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Article

Predicting Organic Acid Variation in White Wine Malolactic Fermentation Using a Logistic Model

by
Aikaterini Karampatea
*,
Adriana Skendi
*,
Maria Manoledaki
and
Elisavet Bouloumpasi
Department of Agricultural Biotechnology and Oenology, Democritus University of Thrace, 1st Km Dramas—Mikrochoriou, 66100 Drama, Greece
*
Authors to whom correspondence should be addressed.
Fermentation 2025, 11(5), 288; https://doi.org/10.3390/fermentation11050288
Submission received: 31 March 2025 / Revised: 8 May 2025 / Accepted: 13 May 2025 / Published: 15 May 2025
(This article belongs to the Special Issue Lactic Acid Bacteria Metabolism)
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Abstract

The variation in organic acids during malolactic fermentation (MLF) affects the wine’s quality, presenting a challenge for the wine industry. This study aimed to investigate the kinetics of organic acids during MLF using two Oenococcus oeni strains under different barrel conditions. The study examined the variation in pH, total and volatile acidity, and concentration of tartaric, malic, lactic, and citric acid during MLF in the identical initial wine. In addition, the aromatic profile of the final wines was evaluated. The fermentation occurred in new and used French oak barrels. Two strains of O. oeni were used: (a) citrate-negative O. oeni (CINE) and (b) O. oeni, commonly used in the wine industry. The experimental data obtained were fitted to the logistic model for each monitored parameter. The degree of fitting R2 was higher than 92.79%, indicating good predictive accuracy for substrate consumption (malic and citric acid), as well as product formation (lactic and acetic acid). The mean values of O. oeni and O. oeni CINE differ in acetic (0.29 and 0.15 g/L) and citric acid (0.13 and 0.18 g/L), respectively. The logistic model effectively predicted the change in acid content during fermentation, describing the changes in organic acid levels during the MLF conducted in barrels. Modeling can be useful in forecasting industrial-scale production.

1. Introduction

Winemaking fermentations are designed to achieve optimal product quality, although it is challenging to measure this precisely. The alcoholic fermentation used from ancient times is a complex biochemical process that determines the quality of wine. The initial concentration of sugars, the conditions applied during alcoholic fermentation, and the yeast strains utilized strongly affect the quality of wine [1]. Meanwhile, malolactic fermentation (MLF), often referred to as second fermentation, results in significant physicochemical and organoleptic modifications. Among these modifications is the bioconversion of L-malic acid to L-lactic acid, performed by lactic acid bacteria (LAB) of the genera Oenococcus, Lactobacillus, Pediococcus, and Leuconostoc [2]. Though tartaric and malic acids are the principal acids in grapes and wines, succinic, citric, lactic, and pyruvic acids are also present in minor concentrations, affecting wine’s composition, microbiological stability, and sensory characteristics [3]. Acetate, lactate, diacetyl, acetoin, and 2,3-butanediol are metabolites of citrate metabolism that could alter flavor [3]. According to the literature, the final effect (positive or negative) on the sensory characteristics of wine depends on the bacterial species utilized and, more specifically, the strain of LAB employed to conduct the MLF [4,5].
Mostly, winemakers rely on the indigenous bacterial microflora to complete MLF [6]. Depending on the factors of total acidity, pH, temperature, nutrient availability, and type of container (e.g., oak barrel or tank), MLF completion is not ensured. The fermentation based on native LAB species could last several months or may occur in some barrels and tanks but not in others, leading to sensory deviations (undesirable aromas, altered color). It could possibly also lead to the production of biogenic amines that can cause both aroma and health issues (allergy-like intolerance reactions) in wine [7,8], although some studies refute the notion that the biogenic amine content of wines is solely impacted by malolactic fermentation [6]. The production of biogenic amines is a result of decarboxylation of the free amino acids by the LAB species (several Lactobacillus spp. or Pediococcus spp.) during the MLF [9,10,11]. According to EFSA, the toxicology of biogenic amines depends on the compounds produced and the predisposition each individual has [12]. Some O. oeni strains can form biogenic amines from arginine and ornithine and significantly increase the overall BA content of wines, contributing to the deterioration of sensorial attributes and creating health issues [13]. Thus, effective MLF strain selection involves assessment of strains for MLF efficiency and lack of undesirable traits like biogenic amine production [14]. Nevertheless, recently, in their review, Moreira, Milheiro, Filipe-Ribeiro, Cosme and Nunes [7] consider O. oeni a weak producer of biogenic amines. Yet, they recommend LAB inactivation after MLF to prevent the formation of biogenic amines by utilizing alternative energy sources.
Controlling alcoholic fermentations for winemaking is a complex task. Unlike industrial fermentations, such processes do not aim to maximize the concentration or yield of a specific metabolite, or the productivity of the process. In winemaking, the primary goal is to optimize product quality, which is challenging to quantify [15]. Oxygen plays an essential role in the vinification process and influences various stages of winemaking, including fermentation and aging. The micro-oxygenation technique focuses on managing oxygen’s function in winemaking to promote changes in the phenolic structure of a wine [16]. This method can enhance wine quality by accelerating transformation of phenols [17]. Therefore, it is essential to assess the extent to which micro-oxygenation impacts the aroma of the resulting wines. MLF is carried out in barrels to obtain more complex wines [18]. This may be attributed to the higher dissolved oxygen levels present in oak barrels compared to steel tanks during the fermentation process, with new barrels exhibiting higher levels than older ones [19].
MLF’s success relies on the viability and metabolic performance of the LAB starter culture as well as the management of various physicochemical parameters. This crucial winemaking step ensures microbial stability, lowers total acidity, enhances aromatic complexity, and improves wine flavor and mouthfeel [20,21]. At the end of the MLF process, during white wine production, even with wines having high acidity, desirable volatile compounds that improve their sensorial characteristics are formed [22].
The object of this research was to apply a logistic model to describe lactic acid bacteria’s ability to modify organic acid concentration during white wine MLF in new and used French oak barrels, inoculated with two different O. oeni strains. The MLF dynamic in obtained wines was monitored following pH, total and volatile acidity, L-malic, L-lactic, and citric acid concentrations, as well as volatile compound content. The volatile composition of the produced wines was also studied.

