Espectroscopia no infravermelho próximo e métodos de calibração multivariada aplicados à determinação simultânea de parâmetros bioquímicos em plasma sanguíneo

In this work, the quantitative analysis of glucose, triglycerides and cholesterol (total and HDL) in both rat and human blood plasma was performed without any kind of pretreatment of samples, by using near infrared spectroscopy (NIR) combined with multivariate methods. For this purpose, different techniques and algorithms used to pre-process data, to select variables and to build multivariate regression models were compared between each other, such as partial least squares regression (PLS), non linear regression by artificial neural networks, interval partial least squares regression (iPLS), genetic algorithm (GA), successive projections algorithm (SPA), amongst others. Related to the determinations of rat blood plasma samples, the variables selection algorithms showed satisfactory results both for the correlation coefficients (R²) and for the values of root mean square error of prediction (RMSEP) for the three analytes, especially for triglycerides and cholesterol-HDL. The RMSEP values for glucose, triglycerides and cholesterol-HDL obtained through the best PLS model were 6.08, 16.07 e 2.03 mg dL-1, respectively. In the other case, for the determinations in human blood plasma, the predictions obtained by the PLS models provided unsatisfactory results with non linear tendency and presence of bias. Then, the ANN regression was applied as an alternative to PLS, considering its ability of modeling data from non linear systems. The root mean square error of monitoring (RMSEM) for glucose, triglycerides and total cholesterol, for the best ANN models, were 13.20, 10.31 e 12.35 mg dL-1, respectively. Statistical tests (F and t) suggest that NIR spectroscopy combined with multivariate regression methods (PLS and ANN) are capable to quantify the analytes (glucose, triglycerides and cholesterol) even when they are present in highly complex biological fluids, such as blood plasma

Espectrometria de emissão óptica com plasma indutivamente acoplado aliada à quimiometria na determinação de íons metálicos no molusco Mytella falcata para discriminação de estuários potiguares

This work examined the possibility of using mussel Mytella falcata as bioindicator sample to detect metal ions in several estuaries potiguares, since species substances that accumulate in their tissues due to its characteristics filter feeders have been used for environmental monitoring. The chemometrics by principal components analysis was used to reduce the size of the original data in order to establish a pattern of distribution of metal ion. Samples were collected at three different points in the estuaries Curimataú, Guaraíra-Papeba, Potengi, Galinhos-Guamaré and Piranhas-Assu having been marked with the location using GPS (Global Positioning System). The determination of humidity content and digestion of the samples were performed using methods described in the Compendium of analytical standards of the Institute Adofo Lutz (2005) and the determination of metal ions of the elements Al, Ba, Cd, Cr, Cu, Mn, Ni, Pb, Sn and Zn were performed by optical emission spectrometry with inductively coupled plasma as described by USEPA method 6010C. The results show that it is possible to use this molluscum Mytella falcata in the estuaries of Rio Grande do Norte for the determination of metal ions. The data were subjected to principal components analysis (PCA) which enabled us to verify the distribution pattern of the metal ions studied in several estuaries potiguares and group them according to the metal ions in common with and relate them to the activities in each region