Non-Conventional Yeasts for Beer Production—Primary Screening of Strains

Abstract

Although beer fermentation has traditionally been carried out with Saccharomyces, the boom in craft brewing has led to the use of non-conventional yeast species for beer production. This group also includes non-Saccharomyces starters, which are commonly used in winemaking and which have different technological characteristics compared to standard representatives of the Saccharomyces genus. One of the important characteristics of the non-Saccharomyces group is the richer enzyme profile, which leads to the production of beverages with different taste and aroma profiles. The aim of this study was to investigate sweet and hopped wort fermentation with seven strains of active dry non-conventional yeasts of Lachancea spp., Metschnikowia spp., Torulaspora spp. and a mixed culture of Saccharomyces cerevisiae and Torulaspora delbrueckii. One ale and one lager active dry yeast strain were used as control strains. The extract consumption, ethanol production, degree of fermentation, pH drop, as well as the yeast secondary metabolites formed by the yeast (higher alcohols, esters and aldehydes) in sweet and hopped wort were investigated. The results indicated that all of the studied types of non-conventional yeasts have serious potential for use in beer production in order to obtain new beer styles. For the purposes of this study, statistical methods, principle component analysis (PCA) and correlation analysis were used, thus establishing the difference in the fermentation kinetics of the growth in the studied species in sweet and hopped wort. It was found that hopping had a significant influence on the fermentation kinetics of some of the species, which was probably due to the inhibitory effect of the iso-alpha-acids of hops. Directions for future research with the studied yeast species in beer production are presented.

1. Introduction

Beer is one of the most consumed beverages worldwide. Interest in its production dates back to ancient times. Initially, it was brewed via spontaneous fermentation, due to the wort inoculation by microorganisms found in the air. With the rise of science and the evolution of microbiological research, beer was produced through controlled fermentation with Saccharomyces yeast. Nowadays, the two main yeast types in brewing are Saccharomyces cerevisiae (ale yeast) and Saccharomyces pastorianus (lager yeast) [1,2]. However, the competitive market, combined with the increased interest of consumers for products with new taste and healthy benefits, led to the study of the possibilities of using non-conventional yeasts for beer production [3,4]. Non-conventional yeasts represent a wide range of Saccharomyces and non-Saccharomyces yeasts, which were initially considered as detrimental for alcoholic beverages because they negatively affected their sensorial characteristics [5,6]. However, the combination between wort composition, non-conventional yeast strain selection and accurate fermentation control can result in a beer with the desired sensory characteristics. Some wort ingredients (mainly carbohydrates and amino acids) are used by yeast during fermentation for the synthesis of new products, and others (mainly aromatic compounds) can be biologically transformed. Some of the yeast metabolites, such as sulfur compounds, organic and fatty acids, carbonyl compounds, higher alcohols and esters, significantly affect beer flavor and aroma [7,8].

The use of non-Saccharomyces yeasts is a new trend in the brewing industry, especially related to craft breweries. The goal is to search for new beer styles with different organoleptic profiles. In recent years, there has been a significant evolution in brewing practices, especially with regard to non-conventional yeast species, which leads to the production of beverages with a distinctive sensory profile [9,10,11,12,13,14]. The increased interest in non-Saccharomyces yeasts is due to their enzymatic profile, which is the basis for the production of various metabolites. For instance, yeasts such as Brettanomyces spp., Torulaspora del-brueckii, and others offer a wide array of organic compounds, such as esters, which can enrich the overall sensory experience of the beer [12,15,16]. Researchers are also interested in conducting mixed fermentations of non-Saccharomyces and Saccharomyces yeasts, which lead to the production of different profiles of the beverage [17,18,19]. Various studies have shown the importance of using non-Saccharomyces yeasts in beer production. Scientifically, research is dispersed in different directions—the possibilities of biodiversity for obtaining new styles of beverages [13,16] or the optimization of fermentation regimes for the accumulation of different metabolites [17,19]. The screening and application of novel yeasts are becoming common in craft breweries, reflecting the industry’s responsiveness to consumer preferences for bespoke flavors and aromas [10,17,20].

