Intelligent article to control the internal pressure in continue in bottles

Oak barrels form an integral part of wine production, especially that of high quality wines. However, due to its porosity, wood presents an ecological niche for microbial proliferation and is highly susceptible to microbial spoilage which could cause considerable economic losses. Brettanomyces bruxellensis, the most commonly encountered microorganism responsible for spoilage during barrel ageing, can remain in barrels after barrel sanitation to contaminate new batches of wine after refilling. Therefore, effective sanitation treatments are of utmost importance to prevent recurring wine spoilage.

Bacterial malolactic fermentation (MLF) has a considerable impact on wine quality. The yeast strain used for primary fermentation can consistently stimulate (MLF+ phenotype) or inhibit (MLF- phenotype) malolactic bacteria and the MLF process as a function of numerous winemaking practices, but the molecular evidence behind still remains a mystery. In this study, such evidence was elucidated by the direct comparison of extracellular metabolic profiles of MLF+ and MLF- yeast phenotypes. Untargeted metabolomics combining ultrahigh-resolution FT-ICR-MS analysis, powerful machine learning methods and a comprehensive wine metabolite database, discovered around 800 putative biomarkers and 2500 unknown masses involved in phenotypic distinction.

“The Human being has an excellent ability to detect and discriminate odors but typically has great difficulty in identifying specific odorants”(1). Furthermore, “from a cognitive point of view the mechanism used to judge wines is closer to pattern recognition than descriptive analysis.” Therefore, when one wants to reveal the volatile “wine-like feature” pattern recognition techniques are required. Sensomics is one of the most recent “omics”, i.e. a holistic perspective of a complex system, which deals with the description of substances originated from microorganism metabolism that are “active” to human senses (2). Depicting the relevant volatile fraction in wines has been an ongoing task in recent decades to which several research groups have allocated important resources. The most common strategy has been the “target approach” in order to identify the “key odorants” for a given wine varietal.

Must clarification is an important step occurring just after grape extraction in the elaboration of white wine, consisting in a solid-liquid separation. Traditionally, low must turbidity, around 50-150 NTU, is generally reached in white winemaking in order to prevent reductive aromas and facilitating alcoholic fermentation. Alternatively, a higher turbidity (300 NTU or above) can be sought for reasons such as a better expression of grapes identity (terroir), or for getting a must matrix that could supposedly lead to wines having greater ageing potential.

Despite the extensive research performed during the last decades, the multifactorial mechanism responsible for the white wine protein haze formation is not fully characterized. Herein, a new model is proposed, which is based on the experimental identification of sulfur dioxide as a major modulating factor inducing wine protein haze upon heating. As opposed to other reducing agents, such as 2-mercaptoethanol, dithiothreitol and tris(2-carboxyethyl)phosphine hydrochloride (TCEP), the addition of SO2 to must/wine upon heating cleaves intraprotein disulfide bonds, hinders thiol-disulfide exchange during protein interactions and can lead to the formation of novel inter/intraprotein disulfide bonds. Those are eventually responsible for wine protein aggregation which follows a nucleation-growth kinetic model as shown by dynamic light scattering [1].