Development of a Simple and Rapid Method for Color Determination in Beers Using Digital Images

Preconcentration of sulphonamides in bovine milk by the cloud point extraction method using smartphone-based digital images

Sulphonamides are a group of synthetic antibiotics used specially in veterinary medicine. Among the procedures employed in the sample preparation for sulphonamide determination are liquid-liquid extraction (LLE) and solid-phase extraction (SPE) that use large volumes of organic solvents. Hence, a clean procedure was developed based on preconcentration and cloud point extraction (CPE) without using organic solvents to quantify total sulphonamides in bovine milk. The procedure was optimized as follows: 2 mL of pre-cleaned milk sample, 2 mL of reagent solution and 1 mL of Triton X-114 7% (m/v) were added to a tube, heated in a water bath at 40 °C for 10 minutes and centrifuged at 2950 rcf for 20 minutes. Digital image acquisition was employed directly at the tube without removing the supernatant/aqueous phase. The linear response was observed between 10 and 400 μg L^-1 of total sulphonamides and described by the following equation: S = 2.50 + 0.0514C (μg L^-1) and R = 0.999. The LOD and the CV (n = 11) were estimated to be 10 μg L^-1 and 1.3%, respectively. The main interferents present at their usual concentrations in the sample did not interfere with the results. Spike and recovery tests of total sulphonamides were carried out in UHT and pasteurized milk with recovery values between 73 and 106% and the results obtained for this kind of sample were in agreement with those achieved by a high performance liquid chromatography (HPLC) procedure at the 95% confidence level. The analytical procedure presents an adequate sensitivity to determine total sulphonamides in bovine milk and does not require organic solvents, being aligned to the principles of green chemistry.

Authentication of carioca common bean cultivars (Phaseolus vulgaris L.) using digital image processing and chemometric tools

Bean authentication can result in higher quality products for commerce. Partial least squares discriminant analysis (PLS-DA) was applied to digital images in order to develop a methodology that allows the non-destructive discrimination of three Phaseolus vulgaris L. cultivars (Agro ANfc9, IPR-Andorinha, and IPR-Sabiá) having different technological characteristics. Principal component analysis resulted in a separation of these cultivars, but with a certain amount of overlap. Supervised analysis showed that three PLS1-DA models, each for two cultivars, was moderately better than the simultaneous treatment of all three cultivars (PLS2-DA). Permutation test evaluated statistical significance of PLS-DA models. The classification models were more accurate for Agro ANfc9 and IPR-Sabiá cultivars than for IPR-Andorinha. The Agro ANfc9-IPR-Sabiá model correctly classified 100% of the two bean classes in both training and test sets. This analytical strategy is fast, inexpensive, environmentally friendly, and can be applied for bean quality control helping cultivar authenticity for commerce.