Non-Saccharomyces yeasts are finding their place in the production of so-called hybrid beer styles, which are preferred by modern beer consumers [17,21]. Of particular scientific interest are new sources of such yeasts—fruits, sourdoughs, spontaneously fermented mash and others [12,22]. As brewers continue to experiment with these novel yeasts, we anticipate even greater diversity in beer styles and flavors, supporting a sustainable model of craft brewing that emphasizes innovation and artisanal qualities [9,10,17].

Various non-conventional yeasts, such as Lachancea spp., Metschnikowia spp. and Torulaspora spp., have already been applied successfully at the laboratory scale in beer production [23,24,25]. Lachancea thermotolerans and Lachancea fermentati are good alternatives to bacteria for sour beer production due to their ability to produce lactic acid, ethanol and carbon dioxide from wort carbohydrates [26,27]. Usually, Lachancea yeasts are used in mixed fermentations with Saccharomyces yeasts in order to achieve a rapid and natural decrease in the pH of the finished beer [28]. However, it has been found that not all strains of Lachancea termotolerans are capable of producing lactic acid, which explains the fact that this yeast continues to be the subject of research [26,27]. Although Metschnikowia yeasts are used in commercial winemaking, there are few studies about their application in beer production. Metschnikowia pulcherrima shows β-D-glucosidase and cysteine-β-lyase activity, which results in a wide range of flavor-active metabolites in the final product. Moreover, the low alcohol tolerance of this yeast strain makes it suitable for the production of low-alcoholic beer [28,29]. Torulaspora delbrueckii is one of the most commonly used non-Saccharomyces yeasts in oenology, since these yeasts synthesize small amounts of undesirable compounds such as acetoin, acetaldehyde and acetic acid. Unlike winemaking, the interest in Torulaspora delbrueckii emerged at a much later stage in the brewing industry. However, different Torulaspora delbrueckii strains were investigated in mixed fermentations or co-fermentations with Saccharomyces in order to produce beer with interesting flavor profiles [30]. The main characteristics of the described yeast species were presented in Zapryanova et al., 2025 [31].

Hops are one of the main ingredients in beer, and their use has historically been associated with their antimicrobial properties. Traditional yeasts used in beer production are resistant to the influence of hop components. Certain non-Saccharomyces yeasts may exhibit different susceptibilities to hop acids compared to Saccharomyces strains, which could influence the overall microbial stability of the brew [9,32,33].

The aim of this study was to make a preliminary screening of eight non-conventional yeasts (seven non-Saccharomyces and one mixed culture of Saccharomyces cerevisiae and Torulaspora delbrueckii), as compared to two commercial brewer’s yeasts (one lager and one ale yeast). The selected yeast strains were tested in both sweet and hopped wort to assess not only their fermentation capacity and metabolite production but also the impact of hops on yeast performance. The study of the behavior of non-conventional yeasts, intended primarily for wine production, in the presence of hop bitters provides a new aspect of the possibilities for the application of these species in beer production. This study aims to determine the influence of hops on the primary and secondary metabolism of yeasts and the possibility of changes in the taste and aroma profile of the beverage. It also contributes to our understanding of how hop constituents may interact with yeast metabolism, potentially enabling more targeted formulations of wort and fermentation strategies in craft and specialty brewing.

2. Materials and Methods

2.1. Microorganisms

Ten yeast strains were used in this study: seven non-Saccharomyces, two Saccharomyces yeasts and one mixed culture of Saccharomyces cerevisiae and Torulaspora delbrueckii (

Table 1. Yeast strains used for the experiments.