2. Materials and Methods

2.1. Grapes’ Harvest and Vinification

Harvesting, vinification, and analytical procedures were conducted at Domaine Skouras in Argos, Peloponnese, Greece. White Viognier grapes were manually harvested on 16 August 2022 from a 34-year-old, linearly irrigated vineyard with sandy soil, cultivated under integrated pest management (IPM) practices. The vineyard, owned by Domaine Skouras, is situated near the Gulf of Corinth. The 2022 growing season was characterized by a cold and wet winter, followed by a spring affected by frost and hail events in May. Elevated humidity levels during flowering led to occasional incidences of floral bleeding. Summer conditions were mild with cool nocturnal temperatures, and August was predominantly dry, except for precipitation during the final ten days of the month. Upon arrival at the winery, the fresh grapes were destemmed and pressed using a pneumatic press (Bucher, Tettnang, Germany). The must was maintained at 16 °C and, following pressing, potassium metabisulfite (40 mg/L) was added. After clarification of must by flotation, inoculation was performed (25 g/hL) with commercial yeast Saccharomyces cerevisiae (Lalvin CY3079 Lallemand, Germany). Alcoholic fermentation was conducted in stainless steel tanks at a constant temperature (15 °C), reaching a final sugar concentration of 3.5 g/L over 24 days. The wine was then transferred for maturation (36 months) into French oak barrels (225 L) of Bordeaux-Jupilles type (from the Bercé forest), Traditionnelle Longue toast (Tonnellerie Quintessence, Bordeaux, France).
At the end of the alcoholic fermentation, nutrients (inactivated yeast, rich in amino nitrogen, cell wall polysaccharides, and microcrystalline cellulose) (20 g/hL) (Opti’Malo Plus, Lallemand, Wismar, Germany) were added. Following inoculation with lactic acid bacteria, the substrate was allowed to ferment for 48 h. Two commercial strains of lactic acid bacteria (LAB) O. oeni were used: one robust strain, typically used for white wines fermented in oak barrels, Viniflora CH35, and one citrate-negative strain, Viniflora CINΕ (both from Chr. Hansen, Hoersholm, Denmark).
The effects of varying dissolved oxygen levels during oak-barrel MLF, including the kinetics of organic acids and the final volatile profile of wines, were investigated. In one treatment, new barrels were utilized for each strain (CH35new, CINEnew). In the other treatments, barrels had been previously used once (CINEold, CH35old). Constant storage conditions (17 ± 1 °C at 65 ± 2% humidity) were maintained in the cellar.
Prior and throughout malolactic fermentation, periodic bâtonnage (lees stirring) was performed—every other day during the first month and every two days during the second month—to maintain lees contact. Upon completion of MLF (after 47 days), the wines were stabilized with potassium metabisulfite (7 g/hL). The parameters of pH, total titratable acidity (TTA), volatile acidity (VA), malic acid (MA), lactic acid (LA), and citric acid (CA) were monitored during MLF. The MLF was carried out in duplicate.

