Mineral Composition Evaluation in Energy Drinks Using ICP OES and Chemometric Tools.

This study reports the simultaneous determination of the total concentrations of Al, Ca, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, P, and Zn in 17  samples of commercial energy drinks through inductively coupled plasma optical emission spectrometry and multivariate methods, such as Pearson correlation and principal component analysis (PCA), in order to conduct a more thorough evaluation of the original data. The samples studied were stored in two types of containers (polyethylene terephthalate bottles and aluminum cans) and purchased in the city of Belém (State of Pará, Brazil). The results showed high Na content in energy drinks, followed by K, Ca, and Mg. The accuracy of the optimized method was evaluated with the certified reference materials to assess trace elements in water (NIST 1643e); the resultant recoveries varied from 83 to 105%. Energy drinks stored in cans presented higher levels of aluminum and magnesium, while those bottled in polyethylene terephthalate bottles had a higher K content. There were significant differences between the observed Na concentrations and the values dictated on the drink package. Furthermore, PCA explained 70.38% of the total variance, allowing for an evaluation of the degree of similarity between the energy drinks studied and showing that the main contributions to the formation of groups are related to Fe, Na, Mg, and Zn contents. These results will be used to better understand the distribution of inorganic elements contained in energy drinks.

Sub-optimal wavelet denoising of coaveraged spectra employing statistics from individual scans.

This paper proposes a novel wavelet denoising method, which exploits the statistics of individual scans acquired in the course of a coaveraging process. The proposed method consists of shrinking the wavelet coefficients of the noisy signal by a factor that minimizes the expected square error with respect to the true signal. Since the true signal is not known, a sub-optimal estimate of the shrinking factor is calculated by using the sample statistics of the acquired scans. It is shown that such an estimate can be generated as the limit value of a recursive formulation. In a simulated example, the performance of the proposed method is seen to be equivalent to the best choice between hard and soft thresholding for different signal-to-noise ratios. Such a conclusion is also supported by an experimental investigation involving near-infrared (NIR) scans of a diesel sample. It is worth emphasizing that this experimental example concerns the removal of actual instrumental noise, in contrast to other case studies in the denoising literature, which usually present simulations with artificial noise. The simulated and experimental cases indicate that, in classic denoising based on wavelet coefficient thresholding, choosing between the hard and soft options is not straightforward and may lead to considerably different outcomes. By resorting to the proposed method, the analyst is not required to make such a critical decision in order to achieve appropriate results.