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Long-chain fatty acids (LCFA) are commonly found in lipid-rich wastewaters and are a key factor to monitor the anaerobic digesters. A new simple, fast, precise, and suitable method for routine analysis of LCFA is proposed. The system involves in-syringe-magnetic stirring-assisted dispersive liquid-liquid microextraction (DLLME) prior to gas chromatography-mass spectrometry (GC-MS) without a derivatization process. Calibration curves were prepared in an ethanol solution (R2 ≥ 0.996), which was also useful as disperser solvent. Hexane was chosen as the extraction solvent. Several parameters (pH, ionic strength, extraction solvent volume, stirring time) were optimized in multivariate and univariate studies. Limits of detection (LODs) were found in the range 0.01–0.05 mg L−1 and good precision inter-day (RSDs≤7.9%) and intra-day (RSDs≤4.4%) were obtained. The method was applied to quantify LCFA in supernatants of anaerobic digesters and olive mill wastewaters (OMW). Palmitic, stearic, and oleic acids were the most abundant fatty acid in the analyzed samples and the relative recoveries for all of them were between 81 and 113%.

Corn is one of the most consumed cereals in world and the second most cultivated in Brazil. Cereal flour has a flavor well accepted by the Brazilian population. This work describes the preparation and characterization of a new corn flour reference material which can be used as a quality assurance tool to demonstrate the reliability of the analytical results obtained from inorganic analysis of cereals and their derivatives. The studied elements were Ca, K, Mg, P, Cu, Fe, Mn and Zn. Homogeneity and stability tests, material characterization and attribution of uncertainty value were performed in this work by following recommendations of ISO Guides 30–35. The material was shown to be homogeneous and stable for 15 months at temperatures of −10 °C, +25 °C and +45 °C, with small uncertainty. Alternatively, the data were analyzed by hierarchical clustering technique and the stability was confirmed. The confidence ellipse technique enabled a simple and fast evaluation of data supplied by collaborating laboratories allowing the exclusion of outlying values and indicating possible causes of the deviations.