Malolactic Fermentation Timing and Color

The malolactic fermentation (MLF) is a key process in the production of red wines and some white wines. While it is commonly conducted after the completion of the alcoholic fermentation (sequential), it can also be induced at the same time where Oenococcus oeni is inoculated shortly after the beginning of alcoholic fermentation (AF). While a concurrent MLF is typically completed in a shorter time period than a sequential MLF, it is often avoided due to concerns over the production of excess acetic acid, loss of color, and competition with the fermentative yeast Saccharomyces cerevisiae. Finally, the impact of concurrent MLF on the organoleptic qualities of red wines is relatively unknown and may present an obstacle for the adoption of this practice. This project seeks to address some of these concerns and determine the impact of MLF timing on Pinot noir wine chemical and sensory properties. In addition, the use of nonSaccharomyces yeast during cold soaking and their potential impact on a concurrent MLF will be explored. Initial experiments assessed the production of acetaldehyde by six different non-Saccharomyces yeast under cold soak conditions. All yeast grew well during the six day simulated cold soak and significant differences in the amount of acetaldehyde produced were observed. T. delbrueckii Alpha produced the highest concentration of acetaldehyde (71.8 mg/L) while H. uvarum and M. fructicola Gaia produced the lowest. While M. fructicola Gaia had been used in previous cold soak experiments, we decided to use T. delbrueckii Alpha for future Pinot noir cold soak experiments due to the high production of acetaldehyde observed and the potential significance of this compound for color formation and stability. In 2022 Pinot noir wines were produced with and without cold soak, with and without T. delbrueckii Alpha, and with a concurrent or sequential MLF. AFs were completed in ten days or less while all MLFs were completed within fourteen days. Only minor differences in the time to completion of AF or MLF were observed between the treatments suggesting that the use of nonSaccharomyces yeast during cold soak did not have an impact on the concurrent MLF and that the concurrent MLF did not impact the performance of the AF. Wines are currently being filtered and bottled and will be assessed by a sensory panel in summer 2023. Color analysis of the wines is also underway as is analysis of acetaldehyde concentrations throughout cold soak, AF, and MLF. Findings from this study will help determine the implications of MLF timing and the use of a non-Saccharomyces yeast in conjunction with a concurrent MLF. This will allow strategies to be developed for the use of MLF and non-Saccharomyces yeasts to improve Pinot noir wine color as well as the impact on other sensory characteristics.

Utilizing Malolactic Fermentation as a Tool to Prevent Brettanomyces Bruxellensis Wine Spoilage

Brettanomyces bruxellensis is considered one of the most problematic wine spoilage yeasts due to the difficulty of controlling it, the potential significant financial losses due to loss of wine quality, and the cost of prevention and remediation measures. Wine is particularly vulnerable to B. bruxellensis infection during and shortly after the malolactic fermentation (MLF) as SO2 cannot be added until this process is complete. It has been suggested that conducting a rapid MLF initiated by inoculation of Oenococcus oeni is a useful strategy to prevent B. bruxellensis spoilage as this minimizes the length of time the wine is not protected by SO2. This project investigates an additional benefit of conducting a rapid MLF, the prevention of B. bruxellensis growth due to inhibitory interactions with O. oeni. Pinot noir wine (no SO2 additions, no MLF) was produced and used to test the ability of a large number of commercial O. oeni strains to inhibit B. bruxellensis growth at the end of MLF. Sterile filtered wine was inoculated with one of eleven commercial O. oeni strains and growth and malic acid monitored. When MLF was complete, wines were inoculated with a select strain of B. bruxellensis and growth and volatile phenol production monitored.

