquinas de Vetores de Suporte com Mínimos Quadrados (LS-SVM) para selecionar o modelo adequado para quantificação do CMP em amostra de leite. Desse modo o modelo de escolha foi o s4i16M com LS-SVM. Assim, o método proposto mostrou ser rápido, simples e econômico para detectar amostras de leite adulteradas com CMP, cujos resultados foram comparados com o método de referência, com boa correlação (R2 = 0,9984).In Brazil, the milk quality is conducted by the Ministry of Agriculture, Livestock and Supply (MAPA) through Normative Instruction No. 68 (IN 68) and it is a constant concern due to the discovered frauds in recent years. A common fraud that occurs in Brazilian milk is the milk whey addition; which is prohibited by Brazilian law for milk of direct consumption. This fraud can be detected by determining the Caseinemacropeptide index (CMP). Since the CMP is a specific peptide of whey, it can be used as marker of milk adulteration. In this work, an analytical method capable of quantifying the CMP in milk, using the Fourier Transform Infrared Spectroscopy (FTIR) and chemometrics, was developed. Exploratory data analysis was performed using Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). These methods indicated a similarity between the samples of raw milk and semi-skimmed milk contaminated with CMP. This similarity may be due to the presence of fat in these milks. In the scores of the PCA, it was possible to observe a trend of separation of samples with different CMP concentrations. It was used Partial Least Squares algorithms (PLS), Partial Least Squares with Interval Synergism (SIPLS) and Support Vector Machines with Least Squares (LS-SVM) algorithms as regression algorithms. The model of choice was the s4i16M with LS-SVM. The proposed method proved to be fast, simple and economical to detect milk samples adulterated with CMP. These results corroborated with the reference method (R2 = 0,9984)

Novas metodologias para determinação de origem de vinhos geúchos empregando espectrometria por fluorescência associada a quimiometria

Novas metodologias por espectrofluorimetria acoplada ás ferramentas quimiométricas foram desenvolvidas, visando a classificação de amostras de vinho tinto varietais produzidas em duas regiões do Rio Grande do Sul, considerando a sua origem geográfica. Como as propriedades fluorescentes de um composto químico podem variar em função do pH, e as mudanças estruturais resultantes do pH induzem alterações significativas nos seus espectros de fluorescência, foi possível separar e identificar também diferentes compostos químicos. Neste trabalho, foram analisadas 53 amostras de vinho tinto da região da Serra Gaúcha e 20 da região da Campanha, contemplando 10 variedades de uvas. O sinal de fluorescência registrado corresponde a nove matrizes de emissão (51 variáveis) de excitação (12 variáveis) (EEM) registradas em diferentes pH (3 até 11) gerando assim a matriz de dados representando os dados de ordem superior. Estes foram tratados pela Resolução Multivariada de Curvas com Mínimos Quadrados Alternantes (MCR-ALS) e one class method Data Driven Soft Independente Modelling of Class Analogy (DD-SIMCA) para construção dos modelos de classificação. Ainda foram selecionados dois pHs (3 e 7) bem como a fusão dos dados destes, para representar dados de 1ª ordem, que foram explorados por meio da aplicação dos algoritmos (ACO, GA e SW) para seleção de variáveis, para auxiliar no reconhecimento dos vinhos empregando análise discriminante linear. Como resultado, observou-se que a predição foi realizada com melhor taxa para o modelo SW quando realizado a fusão dos pH 3 com pH 7, resultando num modelo com taxa de acerto superior a 90%. Por outro lado, os resultados do MCR-ALS apresentaram ótima recuperação dos compostos fluorescentes presente nos vinhos tinto analisados, e o DD-SIMCA alta capacidade de reconhecimento geográfico. Isso mostra que a metodologia proposta pode ser utilizada como uma ferramenta eficaz para identificação e classificação de amostras de vinhos visando a rastreabilidade desse produto, quando consideradas as mais representativas regiões produtoras de vinho do Estado do Rio Grande do Sul.New methodologies for spectrofluorimetry coupled with chemometric tools were developed, aiming at the classification of red wine samples produced in two regions of Rio Grande do Sul, considering their geographical origin. As the fluorescent properties of a chemical compound can vary depending on the pH, and the structural changes resulting from the pH induce significant changes in its fluorescence spectra, it was possible to separate and identify different chemical compounds as well. In this work, 53 samples of red wine from the Serra Gaúcha region and 20 from the Campanha region were analyzed, covering 10 grape varieties. The registered fluorescence signal corresponds to nine emission matrices (51 variables) of excitation (12 variables) (EEM) registered at different pH (3 to 11) thus generating the data matrix representing the higher order data. These were treated by Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS) and one class method Data Driven Soft Independent Modelling of Class Analogy (DD-SIMCA) to build the classification models. Two pHs (3 and 7) were also selected, as well as the fusion of their data, to represent 1st order data, which were explored through the application of the algorithms (ACO, GA and SW) to select variables, to assist in the recognition of wines using linear discriminant analysis. As a result, it was observed that the prediction was performed with a better rate for the SW model when the fusion of pH 3 with pH 7 was performed, resulting in a model with a hit rate greater than 90%. On the other hand, the results of the MCR-ALS showed an excellent recovery of the fluorescent compounds present in the analyzed red wines, and the DD-SIMCA high capacity for geographic recognition. This shows that the proposed methodology can be used as an effective tool for the identification and classification of wine samples aiming at the traceability of this product, when considered the most representative wine producing regions of the State of Rio Grande do Sul

