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This paper proposes a method to perform rapid and sensitive graphite furnace atomic absorption spectrometry (GFAAS) determination of Cr(VI) in vinegar samples without wet digestion. Taking into account low levels of Cr(VI) and the presence of several concomitants in the sample matrix, the proposed method uses single-phase liquid-liquid extraction (SPLLE). The method is automated, employing a flow-batch analyzer (FBA) which uses a mixing chamber designed to emulate a separation funnel promoting the formation/break of the single-phase solution, reaction and extraction. The Cr(VI) extraction procedure was performed by proportioned mixing of the vinegar sample (aqueous phase), amyl alcohol (organic phase), and ethanol (consolute) to form a single-phase solution. The chelating reagent sodium diethyldithiocarbamate (DDTC) dissolved in the consolute reacts rapidly with Cr(VI), in the single phase to produce a stable Cr-DDTC complex. The single-phase break step is performed by adding an excess of Britton-Robinson buffer, and then the organic phase enriched with Cr(VI) was analyzed by GFAAS. Factors such as extraction steps, single-phase composition, pH, chelating agent concentration, and flow-batch parameters were optimized. The FBA-SPLLE-GFAAS was evaluated in vinegar samples showing satisfactory analytical features in terms of limit of quantification (0.86 μg L −1 ), precision (RSD

In this paper, we propose a new methodology to identify adulterations in ground roasted coffees (due to the presence of husks and sticks) using digital images and the successive projections algorithm for variable selection in association with linear discriminant analysis (SPA-LDA). A simple document scanner was used for capturing the images, and a Petri dish support with eight circular holes (one for each sample) to be scanned was employed. Color histograms in the hue-luminosity-saturation (HLS) channels extracted from the digital images were used as input data and statistically evaluated using supervised pattern recognition techniques. For comparison with SPA-LDA, soft independent modeling by class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA) were also used. In general, SPA-LDA provided significantly better performance than the other classification models, reaching a mean accuracy of 92.5 % for both the training and test sets, while SIMCA and PLS-DA attained only 71.5 and 85.5 %, respectively. More specifically, all of the models presented high rates (above 90 %) for sensitivity and specificity (in the test set samples classification), except SIMCA, which presented a specificity rate of 76 %. Moreover, the SPA-LDA model generally showed the lowest classification error rates. As such, it is a more adequate chemometric tool for discriminating pure coffee samples and adulterated by husks and sticks. The proposed strategy avoided laborious sample preparation, and additional operational costs, assessing coffee adulteration by husks and sticks.