All O. oeni strains tested inhibited the growth of B. bruxellensis UCD2049 in Pinot noir wine with O. oeni strain variation observed. O. oeni strains Alpha, 350, VP41, MBR31 and PN4 most strongly inhibited growth of B. bruxellensis UCD2049, while strains CH11, Omega, Beta, and VFO 2.0 inhibited B. bruxellensis to a lesser extent. The potential mechanism of this inhibition was investigated by using a dialysis membrane to physically separate O. oeni and B. bruxellensis cells but allow free movement of nutrients and other potential inhibitory compounds. The physical separation of O. oeni from B. bruxellensis relieved the inhibition of B. bruxellensis by O. oeni that occurred when the two microorganism were in present together. These results indicate that inhibition is not due to nutrient depletion by O. oeni as nutrients could flow freely across the dialysis membrane. It is also unlikely that B. bruxellensis inhibition was due to the production of an inhibitory compound by O. oeni as any potential inhibitory compound would also have passed through the dialysis membrane. Instead, these results provide strong evidence that the inhibition of B. bruxellensis by O. oeni is due to cell-cell contact.

The sensitivity of additional B. bruxellensis strains to O. oeni was also determined. While B. bruxellensis UCD2049 populations declined rapidly when inoculated into Pinot noir wine that had just completed MLF with O. oeni Alpha, growth of the other B. bruxellensis strains tested was not impacted. Why B. bruxellensis strain UCD2049 was inhibited by O. oeni while the other B. bruxellensis strains were not was subsequently investigated. Initial experiments considered whether ethanol tolerance between B. bruxellensis strains impacted inhibition by O. oeni. Given that earlier experiments had been conducted in 13% (v/v) wine, wines were instead adjusted to 12.5% or 14% (v/v) ethanol. In low (12.5%) ethanol wine that had undergone MLF, B. bruxellensis UCD2049 grew well, in contrast to what was observed in 13% wine where growth was inhibited. B. bruxellensis strains AWRI-1499 and Copper Mountain also grew well in low ethanol wine with no difference between treatments. In higher ethanol wine, B. bruxellensis UCD2049 struggled to grow whether the wine had undergone MLF or not. In contrast, B. bruxellensis strains AWRI-1499 and Cooper Mountain grew well in the higher ethanol wine. B. bruxellensis strains AWRI-1499 populations recovered slower in wine that had undergone MLF while the opposite occurred for strain Copper Mountain. These results demonstrate that ethanol tolerance differences between B. bruxellensis strains impact their inhibition by O. oeni. For example, strain UCD2049 was not inhibited by O. oeni in wine at 12.5% ethanol but was inhibited in 13% and 14% ethanol wine. Additional experiments will be conducted where pH will also be considered as tolerance to this factor is known to differ between B. bruxellensis strains. Experiments are also underway exploring how long MLF induced B. bruxellensis inhibition last as well as whether B. bruxellensis inhibition occurs if infection happens at the beginning or mid-point of MLF.

Using a genome-scale metabolic model for Saccharomyces cerevisiae for understanding and modifying strain performance

Two key metabolic activities of yeast relevant to wine fermentations are nutrient utilization efficiency and wine aroma development. For nutrient utilization efficiency (NUE), variability in yeast cell metabolism results from modulation of cellular processes that include changes in membrane composition along with a range of other metabolic pathways that are not fully understood. This variability often affects the completeness of a fermentation (characterized as “dry,” “sluggish,” or “stuck”). Moreover, variability in yeast species or strains used in wine production results in different concentrations of aroma compounds, which can lead to distinct sensory characteristics. Controlling factors affecting nutrient utilization efficiency and wine aroma profile and mouthfeel characteristics related to yeast requires a detailed understanding of cellular metabolism. To develop such understanding, studies often use large-scale data approaches (e.g. genomics and metabolomics), along with multivariate statistics, to identify key metabolic fluxes or metabolites whose presence favors a specific fermentation outcome. Although these studies are useful in exploring variation between yeasts, they are often not comprehensive enough, especially considering that they are labor intensive and costly. An alternative method is to use genome-scale metabolic models combined with dynamic FBA (flux balance analysis) to predict the flux distribution of all the metabolites within the cell over the course of an entire fermentation. As a part of this grant, our goal is to show that this computational approach can be used to predict experimental wine fermentation data, to understand differences between commercial strains, and to suggest genetic modification strategies towards increasing strain performance and control aroma characteristics. To date, we have been able to simulate anaerobic, nitrogen-limited yeast fermentations with the latest genome-scale yeast model. Predictions for nutrient utilization and production of metabolites such as ethanol, glycerol, and organic acids are quite good. Biomass is somewhat underpredicted using conditions that we would expect. However, predicted biomass increases if we vary amino acid utilization and oxygen utilization at the beginning of fermentation. We have also found that the biomass composition and amino acid composition of proteins are important parameters in predicting maximum biomass concentration. Experimentally, however,
we have found that composition changes between strains and over time. We are now generating a more complete set of this data. In past work, no measurements of this biomass composition were conducted—researchers just assumed composition from old data sets—thus limiting the utility of their predictions. We have also made progress on curating the model for aromatic compounds derived from yeast metabolism and have begun our experiments to quantify the aromatic compounds as a function of time and yeast strain.