Yeast Strains	Manufacturer
Saccharomyces pastorianus Saflager W34/70	Fermentis by Lesaffre, Lambersart, France
Saccharomyces cerevisiae Safale US-05	Fermentis by Lesaffre, Lambersart, France
Torulaspora delbrueckii Viniferm NS-TD	Agrovin, Alcázar de San Juan, Spain
Torulaspora delbrueckii Viniflora PRELUDE	Novonesis, Bagsværd, Denmark
Metschnikowia pulcherrima EXELLENCE B-Nature BIOPROTECTION	Lamothe-Abiet, Bordeaux, France
Lachancea thermotolerans Viniferm Ns-CHANCE	Agrovin, Alcázar de San Juan, Spain
Lachancea thermotolerans JAZZ	Lamothe-Abiet, Bordeaux, France
Lachancea thermotolerans NEVEA	SAS Sofralab, Magenta, France
Lachancea thermotolerans/formerly Klyuveromyces thermotolerans/ Viniflora CONCERTO	Novonesis, Bagsværd, Denmark
Saccharomyces cerevisiae/Torulaspora delbrueckii Oenoferm Wild and Pure	ERBSLÖH Gaisenheim GmbH, Gaisenheim, Germany

). The non-Saccharomyces yeasts were four strains of Lachancea thermotolerans, two strains of Torulaspora delbrueckii, and one strain of Metschnikowia pulcherrima. The Saccharomyces yeasts were Saccharomyces pastorianus (lager yeast) and Saccharomyces cerevisiae Safale US-05 (ale yeast). All the yeasts used were active dry yeasts, and they were re-hydrated according to the producer’s instructions before usage.

2.2. Wort Preparation

Two wort types (sweet and hopped) were used for the experiments. They were produced with 4.5 kg coarsely ground Pilsen malt (Best Malz, Haidelberg, Germany) and 20.25 L of water. Mashing was conducted in a Braumeister (Speidel, Ofterdingen, Germany) by using following mashing steps: 45 °C for 10 min, 52 °C for 20 min, 63 °C for 30 min, 72 °C for 20 min, and 78 °C for 5 min. The lautering was conducted in the same Braumeister, and the wort obtained was divided into two equal parts. The first part was frozen at −18 °C. The second part was boiled in the same Braumeister for 60 min. Ten minutes after the start of the boiling, bitter hop Magnum (α-bitter acids of 14.4%) was added to obtain wort with 90 mg/L α-bitter acids. The hot trub was removed, and hopped wort was stored at −18 °C before its use for the experiments. The parameters of sweet and hopped wort produced are shown in

Table 2. Sweet and hopped wort characteristics.

Wort Type	Initial Extract, °P	pH
Sweet wort	11.3 ± 0.1	6.22 ± 0.02
Hopped wort	12.0 ± 0.1	6.06 ± 0.01

.

2.3. Fermentation

Fermentations were carried out in 330 mL glass bottles, equipped with airlock systems, filled with concentrated sulphuric acid (Sigma-Aldrich, St. Louise, MO, USA). Bottles were filled with 200 mL of sweet and hopped wort. Bottles with wort were sterilized by means of a Koch steam sterilizer. One milliliter of suspension of pre-rehydrated yeast was added to the cooled wort. The bottles were placed in a thermostat FOC 200i (VELP Scientific, Usmate, Italy). Fermentation was carried out at 27 °C under static conditions. The fermentation dynamic was monitored through daily measurements of the weight loss of the bottles due to the release of CO₂. Fermentation finished when the difference in bottle weight between 2 consecutive days was below 0.1 g.

2.4. Analytical Procedures

2.4.1. Basic Wort and Beer Parameters

The analyses of basic wort and beer parameters (extract, alcohol, real degree of fermentation (RDF), and pH) were conducted according to the EBC methods of analysis [34]. Wort and beer extracts were measured using an Anton Paar DMA 35 density meter (Anton Paar, Graz, Austria). Alcohol content was also measured using the same density meter after simple distillation of the beer. The pH was determined by WTWinolab Ph7110 (Xylem analytics, Weiheim, Germany).

2.4.2. Secondary Metabolites Determination

Higher alcohols were measured using the p-dimethylaminobenzaldehyde method according to AOAC [35]. Ester concentration was determined according to ester saponification with sodium hydroxide (Sigma-Aldrich, St. Louise, MO, USA) after simple distillation of the beer [36]. The aldehyde concentration was determined according to the bisulphite method after simple distillation of the beer [36].