Chemometric tools and ftir-atr spectroscopy applied in milk adulterated with cheese whey

Brazilian law forbids the addition of cheese whey in milk. However, adulteration with cheese whey is one of the most applied fraud due to its low cost. The detection of this fraud is the quantification of Caseinomacropeptide (CMP). The CMP is a constituent of the whey that can be used as adulteration marker. Thus, an analytical method capable of identifying CMP by Fourier Transform Infrared Spectra (FTIR) was developed using chemometrics methods. Firstly, we attempted to develop an exploratory analysis model by Hierarchical Grouping Analysis (HCA) and Principal Component Analysis (PCA) that indicated similarity between samples of raw milk and semi-skimmed milk. Moreover, in the PCA scores, it was possible to observe a tendency of separation between samples with different concentrations of CMP. Afterwards, multivariate regression models were used for Partial Least Squares (PLS), Partial Least Square with Interval Synergism (siPLS) and Supporting Machines with Least Squares (LS-SVM) to quantify the adulteration in different types of milk by Cheese serum through the CMP. All the models were then compared to each other and the results of the official method with Liquid Chromatography Tandem mass spectrometry (LCMS/MS) analysis used by the Ministry of Livestock and Supply (MAPA). The model LS-SVM, employing the full spectrum, obtained the best result compared to the other models (PLS and siPLS) to quantify the CMP in the milk samples

FEASIBILITY STUDY FOR ANTISEPTIC GEL FORMULATIONS INCORPORATING THE ESSENTIAL OILS CYMBOPOGON CITRATUS (DC.) STAPF. AND CARYOPHYLLUS AROMATICUS L.

Recent years have seen an increase in the search by the pharmaceutical industry for products and active ingredients of natural origin. Additionally, there is a need for the creation of new market products such as antiseptics, used to prevent or reduce the risk of infection by inhibiting the proliferation of microorganisms. The objective of this study was to produce two antiseptic gels by incorporating into each of them individually an essential oil, namely lemongrass and cloves, and subsequently evaluating the antimicrobial activity by minimum inhibitory concentration (MIC) testing. The oils and gels were tested in parallel and resulted in a similar profile that can be observed in the inhibition concentration. This was a preliminary study that merits further investigation, which may progress to stability testing and evaluation of lower concentrations incorporated into the gel base.Recent years have seen an increase in the search by the pharmaceutical industry for products and active ingredients of natural origin. Additionally, there is a need for the creation of new market products such as antiseptics, used to prevent or reduce the risk of infection by inhibiting the proliferation of microorganisms. The objective of this study was to produce two antiseptic gels by incorporating into each of them individually an essential oil, namely lemongrass and cloves, and subsequently evaluating the antimicrobial activity by minimum inhibitory concentration (MIC) testing. The oils and gels were tested in parallel and resulted in a similar profile that can be observed in the inhibition concentration. This was a preliminary study that merits further investigation, which may progress to stability testing and evaluation of lower concentrations incorporated into the gel base

Chemometric tools and ftir-atr spectroscopy applied in milk adulterated with cheese whey

Brazilian law forbids the addition of cheese whey in milk. However, adulteration with cheese whey is one of the most applied fraud due to its low cost. The detection of this fraud is the quantification of Caseinomacropeptide (CMP). The CMP is a constituent of the whey that can be used as adulteration marker. Thus, an analytical method capable of identifying CMP by Fourier Transform Infrared Spectra (FTIR) was developed using chemometrics methods. Firstly, we attempted to develop an exploratory analysis model by Hierarchical Grouping Analysis (HCA) and Principal Component Analysis (PCA) that indicated similarity between samples of raw milk and semi-skimmed milk. Moreover, in the PCA scores, it was possible to observe a tendency of separation between samples with different concentrations of CMP. Afterwards, multivariate regression models were used for Partial Least Squares (PLS), Partial Least Square with Interval Synergism (siPLS) and Supporting Machines with Least Squares (LS-SVM) to quantify the adulteration in different types of milk by Cheese serum through the CMP. All the models were then compared to each other and the results of the official method with Liquid Chromatography Tandem mass spectrometry (LCMS/MS) analysis used by the Ministry of Livestock and Supply (MAPA). The model LS-SVM, employing the full spectrum, obtained the best result compared to the other models (PLS and siPLS) to quantify the CMP in the milk samples