Development of a prediction tool for phenolic extraction in red wines as a function of winemaking practices and fermentor design

Red wine fermentations are performed in the presence of grape skins and seeds to ensure extraction of color and other phenolics. The presence of these solids results in two distinct phases in the fermentor, as the solids float to the top to form a “cap.” Modeling of red wine fermentations is, therefore, complex and must consider spatial heterogeneity to predict fermentation kinetics and phenolic extraction. We have developed a reactor-engineering model for red wine fermentations that includes the fundamentals of fermentation kinetics, heat transfer, diffusion, compressible fluid flow, and extraction of phenolics (anthocyanins, skin tannins, and seed tannins).

COMSOL was used to solve all components of the model simultaneously utilizing a Finite Elements Analysis (FEA) approach. Prediction of phenolic concentration gradients and temperature gradients from this model were validated against measurements in 2000 L pilot fermentations. Model prediction and experimental data showed excellent agreement for anthocyanin and tannin concentrations and distributions over the course of fermentation.

After validation, this model was applied to examine how fermentor design (e.g. scale and aspect ratio) and operational decisions (temperature set point, pump over frequency) would affect phenolic extraction rates, relative concentrations of skin to seed tannins, and distribution of phenolics throughout the fermentor in the absence of cap management. These results were a follow up to 2018-2019’s work, where the model was used to explore fermentation dynamics and temperature control in red wine cylindrical fermentors and white wine concrete egg fermentors. Example findings include optimization of skin tannin extraction via cap management, with 1x/day pump overs being found superior to both no cap management and 8x/day pump overs, a finding made possible via the combined spatial fermentation-extraction model.

Our results have opened up two exciting avenues of further investigation. The first is applying our reactor engineering models to isothermal fermentation process acceleration, where wine fermentation process cycle time could be greatly decreased by the judicious application of yeast nutrients throughout the fermentation, maximizing yeast biomass. This would greatly improve productivity in existing wine fermentors and lower the capital cost of new winery equipment. The second is the application of COMSOL extraction models to external grape pomace extraction columns, allowing for the fine-tuning of phenolic profiles in the end wine, potentially in a much more rapid fashion than in-tank extraction.

In the nineteen months since this grant began, we have been highly productive having published six primary research papers, along with a review of wine fermentation process modeling. We have also published three papers in cooperative works stemming from this grant. We have presented this work at various extension venues, as well as technical conferences throughout the nation.

