Combined high-resolution chromatography techniques and sensory analysis as a support decision system tool for the oenologist

High temperatures influence plant development and induce a large set of physiological responses at the leaf scale. Stomatal closure is one of the most observed responses to high temperatures. This response is commonly considered as an adaptive strategy to reduce water loss and embolism in the vascular system caused by the high evaporative demand.

Certain yeast species belonging to the Pichia genus are known to form a distinctive film on grape must and wine. In a mixed-culture type fermentation, Pichia spp. (P. kluyveri in particular) are known to impart beneficial oenological attributes. In this study, we report on an easy isolation method of Pichia spp. from grape must by exploiting their film-forming capacity on media containing 10% ethanol. We isolated and identified two Pichia species, namely Pichia kudriavzevii and Pichia kluyveri, and subsequently co-inoculated them with Saccharomyces cerevisiae to ferment Gewürztraminer musts. Noteworthy differences included a significant increase in the 2-phenethyl acetate levels with the P. kluyveri co-fermentation and a general increase in ethyl esters with the P. kudriavzevii co-fermentation. Both Pichia co-inoculations yielded higher levels of glycerol in the final wines. Based on all the wine parameters we tested, the P. kluyveri strain that was isolated performed similarly to a commercial P. kluyveri strain.

Oenococcus oeni is the predominant lactic acid bacteria species in wine and cider, where it performs the malolactic fermentation (MLF) (Lonvaud-Funel, 1999). The O. oeni strains analyzed to date form four major genetic lineages named phylogroups A, B, C and D (Lorentzen et al., 2019). Most of the strains isolated from wine, cider, or kombucha belong to phylogroups A, B+C, and D, respectively, although B and C strains were also detected in wine (Campbell-Sills et al., 2015; Coton et al., 2017; Lorentzen et al., 2019;

According to the algorithm “A step back towards the future 4.1C”, (Cargnello,1986a, 1987d, 1988a.b, 1991, 1993, 1994b, 1995, 1999a.e, 2000b, 2007c, 2008a, 2009d, 2013; and according to the principles of “Charter of Sustainable Viticulture BIO‐MetaEthics 4.1CC” of GiESCO (Carbonneau and Cargnello, 2003 2015, 2017), the historical, applied philosophy and productive analysis connected to the innovations and to the “Certification of the Universal Holistic MetaEthical Sustainability 4.1C” “indexed new global production model 4.1C” has always been fundamental, especially for the “Prosecco Territory” and for the “Prosecco Wine” to design and implement their synergistic future “Sustainable and Certificable 4.1CC” according to the principles of the “Charter of Sustainable Viticulture BIO‐MetaEthics 4.1CC” by the GiESCO (Carbonneau and Cargnello, lc, Cargnello et Carbonneau, 2007, 2018), and of the Conegliano Campus 5.1C. (Cargnello, lc). Nowadays, people think that Prosecco is a wine from the Veneto Region (from Conegliano and Valdobbiadene in particular), while it comes from Friuli‐Venezia Giulia Region (in North Eastern Italy, such as Veneto) more precisely from “Prosecco” in the Municipality of Trieste (TS‐Italy), as documented in 1382 and in 1548, when Pier Andrea Mattioli, described “that ancient wine, which is born in Prosecco”, as a wine with the following characteristics “thin, clear, shiny, golden, odorous and pleasant to taste». In 1888 at the “Wine Fair” of Trieste there were the “Sparkling wine Prosecco” by Giovanni Balanc, by Giuseppe Klampferer and that one by Marino Luxa. In the 19th century, many expressed their appreciation for the “Prosecco” of Trieste. In order to implement intra and extra territorial and cross‐border relations, as well as, the “Certification of: Products, Companies, Territory, Bio‐MétaÉthique District 4.1C” of Prosecco, a series of activities and researches were conducted in 8 companies: 5 in the “Territory of Prosecco” (TS) in which the principles of “Charter of Sustainable Viticulture BIO‐MetaEthics 4.1CC” of GiESCO (Carbonneau and Cargnello, lc) have been successfully applied. In particolar: 1‐ new and original “Sustainable 4.1C global production model” developed also to prevent the problems caused by wild boar, roe deer, and birds while safeguarding their “psychophysical wellness”, as well as the “psychophysical wellness 4.1C” of the macro and micro flora and fauna, of the biodiversity, of the landscape, etc. (Cargnello, lc), 1.2‐ chemical weed control and “Non MetaEthics 4.1C” processing with the total grass growing of the ground without or with mowing, better if it is manual to protect grass, air and soil, 2‐ recovery of “Historic”: land, vineyards, vines, biodiversity, landscapes, productions, products, … , 3‐ production of the famous “Prosekar, also rosé, of Prosecco” and “Prosecco di Prosecco”, according to “A step back towards the future 4.1C” 4‐ to offer a deserved psychophysical well‐being to the “Prosecco Territory” and entrepreneurs. 

Barolo DOCG is an economically important wine producing region in Northwest Italy. It is a small region of approximately 70 km2 gross area. The topography is very complex with steep sloped hills ranging in elevation from below 200 m to 550 m. Barolo DOCG wine is made exclusively from the Nebbiolo grape. Bioclimatic indexes are often used in viticulture to gain a better understanding of broader climate trends which can be compared temporally and geographically. These indexes are also used for identifying potential phenological timing, growing region suitability, and potential risks associated with expected climatic changes. Understanding how topography influences bioclimatic indexes can help with understanding of mesoscale climate behaviour leading to improved decision making and risk management strategies. The average monthly maximum and minimum temperatures, the Cool Night Index, the Huglin Index, and the monthly diurnal range (from July to October) were calculated using data from 45 weather stations within a 40 km radius of the Barolo DOCG growing area between the years 1996 and 2019. Linear and multiple regression models were developed using independent variables (elevation, aspect, slope) extracted from a digital elevation model to identify significant relationships. Bioclimatic indexes were then kriged with external drift using independent variables that showed significant relationships with the bioclimatic index using a 100 m resolution grid. The maximum monthly temperatures and the Huglin Index showed consistent significant negative relationships with elevation in all years. The minimum monthly temperatures showed no relationship with elevation but in some months a small but significant relationship was observed with aspect. Due to the lack of a relationship between minimum monthly temperatures and elevation compared to the significant relationship between maximum monthly temperatures and elevation, monthly diurnal range had a negative relationship with elevation.