Brandy de Jerez is a European Geographical Indication for grape-derived spirits aged in oak casks that have previously contained any kind of Sherry wine and, therefore, are known as Sherry Casks^®. Wood compounds have a substantial influence in the quality of the brandies that are aged in the barrels. In the cellar, the barrels that have been used for many years to keep Sherry wine or other wine spirits are often used for this purpose. When wooden barrels are used for the first time, they release a large amount of compounds into the liquid contained in them. Such amount decreases over time but casks life cycle has remained unexplored until now. The present work has the aim to study the brandies obtained from the same wine spirit after two years ageing in three differently oak casks: namely new, 7 years of use (4 years containing Oloroso wine and 3 years containing wine spirits) and 32 years of use (8 years containing Oloroso wine and 24 years containing wine spirits). According to the results from our experiments, even after 32 years of use, the wood barrels still contribute to modify the organoleptic characteristics of brandy. Moreover, the brandies aged in used barrels were judged more balanced than those aged in new barrels.

Wooden barrels and wood chips are usually used in the ageing of spirits and wines to improve their sensorial profile. Oak wood is the most popular material used in cooperage, but there are other interesting woods, such as cherry or chestnut, that could be considered for this purpose. In this study, a novel method for the determination of the aromatic profile of wood powder by Direct Thermal Desorption-Gas Chromatography-Mass Spectrometry (DTD-GC-MS) was optimized by experimental design. The volatile composition of five different types of wood chips was determined by direct analysis of wood powder by DTD-GC-MS method developed. Thirty-one compounds from wood were identified through this analysis, allowing the differentiation between woods. The aromatic and phenolic compound profile of the 50% hydroalcoholic extract of each type of wood studied was analyzed by Stir-bar Sorptive Extraction-Gas Chromatography-Mass Spectrometry (SBSE-GC-MS) and Ultra-High-Performance Liquid Chromatography (UHPLC) to determine which wood compounds are transferred to spirits and wine after ageing. Different phenolic profiles were found by UHPLC in each wood extract, allowing their differentiation. However, results obtained by SBSE-GC-MS did not allow distinguishing between wood extracts. The analysis of wood in solid state, without any type of previous treatment except grinding, by DTD-GC-MS does not imply any loss of information of the aromatic compounds present in wood as other techniques. This is a potential method to identify aromas in wood that, in addition, allows different types of wood to be differentiated.

Fino and Amontillado are Sherry wines, produced in Marco de Jerez area (southern Spain), and aged in Criaderas y Solera system. Fino Sherry wine follows a biological aging process, under a veil of flor yeasts, while Amontillado Sherry wine shares the same biological aging firstly, followed by oxidative aging, which gives them special features. Organic acids, esters, higher alcohols, phenolic compounds and total dry extract of Sherries evolve during aging due to evaporation processes, physical-chemical reactions, wood contributions and microbiological activity. During aging, Sherry wines improve their organoleptic profile, as could be proved in the tasting sessions. Hierarchical Cluster Analysis and Factor Analysis with factor extraction using Principal Components of Sherry wines studied were carried out and natural groupings of the wines according to the type of aging and their age were observed. A strong correlation between the parameters analyzed and the aging of each wine has been seen in the Multiple Linear Regression studies, establishing two different models, one for each type of Sherry wine, that, with only four of all the variables studied estimated the wine age with more than 99% of confidence. This constitutes a useful tool to control the age of these Sherry wines in the winery.

Oak wood is the main material used by coopers to manufacture casks for the aging of spirits or wines. Phenolic compounds are the main components extracted from the wood during spirit aging. In the present study, a chemometric approach based on unsupervised (PCA) and supervised (PLS-DA) pattern recognition techniques has been applied to the chromatographic instrumental fingerprints, obtained by ultra-high-performance liquid chromatography (UHPLC) at 280 nm, of the phenolic profiles of brandies aged in casks made of different oak wood species. The resulting natural data groupings and the PLS-DA models have revealed that the oak wood species, the toasting level, and the aging time are the most influential factors on the phenolic profile of the final products. Fingerprinting should be considered as a very useful feature, as it represents a considerable advantage, in terms of internal and quality control, for brandy producers.

Brandies are spirits produced from wine spirit and wine distillates. The original wines selected to be distilled to produce the wine spirits as well as the distillation method used determine, to a large extent, the organoleptic characteristics of the final products. The young wine spirits evolve during their aging in oak casks, this being another key stage that affects the chemical and sensorial characteristics of the final brandy. In this work, seven different brandies have been studied. They were obtained from wine produced with and without the addition of sulfur dioxide, during their fermentation, using different distillation methods (single, double or serial distillation using pot stills and continuous column distillation) and aged for 14 or 28 months in three different types of oak wood (Quercus alba, Quercus robur and Quercus petraea) previously toasted to two different grades (medium or light). The use of unsupervised pattern recognition methods (HCA and FA) determined that the addition of sulfur dioxide during the fermentation of the base wine has a major influence on the aromatic and phenolic profile of the aged distillates. On the other hand, by means of supervised pattern recognition methods such as LDA and ANNs, the most significant variables that would allow to discriminate between the classes of brandies identified in the study were evaluated. Thus, the results obtained should cast some light on the most significant variables to be taken into account regarding Brandy production processes if a better control over these production processes is to be achieved, so that more exclusive and better quality products are obtained.

Engineered stone silicosis (ESS), primarily caused by inhaling respirable crystalline silica, poses a significant occupational health risk globally. ESS has no effective treatment and presents a rapid progression from simple silicosis (SS) to progressive massive fibrosis (PMF), with respiratory failure and death. Despite the use of diagnostic methods like chest x‐rays and high‐resolution computed tomography, early detection of silicosis remains challenging. Since routine blood tests have shown promise in detecting inflammatory markers associated with the disease, this study aims to assess whether routine blood biomarkers, coupled with machine learning techniques, can effectively differentiate between healthy individuals, subjects with SS, and PMF. To this end, 107 men diagnosed with silicosis, ex‐workers in the engineered stone (ES) sector, and 22 healthy male volunteers as controls not exposed to ES dust were recruited. Twenty‐one primary biochemical markers derived from peripheral blood extraction were obtained retrospectively from clinical hospital records. Relief‐F features selection technique was applied, and the resulting subset of 11 biomarkers was used to build five machine learning models, demonstrating high performance with sensitivities and specificities in the best case greater than 82% and 89%, respectively. The percentage of lymphocytes, the angiotensin‐converting enzyme, and lactate dehydrogenase indexes were revealed, among others, as blood biomarkers with significant cumulative importance for the machine learning models. Our study reveals that these biomarkers could detect a chronic inflammatory status and potentially serve as a supportive tool for the diagnosis, monitoring, and early detection of the progression of silicosis.