Utilizing malolactic fermentation as a tool to prevent Brettanomyces bruxellensis wine spoilage

Brettanomyces bruxellensis is considered one of the most problematic wine spoilage yeasts due to the difficulty of controlling it, the potential significant financial losses due to loss of wine quality, and the cost of prevention and remediation measures. Wine is particularly vulnerable to B. bruxellensis infection during and shortly after the malolactic fermentation (MLF) as SO2 cannot be added until this process is complete. It has been suggested that conducting a rapid MLF initiated by inoculation of Oenococcus oeni is a useful strategy to prevent B. bruxellensis spoilage as this minimizes the length of time the wine is not protected by SO2. This project investigates an additional benefit of conducting a rapid MLF, the prevention of B. bruxellensis growth due to inhibitory interactions with O. oeni. Pinot noir wine (no SO2 additions, no MLF) was produced and used to test the ability of a number of commercial O. oeni strains to inhibit B. bruxellensis growth at the end of MLF. Sterile filtered wine was inoculated with various O. oeni strains and growth and malic acid monitored. When MLF is complete, wines will be inoculated with a select strain of B. bruxellensis and growth and volatile phenol production monitored.

The sensitivity of a number of B. bruxellensis strains to O. oeni is also being determined. B. bruxellensis strains have been sourced representing B. bruxellensis isolates from a wide range of winemaking regions including Oregon. A model wine system was identified for use to improve the rate that B. bruxellensis strains can be tested for inhibition by O. oeni. Results from the model wine system will be used to select which strains will be used in wine experiments that take significantly longer to complete.

Development of a genome-scale metabolic model for Saccharomyces cerevisiae for use in understanding and modifying strain performance

Two key metabolic activities of yeast relevant to wine fermentations are nutrient utilization efficiency and wine aroma development. For nutrient utilization efficiency (NUE), variability in yeast cell metabolism results from modulation of cellular processes that include changes in membrane composition along with a range of other metabolic pathways that are not fully understood. This variability often affects the completeness of a fermentation (characterized as “dry”, ”sluggish” or ”stuck”). Moreover, variability in yeast species or strains used in wine production results in different concentrations of aroma compounds, which can lead to distinct sensory characteristics. Controlling factors affecting nutrient utilization efficiency and wine aroma profile and mouthfeel characteristics related to yeast requires a detailed understanding of cellular metabolism. To develop such understanding, studies often use large-scale data approaches (e.g. genomics and metabolomics), along with multivariate statistics, to identify key metabolic fluxes or metabolites whose presence favors a specific fermentation outcome.

Although these studies are useful in exploring variation between yeasts, they are often not comprehensive enough, especially considering that they are labor intensive and costly. An alternative method is to use genome-scale metabolic models combined with dynamic FBA (flux balance analysis) to predict the flux distribution of all the metabolites within the cell over the course of an entire fermentation. As a part of this grant, our goal is to show that this computational approach can be used to predict experimental wine fermentation data, to understand differences between commercial strains, and to suggest genetic modification strategies towards increasing strain performance and control aroma characteristics. To date, we have been able to simulate anaerobic, nitrogen-limited yeast fermentations with the latest genome-scale yeast model. Behavior predicted for changing initial nitrogen concentration matches qualitatively with experiment. We simulated fermentation of three commercial yeast strains with highly varied NUE. Utilizing multivariate statistics, we have used the simulation results to identify the metabolic pathways that differ the most between these strains. On first analysis, the results are in agreement with existing experimental data. It is also clear that having an accurate biomass composition will be critical to a good quantitative fit of the data. Therefore, we are currently pursuing measurement of these key parameters as a function of fermentation time and strain.

Development of a prediction tool for phenolic extraction in red wines as a function of winemaking practices and fermentor design