2.2. Wine Characterization

The measurement of free and total sulfuric anhydride was determined potentiometrically using the Ripper method [23] and utilizing the automatic titrator Sulfilyser+, Dujardin-Salleron (France). A WineScan FTIR interferometer (Foss Analytics, Denmark) was used to measure the following parameters: pH, total titratable acidity (TTA), volatile acidity (VA), tartaric acid (TA), malic acid (MA), lactic acid (LA), citric acid (CA), acetic (AA) acid, glycerol, residual sugars, Baumé degrees, and ethanol. The FTIR device was calibrated following ISO 12099:2017 [24] for validation of calibration models.

2.3. Extraction of Volatile Compounds and Gas Chromatography-Mass Spectrometry (GC-MS) Analysis

Volatile compounds were isolated from the wine samples using liquid–liquid extraction. In a 250 mL beaker, 50 mL of wine, 25 mL of diethyl ether, and 25 mL of n-pentane were mixed and stirred for 10 min. The mixture was then divided into two falcon tubes, sealed, and centrifuged (Z206A, Hermle, Franklin, WI, USA) at 3500 rpm (4180 rcf) for 10–12 min, separating the organic and aqueous phases. The organic phase was collected, and the aqueous phase underwent the same extraction process again. Both organic phases were combined in a beaker, washed with deionized water in a separating funnel, and dried using anhydrous sodium sulfate [25].
The organic phase was then filtered into a pear-shaped flask, and 10 μL of n-undecane was added as an internal standard. A Vigreux column was attached, and the flask was heated in a water bath at 50 °C until the contents evaporated. After condensation, a sample was taken, placed in a glass vial, and sealed for GC-MS analysis. Each sample (CINEnew, CINEold, CH35new, and CH35old) was tested twice for qualitative and quantitative determination.
Qualitative and quantitative determination of wine’s volatile compounds was performed by gas chromatography-mass spectrometry (GC–MS) on a GC-MS QP2020 NX (Shimadzu, Kyoto, Japan). The injector was held at 250 °C and the GC oven temperature was programmed to 50 °C for 2.5 min and then ramped at 2.5 °C/min to 100 °C, 4 °C/min to 165 °C, and 7 °C/min to 250 °C, holding the temperature for 2 min. The carrier gas was argon at a constant flow rate of 1.5 mL/min. The mass spectrometer was operated in electron impact mode at 70 eV in the mass range of m/z 50–550 amu, with the ion source and transfer line maintained at 230 °C. Chemical standards and MS fragments were used for quantitative analysis.
Using the GCMS Postrun Analysis software (https://www.shimadzu.com/an/products/gas-chromatograph-mass-spectrometry/gc-ms-software/gcmssolution/index.html, Shimadzu, Kyoto, Japan), the peaks were identified by matching their mass spectra data with those from mass spectra databases (NIST 98).

2.4. Statistical Analysis

A four-parameter logistic model was used to assess responses of malic, lactic, and citric acid variation during fermentation [26]. The function is defined by the following model:
y = A2 + (A1 − A2)/(1 + (x/x0)p)
where y is the response variable, and the x is fermentation time. The parameter p reflects the steepness of the response function (slope of the curve at the point of inflection or where the increasing/decreasing rate is maximal), A1 is the lower asymptote, A2 is the upper asymptote value (for initial zero time or as time approaches infinity), and x0 is the time corresponding to the point at which 50% of the response is detected. Both A1 and A2 concentration levels are expressed as g/L. The fitting and estimation were performed, and graphical representations were generated by Origin 2022 software (Origin Lab Corporation, Northampton, MA, USA). Significant differences were evaluated with ANOVA analysis performed with the SPSS 26 software package.

3. Results and Discussion

Adding 50 to 100 mg/L of SO2 to must at the start of vinification is standard practice to limit the growth of indigenous yeast and acetic acid bacteria [27]. In our study, 40 mg/L of sulfites were added on the cloudy must before the start of alcoholic fermentation. Cells adapted to low pH survived better than non-adapted cells and O. oeni developed a tolerance to sulfite as high as 30 mg/L [28]. Addition of a sublethal dosage of sulfite (15 mg/L) increased sulfite tolerance during the adaptation step in an acidic medium (pH 3.5).
Initial analyses of the must before alcoholic fermentation showed a total acidity of 6.81 g/L tartaric acid, a tartaric acid concentration of 6.6 g/L, pH 3.40, a malic acid concentration of 2.9 g/L, and 12.7° Baume (1.00° Baume = 1.80° Brix).
Prior to the completion of alcoholic fermentation (concentration of reducing sugars <4.0 g/L), the must was transferred to the four oak barrels. No sulfation occurred during the transfer of the wine to the barrels. The composition of wine before being transferred into barrels for MLF was as follows: alcohol 13.68% vol, total acidity of 6.71 g/L tartaric acid, volatile acidity 0.08 g/L acetic acid, tartaric acid 4.5 g/L, pH 3.35, malic acid 1.9 g/L, and citric acid 0.45 g/L. The pH value of the wine samples supports the completion of malolactic fermentation by O. oeni, as high acidity conditions (pH lower than 3.2) significantly compromise the survival of O. oeni [29].

