Metal and metalloid profile as a fingerprint for traceability of wines under any Galician protected designation of origin

Traditional alcoholic beverages have always been part of the Mediterranean culture and, lately, they have been re-evaluated to valorize both the territory and local customs. In this study, the Amarena wine, a fortified wine included in the national list of the traditional agri-food products, was characterized during bottle aging for oenological parameters, chromaticity, volatiles, and inorganic elements. Then, experimental data were visually interpreted by a principal component analysis (PCA). PCA revealed that most of oenological parameters (i.e., alcoholic grade, total dry extract, sugars, organic acids, and phenolic compounds) had a scarce discriminating power. Additionally, ethyl esters were only present in younger products, while remaining at quite constant levels. Conversely, certain metals (i.e., Mg, Na, Mn, Zn, and Cu), chromatic properties, and pH differentiated older Amarena bottles from the younger counterpart. Particularly, acetaldehyde and furanic compounds proved to be valid aging markers. A sensorial analysis highlighted that fruity and floral odors and flavors characterized younger beverages, while dried fruity, nutty, and spicy notes were displayed by older products, along with the valuable attribute of “oxidized” typically observed in aged Sherry wines. Overall, this study may encourage the production and commercialization of the Amarena wine, thus preserving the cultural heritage of the Mediterranean area.

Section: Resultsmentioning

confidence: 99%

Section: Element Analysismentioning

confidence: 99%

Valorization of Traditional Alcoholic Beverages: The Study of the Sicilian Amarena Wine during Bottle Aging

Bella

Porretti

Albergamo

et al. 2022

“…Overall, the total metal content in grapevine originates from natural sources, with a parental rock enriched with metal(oids), and the anthropogenic influences contribute to the site’s discrimination [ 14 , 17 ]. Metal(oid) composition was used to investigate site influence on wine performance [ 14 ] or moreover, as a pathway for authenticating wine products [ 18 , 19 ]. The wine elemental composition can influence aging [ 20 ] and varies with each step of the vinification process [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Chemical Hazards in Wine Production Associated with Climate Change

Nechita

Iordache

Voica

et al. 2023

The climate warming trend challenges the chemical risk associated with wine production worldwide. The present study investigated the possible difference between chemical wine profile during the drought year 2012 compared to the post-drought year 2013. Toxic metals (Cd and Pb), microelements (Mn, Ni, Zn, Al, Ba, and Cu), macroelements (Na, Mg, K, Ca, and P), isotopic ratios (87Sr/86Sr and 206Pb/207Pb), stable isotopes (δ18O, δ13C, (D/H)I, and (D/H)II), and climatic data were analyzed. The multivariate technique, correlation analysis, factor analysis, partial least squares–discriminant analysis, and hierarchical cluster analysis were used for data interpretation. The maximum temperature had a maximum difference when comparing data year apart. Indeed, extreme droughts were noted in only the spring and early summer of 2012 and in 2013, which increased the mean value of ground frost days. The microelements, macroelements, and Pb presented extreme effects in 2012, emphasizing more variability in terms of the type of wine. Extremely high Cd values were found in the wine samples analyzed, at up to 10.1 µg/L. The relationship between precipitation and δ18O from wine was complex, indicating grape formation under the systematic influence of the current year precipitation, and differences between years were noted. δ13C had disentangled values, with no differentiation between years, and when coupled with the deuterium–hydrogen ratio, it could sustain the hypothesis of possible adulteration. In the current analysis, the 87Sr/86Sr showed higher values than in other Romanian studies. The temperature had a strong positive correlation with Pb, while the ground frost day frequency correlated with both Pb and Cd toxic elements in the wine. Other significant relationships were disclosed between the chemical properties of wine and climate data. The multivariate statistical analysis indicated that heat stress had significant importance in the chemical profile of the wine, and the ground frost exceeded the influence of water stress, especially in Transylvania.

“…According to Silva et al (2015) [43], the ANN algorithm offers several advantages over conventional methods, such as its ability to fill in missing data, its non-linearity, insensitivity to noise or high parallelism, among others [44]. ANNs have versatile applications across various fields of research, like in food technology for honey classification [45] or wine authentication [46], in chemistry to determine the electrical percolation of AOT-based microemulsions [47], and so on.…”

Section: Introductionmentioning

confidence: 99%

Modelling Polyphenol Extraction through Ultrasound-Assisted Extraction by Machine Learning in Olea europaea Leaves

Rodríguez-Fernández,

Fernández-Gómez,

Mejuto

et al. 2023

Self Cite

The study of the phenolic compounds present in olive leaves (Olea europaea) is of great interest due to their health benefits. In this research, different machine learning algorithms such as RF, SVM, and ANN, with temperature, time, and volume as input variables, were developed to model the extract yield and the total phenolic content (TPC) from experimental data reported in the literature. In terms of extract yield, the neural network-based ANNZ-L model presents the lowest root mean square error (RMSE) value in the validation phase (9.44 mg/g DL), which corresponds with a mean absolute percentage error (MAPE) of 3.7%. On the other hand, the best model to determine the TPC value was the neural network-based model ANNR, with an RMSE of 0.89 mg GAE/g DL in the validation phase (MAPE of 2.9%). Both models obtain, for the test phase, MAPE values of 4.9 and 3.5%, respectively. This affirms that ANN models would be good modelling tools to determine the extract yield and TPC value of the ultrasound-assisted extraction (UAE) process of olive leaves under different temperatures, times, and solvents.