Red wine fermentations are performed in the presence of grape skins and seeds to ensure
extraction of color and other phenolics. The presence of these solids results in two distinct phases
in the fermentor, as the solids float to the top to form a “cap.” Modeling of red wine
fermentations is, therefore, complex and must consider spatial heterogeneity to predict
fermentation kinetics. We have developed a reactor-engineering model for red wine
fermentations that includes the fundamentals of fermentation kinetics, heat transfer, diffusion,
and compressible fluid flow. To develop the heat transfer component of the model, the heat
transfer properties of grapes were experimentally determined as a function of fermentation
progression. COMSOL was used to solve all components of the model simultaneously utilizing a
Finite Elements Analysis (FEA) approach. Predictions from this model were validated using
prior experimental work. Model prediction and experimental data showed excellent agreement.
The model was then used to predict spatial profiles of active yeast cell concentration and ethanol
productivity, as well as liquid velocity profiles. The model was also used to predict how these
gradients would change with differences in initial nitrogen concentration, a key parameter in
predicting fermentation outcome in nitrogen-limited wine fermentations. After validation, this
model was applied to examine how fermentor design (e.g. scale and aspect ratio) would affect
fermentation mixing, temperature control, and chemical gradients. Along these lines,
temperature control and mixing were also evaluated for concrete eggs using the same model.
Finally, a preliminary model for phenolic extraction from skins and seeds was developed and
validated using experimental data. This led to an analysis of phenolic release of tannins from
grape seeds that we are currently pursuing. We are now in the process of the next step in
modeling—combining the two models for fermentation dynamics and phenolic extraction to be
able to predict and control phenolic profiles in finished red wines. In the seven months since this
grant began, we have been highly productive having published two papers, submitted a third that
is under review, and will be submitting a fourth paper within the next month. We have also
presented this work at various extension venues around the state.

Impact of Pre-Fermentation Cold Soak Conditions on Microbial Populations and Consequences for Wine Aroma

Wine aroma is one of the most important components of wine quality and can be impacted by grape variety, viticultural practices, and winemaking procedures. One particular practice that is employed during Pinot noir production to impact wine aroma is cold soaking. In this process grapes are held at cold temperatures to prevent growth of Saccharomyces cerevisiae and delay the beginning of alcoholic fermentation. Recent research has demonstrated that yeast naturally present during the cold soak can impact wine aroma and flavor (Hall et al. 2017). This research builds off these results and investigates how cold soak conditions could be manipulated to encourage or discourage growth of certain yeast and the consequences for wine aroma. Specifically, ways a winemaker may manage a cold soak (temperature, SO2, yeast diversity) were investigated for their impact on yeast populations and production of volatile aromas. Pinot noir wines were produced where the grapes were cold soaked for six days at two different temperatures (6 or 10?C) with the addition of 0, 50, or 100 mg/L SO2. Six non-Saccharomyces yeast species commonly isolated from grapes were inoculated and their populations monitored throughout the cold soak. Wine was also produced from grapes that did not undergo cold soak. Temperature and SO2 concentration impacted the growth of non-Saccharomyces yeast during the six day cold soak in a species specific manner. The highest populations observed were in the cold soak at 10?C when no SO2 addition had been made. Here H. uvarum increasing in population from approx. 103 cfu/mL to almost 108 cfu/mL by the end of the cold soak. As increasing concentrations of SO2 were added to the grapes prior to cold soak the growth of the non-Saccharomyces yeast including H. uvarum decreased. When 50 mg/L SO2 was added only low populations of H. uvarum, T. delbrueckii, and L. thermotolerans were detected at the end of the 6?C cold soak while at 10?C only H. uvarum was detected. When 100 mg/L SO2 was added there were few culturable yeast present in the cold soaks at either 6 or 10?C. Overall, increasing SO2 was more effective at minimizing H. uvarum growth than decreasing the temperature as there was still significant growth of H. uvarum at 6?C when no SO2 addition was made.

All wines made from grapes that underwent cold soak had significantly higher color and polymeric pigment content than wine made from grapes that did not undergo cold soak with only small differences in color and polymeric pigment content being noted between wines made from grapes cold soaked under different SO2 and temperature conditions. Initial volatile aroma analysis demonstrated significant differences between the concentration of a number of esters in wines made from grapes that were or were not cold soaked. In particular, wines made from grapes cold soaked with no SO2 additions had lower concentrations of certain ethyl esters.