3.1. Variation in pH and Acidity During Fermentation

The variation in the wines that were allowed to ferment for 47 days in the new and used barrels using LAB strains CH35 and cine is reported in Figure 1. Since the same variation was shown for pH and TTA (Figure 1a,b), it is evident that fermentation in the four samples during the first seven days proceeded similarly (Figure 1a,b). With the progression of fermentation, a distinction in the profile of the variation in the pH and TTA among the four samples was observed. The literature reports that malolactic fermentation with malolactic starters is preferred to naturally reducing wine acidity [30]. Meanwhile, we observed that MLF with O. oeni resulted in wines with decreased acidity. Other studies reported that, usually, inoculation with Lactobacillus plantarum induces biological acidification in high pH grape must [31].
As anticipated, the pH increased in all cases. There was an increase of 0.17 for CH35new, 0.14 for CH35old, 0.12 for CINEnew, and 0.11 for CINEold. Again, the CH35 strain seemed to show the greatest change, as well as the new barrels. Therefore, the final pH value was >3.5 for CH35 new, which poses spoilage risks, since sulfur dioxide (SO2) is less effective and unable to totally inhibit all bacterially induced metabolic reactions at high pH values [10].
Tartaric acid content (Figure 1c) showed a decrease of 0.3–0.5 g/L, with CH35new showing the greatest decrease of 0.5 g/L, CH35old followed by 0.4 g/L and CINEnew and old by 0.3 g/L. CINE is an O. oeni citrate-negative strain, which results in a consistently smaller decrease in pH values. Citrate-positive O. oeni strains experience a greater pH rise due to the production of alkaline by-products (i.e., CO2, diacetyl, and acetate) [32]. This metabolism is absent in the CINE strain. Wines with citrate-negative O. oeni (like CINE) do not metabolize citrate, therefore do not produce these alkaline molecules that raise pH. Comparing this to citrate-positive strains like CH35, the pH rise is less in the CINE strain, thus retaining more acidity and freshness [33].
In summary, we observe that the higher porosity of the new barrels, where the wood is less compacted and saturated by wine, as in the older ones, results in increased levels of dissolved oxygen, thereby enhancing the progression of MLF [19] in CH35 new and CINE new, as compared to the used barrels (CH35 old and CINE old). Higher oxygen levels in new barrels create a more favorable environment for MLF bacteria, particularly O. oeni, which benefits from small amounts of oxygen as a microaerophilic bacterium [34].
Glycerol in all cases showed a constant common increase from 6.6 g/L to 6.9 g/L. It has been reported [35] that under controlled MLF, glycerol metabolism may be partial, or remain unchanged. According to Popescu-Mitroi, Radu and Stoica [35], sensory evaluation of these wines showed that glycerol metabolism through controlled MLF does not result in defects or diseases in the wine.

