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Sensitive, rapid, and meaningful diagnostic tools for prostate cancer (PC) screening are urgently needed. Paper spray ionization mass spectrometry (PSI-MS) is an emerging rapid technology for detecting biomarker and disease diagnoses. Due to lack of chromatography and difficulties in employing tandem MS, PSI-MS-based untargeted metabolomics often suffers from increased ion suppression and subsequent feature detection, affecting chemometric methods for disease classification. This study first evaluated the data-driven soft independent modeling of class analogy (DD-SIMCA) model to analyze PSI-MS-based global metabolomics of a urine data matrix to classify PC. The efficiency of DD-SIMCA was analyzed based on the sensitivity and specificity parameters that showed 100% correct classification of the training set, based on only PC and test set samples, based on normal and PC. This analytical methodology is easy to interpret and efficient and does not require any prior information from the healthy individual. This new application of DD-SIMCA in PSI-MS-based metabolomics for PC disease classification could also be extended to other diseases and opens a rapid strategy to discriminate against health problems.

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A methodology was developed to monitor the content of crambe biodiesel in mixtures with conventional diesel using hydrogen nuclear magnetic resonance (1H NMR) spectroscopy combined with the orthogonal projections on the latent structure-discrimination analysis (OPLS-DA). The efficiency of the developed OPLS-DA model was analyzed based on the criteria of true response statistics: false positive and false negative rate, sensitivity, specificity, efficiency and Matthew's correlation coefficient, where the sensitivity (true positive rate) and specificity (true negative rate) were both equal to 1 and the false positive and false negative rates were both equal to 0, which means that all samples to be predicted as belonging to the diesel class of interest, B10 (containing 10% biodiesel and 90% pure diesel), were predicted in class 1, and all samples to be considered as belonging to the diesel class, not of interest, BX (biodiesel content less and greater than in B10), were predicted in class 0. These results showed 100% correct classification of the training and test set samples for B10 and BX, demonstrating a high efficiency of the OPLS-DA model in the monitoring of crambe methyl biodiesel content when mixed with diesel in various proportions. The excellent results in the application of this model suggest that this analytical methodology is feasible, efficient and suitable for use by inspection agencies to control the quality of this fuel.

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The degradation of caffeine in different kind of effluents, via photo-Fenton process, was investigated in lab-scale and in a solar pilot plant. The treatment conditions (caffeine, Fe(2+) and H(2)O(2) concentrations) were defined by experimental design. The optimized conditions for each variable, obtained using the response factor (% mineralization), were: 52.0 mg L(-1)caffeine, 10.0 mg L(-1)Fe(2+) and 42.0 mg L(-1)H(2)O(2) (replaced in kinetic experiments). Under these conditions, in ultrapure water (UW), the caffeine concentration reached the quantitation limit (0.76 mg L(-1)) after 20 min, and 78% of mineralization was obtained respectively after 120 min of reaction. Using the same conditions, the matrix influence (surface water - SW and sewage treatment plant effluent - STP) on caffeine degradation was also evaluated. The total removal of caffeine in SW was reached at the same time in UW (after 20 min), while 40 min were necessary in STP. Although lower mineralization rates were verified for high organic load, under the same operational conditions, less H(2)O(2) was necessary to mineralize the dissolved organic carbon as the initial organic load increases. A high efficiency of the photo-Fenton process was also observed in caffeine degradation by solar photocatalysis using a CPC reactor, as well as intermediates of low toxicity, demonstrating that photo-Fenton process can be a viable alternative for caffeine removal in wastewater.

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A method for direct determination of manganese (Mn) in human serum by graphite furnace atomic absorption spectrometry (GFAAS) was proposed in this work. The samples were only diluted 1:4 with nitric acid 1% (v/v) and Triton(®) X-100 0.1% (v/v). The optimization of the instrumental conditions was made using multivariate approach. A factorial design (2(3)) was employed to investigate the tendency of the most intense absorbance signal. The pyrolysis and atomization temperatures and the use of modifier were available and only the parameter modifier use did not have a significant effect on the response. A Center Composed Design (CCD) presented best temperatures of 430 °C and 2568 °C for pyrolysis and atomization, respectively. The method allowed the determination of manganese with a curve varying from 0.7 to 3.3 µg/L. Recovery studies in three concentration levels (n=7 for each level) presented results from 98 ± 5 to 102 ± 7 %. The detection limit was 0.2 µg/L, the quantifying limit was 0.7 µg/L, and the characteristic mass, 1.3 ± 0.2 pg. Intra- and interassay studies showed coefficients of variation of 4.7-7.0% (n=21) and 6-8%(n=63), respectively. The method was applied for the determination of manganese in 53 samples obtaining concentrations from 3.9 to 13.7 µg/L.

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