2.5. Mathematical and Statistical Analysis

2.5.1. Fermentation Dynamic Calculations

The fermentation dynamic was monitored by measuring the weight of the fermentation bottles. The method allows a large number of samples to be monitored simultaneously, as they are placed under identical fermentation conditions, which eliminates the possibility of methodological errors. The differences in the bottles weights compared to the beginning of the fermentation were calculated using Equation (1).

$$\Delta {\mathrm{M}}_{\tau \mathrm{i}}={\mathrm{M}}_{\mathrm{in}}-{\mathrm{M}}_{\tau \mathrm{i}}$$

(1)

where: ΔM_τi is the difference between the initial bottle weight M_in and the bottle weight at the moment of the measurement M_τi.

The daily change in beer real extract during fermentation was calculated on the basis of the bottles’ weight loss ΔM_τi using Equation (2):

$$\begin{array}{l}\mathrm{R}{\mathrm{E}}_{\mathrm{i}}=\mathrm{a}\Delta {\mathrm{M}}_{\tau \mathrm{i}}\\ \mathrm{a}={\displaystyle \frac{\left(\mathrm{R}{\mathrm{E}}_0-\mathrm{R}{\mathrm{E}}_{\mathrm{f}}\right)}{{\mathrm{M}}_{\mathrm{in}}-{\mathrm{M}}_{\mathrm{f}}}}\end{array}$$

(2)

where: RE₀—original wort extract; RE_f—beer real extract; M_f—bottle weight at the end of fermentation.

The daily change in the beer alcohol content was rated on the basis of the beer real extract change during fermentation using Equation (3):

$$\begin{array}{l}\mathrm{A}\mathrm{C}{\mathrm{L}}_{\mathrm{i}}=\mathrm{b}(\mathrm{R}{\mathrm{E}}_0-\mathrm{R}{\mathrm{E}}_{\mathrm{i}})\\ \mathrm{b}={\displaystyle \frac{\mathrm{A}\mathrm{L}{\mathrm{C}}_{\mathrm{f}}}{\mathrm{R}{\mathrm{E}}_0-\mathrm{R}{\mathrm{E}}_{\mathrm{F}}}}\end{array}$$

(3)

where: b—coefficient of sugar conversion; ALC_i—alcohol content of the sample; ALC_f—alcohol content of beer at the end of fermentation.

The daily biomass accumulation was determined according to Parcunev et al. [37].

2.5.2. Determination of Kinetic Parameters of the Fermentation Processes

The kinetic parameters of each fermentation process were evaluated using standard models applied to biomass, substrate (wort real extract), and product (alcohol) concentration profiles collected during batch fermentations. The following parameters were calculated based on experimental data

• Specific Growth Rate (μ)

The specific growth rate (μ) was estimated from the linear portion of the natural logarithm of biomass concentration (lnX) versus time (τ), according to the classical expression:

$$\mathsf{\mu }={\displaystyle \frac{\mathrm{d}\left(\ln \mathrm{X}\right)}{\mathrm{d}\tau }}$$

(4)

This parameter represents the rate of exponential biomass increase and was determined by linear regression over the exponential phase of growth.

• Biomass Yield on Substrate (Y_X/S)

$${\mathrm{Y}}_{\mathrm{X}/\mathrm{S}}=\left|{\displaystyle \frac{\Delta \mathrm{X}}{\Delta \mathrm{S}}}\right|$$

(5)

The biomass yield (Y_X/S) was calculated as the ratio between the net biomass formation (ΔX) and the amount of substrate consumed (ΔS) over the main growth period.

• Product Yield on Substrate (Y_P/S) and Biomass (Y_P/X)

$${\mathrm{Y}}_{\mathrm{P}/\mathrm{S}}=\left|{\displaystyle \frac{\Delta \mathrm{P}}{\Delta \mathrm{S}}}\right|$$

(6)

$${\mathrm{Y}}_{\mathrm{P}/\mathrm{X}}={\displaystyle \frac{\Delta \mathrm{P}}{\Delta \mathrm{X}}}$$

These yield coefficients were used to describe the efficiency of ethanol production from sugar and the specific productivity in relation to biomass growth.