3.2. Variation in the Organic Acids During Malolactic Fermentation

The wine quality can be altered during MLF by the consumption and release of various metabolites (i.e., lactic acid, acetic acid, ethanol, carbon dioxide) via the fermentative pathways of LAB [36]. Davis et al. [37] reported that during the MLF, there is observed degradation of malic, citric, and fumaric acids and production of lactic and acetic acids; therefore, the oenological parameter of volatile acidity will increase.
The fluctuation in malic, lactic, and citric acid concentrations as well as volatile acidity for each LAB strain (CH35 and CINe) and barrel type (new and used) are shown in Figure 2. Figure 2 also displays the fitted curves from four-parameter logistic model fitting for each treatment.
The data fit very well, showing that the adjusted (Adj R2) coefficient of determination and coefficient of determination (R2) have values higher than 89.19% and 92.79%, respectively, suggesting that a very high percentage of the response variable variation is explained by the fitted regression line. Moreover, a very low Residual Sum of Squares (RSS) (in the range of 0.00011 to 0.06928) implied that the fitted regression line is a good fit for the data.
Figure 2a,b depict the modifications in the concentration of malic and citric acid. The production/consumption of acids decreases/increases nonlinearly over the fermentation period of 47 days. It is evident that the content of malic acid and citric acid in wine remains almost invariable in the early period (first 7 days). This is slightly higher than the lag phase in the oak barrels (5 days), but less than fermentation in steel tanks (11 days) reported by Izquierdo-Cañas et al. [38]. Malic acid decreases quickly from 1.9 g/L to 0.2–0.4 g/L in the following 10–15 days, while in the case of citric acid, this period is higher (15–20 days). Figure 2c,d show the changing patterns of lactic acid and acetic acid concentration in fermentation experiments. The early period (almost 10 days) represents the bacteria growth stage, and the amount of lactic and acetic acid is almost stable at 0–0.1 g/L (no lactic acid formation takes place). About ten days later, fermentation of these two acids evolved at a higher rate. The final lactic acid level in the four samples is about 1–1.1 g/L. The literature reports higher initial (0.20 g/L) and final levels (1.21–1.26 g/L) [38].
The extent of the lag period before the levels of acids begin to decrease reveals information regarding the adaptation of bacterial cells to each environment. In the case of malic and citric acid, the used barrels extended the lag phase. The same trend is observed for the lag phase in the case of lactic acid production. In the case of acetic acid production, differences in the time that the lag phase takes place are not visible among the samples.
During the observation of malolactic fermentation, there was a noticeable difference in the speed of fermentation between new and used barrels. In new barrels, the MLF started and finished faster, whereas in used barrels, the fermentation was slower but more stable. This is due to the greater porosity of new barrels, which allows for wider micro-oxygenation, compared to used barrels whose pores have been partially clogged by wine deposits from previous uses. Studies indicate that malolactic fermentation (MLF) occurs more quickly in oak barrels than in steel tanks due to the airtight storage provided by steel tanks, in contrast with the micro-oxygenation that takes place during the wine’s storage in oak barrels [27].
Comparing the strains of lactic acid bacteria in terms of the speed of malolactic fermentation, CH35 in both cases of barrels showed greater fermentation capacity and faster completion. Between the two used barrels, MLF showed an almost parallel course, with CH35 again having the lead despite being used for the third time. In addition, in all cases, a decrease in citric acid was observed, with CH35new and CH35old showing the greatest decrease. Specifically, CH35new decreased by 0.31 g/L, CH35old by 0.28 g/L, CINEnew by 0.24 g/L, and CINEold by 0.21 g/L. It was therefore observed that first-use barrels favored the reduction in citric acid levels. CINE bacteria are citrate-negative O. oeni, unlike CH35. Micro-oxygenation may positively impact MLF completion [39]. New barrels with larger porosity than used ones keep LAB and nutrients suspended, speeding up fermentation.
Regarding volatile acidity (Figure 2d), where the greatest differences between the two strains were observed, its increase is highlighted in all cases, with CH35new increasing by 0.18 g/L expressed in acetic acid, reaching a total of 0.26 g/L from the 0.08 g/L it initially had. CH35old increased by 0.16 g/L, while CINEnew and CINEold showed an increase of 0.13 and 0.14 g/L, respectively. Izquierdo-Cañas, Mena-Morales, and García-Romero [38] reported higher initial levels of volatile acidity (0.39 g/L) but a lower increase in the volatile acidity (0.12 and 0.02 for oak barrels and steel tanks) during MLF. As far as volatile acidity is concerned, the use or age of the barrels does not seem to have a significant influence on the two different strains of O. oeni. CINE, a citrate-negative strain, showed a lower increase in volatile acidity compared to the CH35 strain, which is due to the degradation of citric acid and pentoses [19].
The fitted coefficients X0 (time to obtain 50% increase/decrease in the response) and p (fermentation rate) for all the experimental samples are shown in Figure 3.
The time (X0) when malic acid concentration is reduced to half or lactic acid and acetic acid reaches half of the final level (upper bound) differs significantly among the samples, but this is not observed for citric acid (Figure 3a). The use of new barrels reduced the time that acids (malic, citric, lactic, and acetic) took to reach the highest rate of consumption/production compared to the used ones. On the other hand, the type of LAB strain does not produce any significant effect. Since MLF is a time-consuming process [40], a decrease in fermentation time is desired.
The rate of consumption/production of acids (p) does not differ among the samples (Figure 3b). Despite the differences among the samples, the model parameter p is not significantly (p < 0.05) affected by the LAB strain or the barrel type.
Statistical analysis showed no difference (p < 0.05) in the total amount of the produced lactic acid and acetic acid, as well as the final concentration of malic and citric acid in the wine samples (Figure 3c). However, the LAB strain used has a significant (p < 0.05) effect only in the production of acetic acid, with the CH35 strain resulting in higher levels. The barrel type does not significantly affect the final concentrations of any of the acids studied.

