Simultaneous metal determination in artisanal cachaça by using voltammetry and multivariate calibration

Non-destructive authentication of Cachaças from Brejo Paraibano based on MIR spectroscopy

Cachaça, an alcoholic beverage derived from the fermentation of sugarcane juice, is a quintessential Brazilian product, with the Brejo Paraibano region standing out as one of the leading producers of high-quality cachaças. Therefore, geographic authentication of these beverages is essential to guarantee their quality and prevent fraud. This study employed mid-infrared spectroscopy (MIR) combined with chemometric models, including One-Class Partial Least Squares (OC-PLS) and Data-Driven Soft Independent Modeling by Class Analogy (DD-SIMCA), to authenticate the geographic origin of cachaças from Brejo Paraibano, as opposed to cachaças originating from other Brazilian regions. The DD-SIMCA model, incorporating spectral preprocessing with baseline shift correction and Savitzky-Golay smoothing using 21 points, achieved 95.6 % sensitivity during training, 100 % in testing, and 100 % specificity, with an overall classification efficiency of 98.4 %. These findings underscore the effectiveness of the proposed approach as a robust, green, and reliable tool to verify the authenticity and geographic origin of cachaças.

Using a biphasic system and digital imaging analysis with chemometric tools for simultaneous determination of Cu2+ and furfural in cachaça

The present study reports, for the first time, the development and application of a highly efficient method based on digital imaging analysis for the simultaneous determination of Cu²⁺ and furfural in cachaça samples using a two-phase system and chemometrics tools. Furfural reacts with aniline in an acidic medium to form a Schiff base, which exhibits a pink color. On the other hand, Cu²⁺ reacts with cuprizone in a basic medium to form a blue complex. The two reactions were performed on a porcelain plate, and a smartphone was used to capture the colorimetric images. Partial least squares (PLS) regression was used to construct the prediction models for Cu²⁺ and furfural contents in cachaça samples. After finding the best PLS models, the ordered predictor selection (OPS) analysis was performed in order to select the most predictive variables. The method developed was found to be effective in estimating the amounts of Cu²⁺ and furfural in cachaça samples, with a mean absolute error of 0.2 mg L^-1 for the Cu²⁺ model, and 0.3 mg per 100 mL of anhydrous alcohol for the furfural model. The method proposed in this study is simple and straightforward; it does not require complex technical knowledge and can be used by the producers themselves in the cachaça manufacturing process.

Chemical Fingerprint of Non-aged Artisanal Sugarcane Spirits Using Kohonen Artificial Neural Network

This study focuses on the determination of the chemical profile of 24 non-aged Brazilian artisanal sugarcane spirits (cachaça) samples through chromatographic quantification and chemometric treatment via principal component analysis (PCA) and Kohonen’s neural network. In total, forty-seven (47) chemical compounds were identified in the samples of non-aged artisanal cachaça, in addition to determining alcohol content, volatile acidity, and copper. For the PCA of the chemical compounds’ profile, it could be observed that the samples were grouped into seven groups. On the other hand, the variables’ bearings were grouped together, making it difficult to separate the components in relation to the sample groups and reducing the chances of obtaining all the necessary information. However, by using a Kohonen’s neural network, samples were grouped into eight groups. This tool proved to be more accurate in the groups’ formation. Among the chemical classes of the compounds observed, esters stood out, followed by alcohols, acids, aldehydes, ketones, phenol, and copper. The abundance of esters in these samples may suggest that these compounds would be part of the regional standard for cachaças produced in the region of Salinas, Minas Gerais.