RESUMEN

A quantitative method for acid-base titrations in paper-based devices (PADs) is described to analyze acetic acid in vinegar samples. In this work, two different types of PADs were developed: a device for individual spot testing and a microfluidic device. Digital colorimetry was used as the detection method, and the images were acquired using a smartphone and a homemade box with LED lights for controlled image acquisition. Titration curves were built with just eight points, using the R channel based on the gradual color transition from red to blue of litmus, a natural indicator. The endpoint was accurately determined by second derivative calculations. Both systems were applied to fifteen vinegar samples of different types, and good concentration results were obtained in comparison to the reference method. The proposed methodology is simple, fast, environmentally friendly, and surpasses the need for calibration curve construction. Moreover, the subjective endpoint identification is eliminated, and the method was automated to provide a high throughput workflow, suitable for quality control processes and real-time measurements.

RESUMEN

Acrolein occasionally appears in cider, completely spoiling its quality due to its bitter taste. It is crucial to detect it in the early steps, before the taste is severely affected, to apply the appropriate treatment. A simple and rapid analytical method to determine this compound in cider is therefore desirable. In this work, a quantitative determination method of acrolein in cider is proposed using the proton nuclear magnetic resonance technique (1H NMR). Acrolein produces a doublet signal in the spectrum at 9.49 ppm, whose area is used to determine the concentration of this compound. Importantly, 3-(Trimethylsilyl)-2,2,3,3-d4-propionic acid sodium salt is added to the cider as a reference for 0.00 ppm and 1,3,5-benzenetricarboxylic acid as an internal standard for acrolein determination. The method is validated by gas chromatography (GC). There is a good correlation between the acrolein concentrations obtained by 1H NMR and by gas chromatography in different commercial ciders (Pearson coefficient 0.9994). The 95% confidence interval for the intercept is 0.15 ± 0.49 (includes 0) and for the slope is 0.98 ± 0.03 (includes 1). When applying the paired t test, no significant difference is observed. The proposed method is direct, and no prior derivatization is needed.

RESUMEN

Color additives are used widely by the food industry to confer a desirable appearance. Some of the most used colorants (Tartrazine (E102), Sunset Yellow (E110), Red Allure (E129) and Blue Brilliant (E133)) were determined in this study using microemulsion electrokinetic capillary chromatography (MEEKC). Regression coefficients were greater than 0.9981; intra- and inter-day precisions, in terms of percentage RSD, were less than 7.01% and 8.55%, respectively; recoveries were between 90 and 100% in most cases. LODs and LOQs ranged from 0.24 to 1.21 mg L-1 and from 0.80 to 4.03 mg L-1, respectively. Moreover, MEEKC consumed less solvent than HPLC, making the analysis more environmentally friendly. The proposed method is suitable for the determination of colorants in a wide variety of foods. Results showed that consumers should be aware of colorants to avoid consumption exceeding recommended amounts.

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RESUMEN

A method based on digital image is described to quantify tartrazine (E102), yellow, and allura red (E129) colorants in food samples. HPLC is the habitual method of reference used for colorant separation and quantification, but it is expensive, time-consuming and it uses solvents, sometimes toxic. By a flatbed scanner, which can be found in most laboratories, images of mixtures of colorants can be taken in microtitration plates. Only 400 µL of sample are necessary and up to 92 samples can be measured together in the same image acquisition. A simple-to-obtain color fingerprint is obtained by converting the original RGB image into other color spaces and individual PLS models are built for each colorant. In this study, root mean square errors of 3.3 and 3.0 for tartrazine and 1.1 and 1.2 for allura red have been obtained for cross-validation and external validation respectively. Results for repeatability and reproducibility are under 12%. These results are slightly worse but comparable to the ones obtained by HPLC. The applicability of both methodologies to real food samples has proven to give the same result, even in the presence of a high concentration of an interfering species, provided that this interference is included in the image analysis calibration model. Considering the colorant content found in most samples this should not be a problem though and, in consequence, the method could be extended to different food products. Values of LODs of 1.8 mg L-1 and 0.6 mg L-1 for tartrazine and allura red have been obtained by image analysis.

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