3.3. Volatile Profile of Wines

The determination of the aromatic profile of the four samples using gas chromatography-mass spectrometry allowed the wines to be categorized based on their qualitative characteristics. Differences between the classes of compounds and odor type identified for the four treatments are reported in Table 1 and represented in Figure 4. The most abundant classes are esters, higher alcohols, and fatty acids, in descending order. Musts with elevated levels of amino acids exhibit higher concentrations of esters and fatty acids, along with lower concentrations of higher alcohols [41]. In our study, nutrients (a blend of specific inactivated yeast rich in amino nitrogen, cell wall polysaccharides, and microcrystalline cellulose) were added at the end of the alcoholic fermentation. This particular composition may have impacted the concentration of the dominant compounds in the samples.
Other studies have reported that changes in the ester concentrations are influenced by the bacterial strain used [42]. The esters with the highest concentrations detected in our samples were phenylethyl acetate, ethyl hexanoate, hexyl acetate, ethyl decanoate, ethyl laurate, and ethyl octanoate. Their concentrations were higher than the odor perception threshold (ODT) in almost all samples, except for isoamyl lactate, diethyl malate, methyl dimethoxyacetate, ethyl 9-decenoate, ethyl palmitate, and ethyl 3-hydroxybutyrate, which were detected but did not exceed the ODT (Figure 4). Siebert et al. [43], in their study on the volatile compounds associated with ‘stone fruit’ aroma attributes in Viognier and Chardonnay wines, identified similar concentrations of hexyl acetate, linalool, terpineol, and phenylethyl alcohol. However, our findings indicated higher concentrations of phenylethyl acetate in the wines analyzed [43]. The descriptive characterization of the above esters is floral and fruity aromas [44]. Their production is dependant on wine condition and strain [19]. CH35 in a new barrel gave wine with the highest number of esters. According to Qu, Chen, Wang, He, Tao, and Jin [44] concentration of ethyl esters is significantly influenced by oxidation–reduction potential. In Table 1, the main aromatic compounds and their concentration in each treatment are listed. C-13 norisoprenoids, sesquiterpenes, aldehydes, and an alkene were detected, respectively, but none of them exceeded the ODT, except for β-caryophyllene, which was detected only for CINEold and CH35old. β-caryophyllene exceeded the odor perception threshold (ODT of β-caryophyllene 0.064 mg/L) in CINEold (0.34 mg/L) while in CH35old it was relatively close (0.06 mg/L). β-Caryophyllene is characterized by the aroma of turpentine oil and garlic [45]. The enrichment of wine in volatile aroma during barrel aging is influenced by several factors, including the wood variety and wood species, the tannin potential, and the toasting processes. Additionally, the interaction between the wine and oak wood during aging leads to the extraction of various volatile constituents, contributing to the wine’s sensory complexity and oxidative stability [46].
With regard to α-terpineol, it was only detected at CINEold, the wine with the smaller pH value [15]. Specifically, from the C-13 norisoprenoids, β-caryophyllene. β-caryophyllene is the primary aroma in mint [47], having a green and woody taste. It has generally not been reported in wine, except for a study by Shi et al. [48] that found β-caryophyllene in significantly higher concentrations during fermentation at 10 °C.
Regarding fatty acids, butanoic, hexanoic, and octanoic acids had the highest concentration in the wine with the smaller pH (CINEold). Knoll et al. [49] reported that volatile fatty acids such as hexanoic and decanoic acid generally increased in all wines after completion of MLF, while octanoic acid levels decreased. They also reported that treatments with simultaneously and sequentially inoculated MLF affected the final concentration of hexanoic acid.
In determining the overall aroma profile, CINE old appeared to have the highest concentrations in most aromatic compounds. It is visible that both strains of O. oeni yielded wines with a higher concentration of volatile compounds in used barrels than in new barrels, which has also been reported by other studies [50]. Except for acetic acid, wine acids are normally below the threshold of perception [51,52]. Also, the concentration of fatty acids in our study is less than those reported by Siebert, Barker, Pearson, Barter, de Barros Lopes, Darriet, Herderich, and Francis [43]. Table 2 lists the fatty acids often associated with undesirable aromas that exceed the odor perception threshold.
Table 1. Aromatic compounds detected in the samples (expressed in mg/L as mean ± the standard deviation), and the odor perception threshold (ODT) **.
Table 1. Aromatic compounds detected in the samples (expressed in mg/L as mean ± the standard deviation), and the odor perception threshold (ODT) **.
Chemical GroupAromatic CompoundODT (mg/L)Ch35newCH35oldCINEnewCINEoldAromatic DescriptionLiterature Reference
Esters2- phenylethyl acetate0.250.537 ± 0.038 b0.479 ± 0.031 b0.103 ± 0.012 a0.486 ± 0.022 brose, floral[53]
ethyl hexanoate0.0051.305 ± 0.064 b1.100 ± 0.148 b0.235 ± 0.032 a1.347 ± 0.073 bripe banana[53]
hexyl acetate1.500.156 ± 0.009 b0.214 ± 0.003 c0.037 ± 0.005 a0.251 ± 0.016 dsweet, perfume[54]
ethyl decanoate0.202.904 ± 0.048 d1.480 ± 0.014 b0.464 ± 0.019 a1.623 ± 0.006 cgrape, floral, soap[54]
ethyl laurate1.50ND *0.165 ± 0.021 b0.049 ± 0.002 a0.237 ± 0.013 cflowers, fruity[55]
ethyl octanoate0.0022.986 ± 0.033 d2.222 ± 0.065 b0.532 ± 0.117 a2.485 ± 0.073 cpineapple, pear, floral[56]
Terpeneslinalool0.0250.170 ± 0.034 b0.158 ± 0.015 b0.038 ± 0.006 a0.200 ± 0.020 bfruity, citric[55]
α-terpineol0.010.051 ± 0.001 b0.048 ± 0.004 b0.010 ± 0.001 a0.060 ± 0.004 ctropical fruits[57]
β-caryophyllene0.064ND0.056 ± 0.007 aND0.306 ± 0.051 bpepper, spice[58]
Higher alcoholsphenethyl alcohol109.609 ± 0.042 d7.934 ± 0.272 b1.996 ± 0.177 a8.820 ± 0.015 cflowers, rose[59]
1-pentanol646.135 ± 0.335 aND7.220 ± 0.389 bNDsweet, balsamic[54]
* ND: not detected. ** Means that any similar superscript letters in the same line are not significantly different (p < 0.05) by Duncan’s multiple range test.
Table 2. Fatty acids, expressed in mg/L ± the standard deviation, that exceeded the odor perception threshold *.
Table 2. Fatty acids, expressed in mg/L ± the standard deviation, that exceeded the odor perception threshold *.
Aromatic CompoundODT (mg/L)CH35newCH35oldCINEnewCINEoldAromatic DescriptionLiterature
Butanoic acid0.170.611 ± 0.022 b0.574 ± 0.013 b0.115 ± 0.007 a0.582 ± 0.043 bcheese[60]
Hexanoic acid0.405.217 ± 0.027 c4.196 ± 0.331 b0.962 ± 0.092 a4.151 ± 0.235 bsweaty, cheesy[54]
Octanoic acid0.502.220 ± 0.151 b7.779 ± 0.352 c1.495 ± 0.112 a7.483 ± 0.203 csweat, cheese[59]
* Means that any similar superscript letters in the same line are not significantly different (p < 0.05) by Duncan’s multiple range test.

