A HPLC method for simultaneous determination of 25 phenolic compounds in grape juice was optimized, validated and applied in the characterization of juices produced in São Francisco Valley (SFV), Brazil. The performance characteristics of the method were established by assays with standard solutions of phenolic compounds, spiked and unspiked samples. Linearity, matrix effects, trueness, precision, detection and quantification limits were evaluated. Linearity was demonstrated in the concentration ranges tested for all phenolic compounds. Significant matrix effects were not identified for the studied compounds. Mean recoveries ranged from 86.18 to 106.50%, demonstrating no lack of trueness. Precision of the method was confirmed for the 25 phenolic compounds, with acceptable repeatability relative standard deviations (from 0.71 to 9.24%) and within-reproducibility relative standard deviations (from 1.34 to 9.26%) for unspiked and spiked samples. The theoretical limits of detection and quantification of the method varied from 0.001 to 0.19 μg mL −1 and 0.003 to 0.37 μg mL −1 , respectively. The results of the validation process showed that the proposed method is fitness for purpose. This method was able to identify simultaneously 25 phenolic compounds and had advantages such as low consumption of solvents and easy sample preparation. The phenolic profile of the grape juices from SFV varied according the grape cultivar. Phenolics of the anthocyanins and tannins class predominated in red grape juices, while in white grape juice phenolic acids and tannins were found at high concentrations.
A sequential strategy was proposed to detect adulterants in milk using a mid-infrared spectroscopy and soft independent modelling of class analogy technique. Models were set with low target levels of adulterations including formaldehyde (0.074g.L), hydrogen peroxide (21.0g.L), bicarbonate (4.0g.L), carbonate (4.0g.L), chloride (5.0g.L), citrate (6.5g.L), hydroxide (4.0g.L), hypochlorite (0.2g.L), starch (5.0g.L), sucrose (5.4g.L) and water (150g.L). In the first step, a one-class model was developed with unadulterated samples, providing 93.1% sensitivity. Four poorly assigned adulterants were discarded for the following step (multi-class modelling). Then, in the second step, a multi-class model, which considered unadulterated and formaldehyde-, hydrogen peroxide-, citrate-, hydroxide- and starch-adulterated samples was implemented, providing 82% correct classifications, 17% inconclusive classifications and 1% misclassifications. The proposed strategy was considered efficient as a screening approach since it would reduce the number of samples subjected to confirmatory analysis, time, costs and errors.
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