• Specific Substrate Consumption Rate (q_s) and Specific Product Formation Rate (q_p)

$${\mathrm{q}}_{\mathrm{S}}={\displaystyle \frac{\mathsf{\mu }}{{\mathrm{Y}}_{\mathrm{X}/\mathrm{S}}}}$$

(7)

$${\mathrm{q}}_{\mathrm{p}}=\mathsf{\mu }{\mathrm{Y}}_{\mathrm{P}/\mathrm{X}}$$

The specific substrate consumption rate and specific product formation rate represent the rates of substrate uptake and product (ethanol) formation per unit of biomass, respectively.

• Volumetric Productivity (Q_p)

$${\mathrm{Q}}_{\mathrm{P}}={\displaystyle \frac{\Delta \mathrm{P}}{\Delta \tau }}$$

(8)

Volumetric productivity was calculated as the slope of product concentration over time and provides an integrated measure of fermentation performance.

2.5.3. Principal Component Analysis (PCA)

All computational analyses were performed using Python, version 3.11 (Python Software Foundation, Wilmington, DE, USA).

To assess the multivariate structure and relationships among yeast strains based on both kinetic performance and aroma-active metabolites, a principal component analysis (PCA) was performed. The dataset integrated parameters describing fermentation kinetics and volatile metabolite concentrations.

The PCA included the following kinetic parameters: specific growth rate (μ), biomass yield on substrate (Y_x/s), product yield on substrate (Y_p/s), product yield on biomass (Y_p/x), specific substrate consumption rate (q_s), specific product formation rate (q_p), volumetric productivity (Q_p), maximum specific growth rate (μ_max), and substrate saturation constant (K_s). Additionally, the concentrations of esters, aldehydes, and higher alcohols (all in mg/L) were included as representative aroma-active metabolites.

Prior to PCA, all variables were standardized using z-score normalization to ensure comparability between parameters with different units and scales. Standardization was performed using the StandardScaler function from the scikit-learn library.

The PCA was conducted using the PCA module from scikit-learn, with two principal components retained. The first two components explained a cumulative variance of approximately 64.7%, with PC1 and PC2 accounting for 47.0% and 17.6% of the total variance, respectively.

Each fermentation was categorized by yeast strain and wort type (sweet or hopped), with special attention given to control strains (S. pastorianus and S. cerevisiae). The analysis aimed to reveal clustering patterns based on fermentation behavior and to assess the impact of wort type on strain positioning in PCA space.

The resulting PCA scores were plotted, with strain labels overlaid and color-coded by wort type (ochre for sweet wort, green for hopped wort). Marker shape indicated yeast type (circles for Saccharomyces controls and squares for non-Saccharomyces strains).

2.5.4. Correlation Analysis

To assess the relationship between fermentation kinetics and metabolite formation, a Pearson correlation analysis was performed between key kinetic parameters and three classes of aroma-active metabolites: esters, aldehydes, and higher alcohols. The kinetic parameters included the following: μ—specific growth rate (1/day); Q_p—volumetric productivity (g/L/day); Y_p/s—product yield from substrate (g/g). The metabolite concentrations (mg/L) were previously quantified for each fermentation, separately for sweet wort (SW) and hopped wort (HW) fermentations. All data were compiled into a structured matrix and normalized where appropriate. Pearson correlation coefficients were calculated using Python (SciPy library). The correlations were interpreted according to standard thresholds: weak (|r| < 0.3), moderate (0.3 ≤ |r| < 0.7), strong (|r| ≥ 0.7). The resulting coefficients were presented in a correlation matrix, enabling the identification of trends, such as the association between higher fermentation activity and increased ester formation, or the potential inverse relationship between aldehyde levels and process efficiency.

3. Results and Discussion

3.1. Basic Beer Parameters

The main parameters of beer produced from sweet and hopped wort are shown in

Table 3. Basic parameters of beer produced.