4. Conclusions

Although malolactic fermentation is undesirable, particularly for white wines, in areas with warm climates such as the Mediterranean, recently an opposite tendency has been observed. This study presents new information on utilizing two O. oeni strains (Viniflora CH35 and Viniflora CINE) during MLF in Viognier wine. The concentrations of organic acids (malic, citric, lactic, and acetic) were quantified following MLF for 47 days. The application of the citrate-negative O. oeni strain, especially in the used barrels, gave a better wine that has the highest concentration of the most desired aromatic compounds.
Findings reveal that during MLF, there is a decrease in TTA, an increase in pH, consumption of malic and citric acid, and production of lactic and acetic acid. The logistic model was successfully applied to monitor the variation in the organic acids in the MLF process. Overall, the speed of malolactic fermentation was favored when using unused (new) barrels as opposed to used ones, even if the latter had higher quantities of desired aromatic volatiles.
Considering that there is less research on MLF in barrels, where there is reduced control over the process and consequently increased risk, modeling the acid variations during MLF in wine offers a valuable tool. The models can be employed to monitor and predict fermentation characteristics, ensuring that the wine achieves the desired organoleptic properties. The four-parameter logistic model aligns well with the monitoring of organic acid consumption during MLF by LAB O. oeni, while the kinetics of the malate consumption rate are influenced by micro-oxygenation. Micro-oxygenation can be performed in tanks, with or without oak products, to simulate the effects of oak barrels. This method may also be used to reduce the duration of malolactic fermentation.

