In recent years, estrogens have gained notable attention among emerging pollutants, increasingly leading to the need for less laborious analytical methods. Optical sensors are a promising technology for the determination of chemical contaminants in water, acting as an alternative screening method. The aim of this work was to evaluate the performance of poly(vinyl chloride) (PVC) as a sensing phase for the detection of 17β-estradiol in water using mid-infrared spectroscopy and chemometrics. The sensing phase was used for differentiation purposes with an accuracy of 80%, sensitivity of 100% and specificity of 71.4% based on soft independent modeling of class analogy (SIMCA). Satisfactory limits of detection (8.9x10-1 - 2.9 mg L-1), quantification (2.9 - 1.0x10 mg L-1), slope of the analytical curve (5.0x10-4 - 2.4x10-3 L mg-1) and determination coefficients (0.90 - 0.99) were obtained. Kinetically, a extraction time of around 30 min was estimated and in a regeneration study, it was possible to use the same phase after 4 cycles of use. Therefore, the results indicate that the PVC sensing phase is promising for the detection of 17β-estradiol in water by means of mid-infrared spectroscopy and chemometric methods.
A simple, robust, versatile, high analytical frequency method was proposed to check if a sample of wine is within the range of standards set by the manufacturer, using the UV-VIS spectroscopy, multivariate analysis and a flow-batch analyzer. Two hundred and fifty-two samples of wines were analyzed. The results from the application of Hierachical Cluster Analysis (HCA) to the matrix of the data involving all samples show the formation of fifteen types of wine. A Soft Independent Modelling of Class Analogy (SIMCA) model was constructed and used to classify the samples of the overall forecast. As a result, it is observed that the prediction was performed with a success rate of 99.2% for a confidence level of 95%. This shows that the proposed methodology can be used as an effective tool for classifying of samples of wines.