Identifying Compound(s) Responsible for Off-flavors associated with “Stressed Vine Syndrome” in Pinot Noir

One of the increased concerns of wine industry is related to vine stress. Although the off-flavor descriptors vary from winery to winery, the frequent descriptors used in the wineries include “tequila”, “shellfish”, “peanut”, “ashtray”, “dry weed”, “herbaceous’, “flint” and other descriptors. In young wine, the taint smells like “bay leave”, and the wines do not age well. There were observations from wineries that taint could be related to compromised or nutritionally imbalanced fruits from stressed vines, induced by drought, nitrogen deficit, or a combination of many factors, but the exact cause(s) has never been studied or documented. This research is aimed at identifying the chemical nature of these off-flavors using gas chromatography/olfactometry, GC-MS and sensory analysis. Once the chemical nature of the off-flavor is identified, viticulture and enology remedies could be further investigated. The objective for the first year is to identify wine sensory characteristics that define “stress vine syndrome” through sensory evaluation and to identify wines for further analysis.

Several wines have been identified from industrial collaborators. Rollin Soles from ROCO winery identified a matured vineyard with full cover crop and dry farming practice to give a wine with bay leaf aromatic (2012 vintage) and the taint intensified with aging. Another vineyard with young vine also repeated to give dried herb tequila aromatics. Gary Horner from Erath winery also identified several wines with stressed vine aromatics including Pinot noir, Pinot gris and Chardonnay. More off-flavor wines are being recruited.

The “stressed vine” off-flavor was characterized by a sensory panel consisted of six winemakers from the Willamette Valley who have been involved with “stressed vine” off flavors previously and five OSU researchers involved in the project. The panel was able to identify the “stressed vine” off-flavor from the wines collected from industry. The off-flavor was also compared with wine standards comprised of tequila, peanut shell, bay leaf, agave and herbal characteristics. The off-flavored wine and the standards will be used for further sensory training and analysis.

Some preliminary chemical analyses were also performed in these wines including higher alcohols, esters, volatile phenols and TDN (kerosene aroma). The analyses were also performed on 20 normal Pinot noir wines. Data are being analysis to compare the off-flavored wine with the normal wines. GC/olfactometry will be performed next to identify the off-flavor compound(s) in “vine stressed” wines.

Investigating Fruitiness Perception in Red and White wines

This report details activities that occurred from August 2017 to January 2017. Several accomplishments were achieved during these first 6 months. We successful made wine, Pinot noir and Pinot gris, that contained no aroma compounds. This was achieved by altering some winemaking productions and an addition of resin that specifically absorbs aroma compounds. There is enough wine to serve as our base wine for the entirety of the study.  Fruit standards for red wines were developed and used for three sensory panels that investigated fruitiness in red wine. After 3 training sessions panelists were found to be consistent with their standard evaluations. The first sensory panel validated previous research focused on the impact of β-­damascenone, lactones, furaneols and red and black-berry associated esters to fruitiness in red wine. Our results for furaneols and β-dsamscenone do agree with previous work. The 2nd and 3rd sensory panels investigated the same compounds as the 1st sensory panel but at different concentrations and combinations. We have found that when norisoprenoids, β-damascenone and β-ionone, are at low concentrations and when furaneol compounds are at high concentrations, there is an impact to perception of red fruit aromas. However this only occurs if all other compounds are at lower concentrations. Once these compounds are in combination with higher concentrations of other compounds, the tested esters and lactones, there is a shift from red fruit aromas to dark fruit aromas. As we anticipated dark fruit aromas appear to be due to a combination of many compounds, with no one compound class dominating over another. We still have 1 more sensory panel to conduct to complete the investigation into the impact of norisoprenoids, furaneols, lactones and red and black berry associated esters on fruity aroma perception. It is our intent to begin focusing on the effect of acetate esters and volatile fatty acids to fruity perception of red wine. We also are prepared to begin investigating fruitiness in white wine, focusing on terpenes and esters.