Author Contributions

Conceptualization, A.K., A.S. and E.B.; methodology, A.K. and M.M.; software, A.S.; formal analysis, A.K., A.S., M.M. and E.B.; data curation, A.K., A.S. and E.B.; writing—original draft preparation, A.S. and A.K.; writing—review and editing, A.S. and E.B.; visualization, A.S.; supervision, A.S. and A.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Acknowledgments

We appreciate the assistance of the Domaine Skouras (10th Km Argous—Sternas Malandreni, 21200 Argos, Greece) and Oenopolis Oenological Laboratory (Drama Greece) in providing the necessary instrumentation for this study.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
MLFMalolactic Fermentation
O. oeniOenococcus oeni
LABLactic Acid Bacteria
TATotal Titratable Acidity
VAVolatile Acidity
MAMalic Acid
LALactic Acid
CACitric Acid
AAAcetic Acid
ODTOdor Perception Threshold

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Fermentation 11 00288 g001
Figure 1. Variation in the pH (a), total titratable acidity (TTA, g/L of tartaric acid) (b), and tartaric acid (TA, g/L of tartaric acid) (c) during fermentation of the wine with LAB strains CH35 and cine in new and used/old barrels for 47 days.
Figure 1. Variation in the pH (a), total titratable acidity (TTA, g/L of tartaric acid) (b), and tartaric acid (TA, g/L of tartaric acid) (c) during fermentation of the wine with LAB strains CH35 and cine in new and used/old barrels for 47 days.
Fermentation 11 00288 g001
Fermentation 11 00288 g002
Figure 2. Variation in malic (a), citric (b), lactic (c) acid, and volatile acidity (VA) (d) during malolactic fermentation of wine with two different LAB strains (CINE and CH35) in two different types of barrels, unused (new) and used (old).
Figure 2. Variation in malic (a), citric (b), lactic (c) acid, and volatile acidity (VA) (d) during malolactic fermentation of wine with two different LAB strains (CINE and CH35) in two different types of barrels, unused (new) and used (old).
Fermentation 11 00288 g002
Fermentation 11 00288 g003
Figure 3. Fitted coefficients X0 (a) and p (b) and final concentration level (upper or lower bound) (c) for malic (MA), lactic (LA), citric acid (CA), and volatile acidity (VA) during malolactic fermentation of wine with two different LAB strains (CINE and CH35) in two different types of barrels, new and used. Error bars above the bars refer to the standard deviation of the mean. Different superscript numbers within the same response indicate significant differences (p < 0.05) amongst the means, as determined by ANOVA followed by Duncan’s multiple range test.
Figure 3. Fitted coefficients X0 (a) and p (b) and final concentration level (upper or lower bound) (c) for malic (MA), lactic (LA), citric acid (CA), and volatile acidity (VA) during malolactic fermentation of wine with two different LAB strains (CINE and CH35) in two different types of barrels, new and used. Error bars above the bars refer to the standard deviation of the mean. Different superscript numbers within the same response indicate significant differences (p < 0.05) amongst the means, as determined by ANOVA followed by Duncan’s multiple range test.
Fermentation 11 00288 g003
Fermentation 11 00288 g004
Figure 4. Alluvial diagram of odor type of perceptible (above-threshold > ODT level, Yes) or not (No) aromatic compounds in wines obtained with LAB strains (CINE and CH35) in two different types of barrels unused (new) and used (old).
Figure 4. Alluvial diagram of odor type of perceptible (above-threshold > ODT level, Yes) or not (No) aromatic compounds in wines obtained with LAB strains (CINE and CH35) in two different types of barrels unused (new) and used (old).
Fermentation 11 00288 g004
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Karampatea, A.; Skendi, A.; Manoledaki, M.; Bouloumpasi, E. Predicting Organic Acid Variation in White Wine Malolactic Fermentation Using a Logistic Model. Fermentation 2025, 11, 288. https://doi.org/10.3390/fermentation11050288

AMA Style

Karampatea A, Skendi A, Manoledaki M, Bouloumpasi E. Predicting Organic Acid Variation in White Wine Malolactic Fermentation Using a Logistic Model. Fermentation. 2025; 11(5):288. https://doi.org/10.3390/fermentation11050288

Chicago/Turabian Style

Karampatea, Aikaterini, Adriana Skendi, Maria Manoledaki, and Elisavet Bouloumpasi. 2025. "Predicting Organic Acid Variation in White Wine Malolactic Fermentation Using a Logistic Model" Fermentation 11, no. 5: 288. https://doi.org/10.3390/fermentation11050288

APA Style

Karampatea, A., Skendi, A., Manoledaki, M., & Bouloumpasi, E. (2025). Predicting Organic Acid Variation in White Wine Malolactic Fermentation Using a Logistic Model. Fermentation, 11(5), 288. https://doi.org/10.3390/fermentation11050288

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