Yeast Strain	Wort Type	Fermentation Time, Days	Real Extract, °P	Alcohol, % w/w	RDF, %	pH
S. pastorianus Saflager W34/70	Sweet wort	5	4.7 ± 0.10	3.70 ± 0.15	59.80	4.75 ± 0.15
	Hopped wort	9	4.8 ± 0.15	3.88 ± 0.14	61.72	4.93 ± 0.07
S. cerevisiae Safale US-05	Sweet wort	7	4.9 ± 0.16	3.47 ± 0.11	57.37	3.34 ± 0.03
	Hopped wort	12	3.8 ± 0.14	4.42 ± 0.13	68.83	4.46 ± 0.08
T. delbrueckii NS-TD	Sweet wort	8	5.3 ± 0.12	3.12 ± 0.13	54.06	3.50 ± 0.09
	Hopped wort	9	4.6 ± 0.16	3.88 ± 0.14	63.18	4.61 ± 0.11
T. delbrueckii PRELUDE	Sweet wort	5	5.2 ± 0.16	3.06 ± 0.16	54.93	4.70 ± 0.06
	Hopped wort	6	5.7 ± 0.11	3.18 ± 0.11	53.21	4.86 ± 0.07
M. pulcherrima B-NATURE	Sweet wort	12	4.6 ± 0.14	3.70 ± 0.12	60.69	3.70 ± 0.09
	Hopped wort	12	5.3 ± 0.15	3.18 ± 0.13	56.73	4.50 ± 0.11
L. thermotolerans Ns-CHANCE	Sweet wort	5	6.0 ± 0.18	2.84 ± 0.15	48.12	3.53 ± 0.12
	Hopped wort	16	3.3 ± 0.10	3.30 ± 0.16	73.45	3.63 ± 0.08
L. thermotolerans JAZZ	Sweet wort	8	6.4 ± 0.15	3.06 ± 0.16	43.95	4.04 ± 0.06
	Hopped wort	11	5.8 ± 0.11	3.30 ± 0.11	51.77	4.25 ± 0.06
L. thermotolerans NEVEA	Sweet wort	5	6.0 ± 0.15	3.00 ± 0.13	48.12	3.64 ± 0.06
	Hopped wort	9	5.5 ± 0.18	3.30 ± 0.10	47.02	3.93 ± 0.04
L. thermotolerans CONCERTO	Sweet wort	5	5.6 ± 0.17	3.06 ± 0.10	51.41	4.02 ± 0.06
	Hopped wort	9	5.9 ± 0.16	3.30 ± 0.16	52.59	4.50 ± 0.05
S. cerevisiae/T. delbrueckii WILD and PURE	Sweet wort	5	5.5 ± 0.14	2.95 ± 0.08	53.17	3.88 ± 0.10
	Hopped wort	8	6.4 ± 0.12	3.06 ± 0.10	47.84	4.67 ± 0.11

. Fermentation time varied significantly depending on both the yeast strain and the wort type. Five strains, including S. pastorianus W34/70, T. delbrueckii PRELUDE, L. thermotolerans Ns-CHANCE, L. thermotolerans NEVEA, and L. thermotolerans CONCERTO, as well as the mixed culture (S. cerevisiae/T. delbrueckii), exhibited the shortest fermentation times in sweet wort (5–6 days). This suggests higher adaptability and metabolic activity under experimental conditions. Although the fermentation temperature was more favorable for ale yeast activity, lager yeast (S. pastorianus) achieved a shorter fermentation time and higher real degree of fermentation (RDF). M. pulcherrima required the longest time (12 days), indicating a slower sugar utilization capacity, possibly due to limited maltose/maltotriose transport or suboptimal enzymatic activity. The lowest RDF was calculated for sweet wort fermentation with L. thermotolerans JAZZ.

Hop addition generally extended the fermentation time for most strains, possibly due to the antimicrobial activity of hop-derived iso-α-acids that can inhibit or slow yeast metabolism. Exceptions were observed with M. pulcherrima, whose fermentation duration remained unchanged, likely due to innate hop tolerance mechanisms.

The final pH values across all samples decreased due to organic acid production. Beers fermented with non-Saccharomyces yeasts often displayed pH values outside the typical 4.3–4.6 range [38], with L. thermotolerans Ns-CHANCE reaching 3.63, suggesting a potential for producing sour beer styles. However, variability among L. thermotolerans strains (e.g., L. thermotolerans NEVEA: pH 3.93; L. thermotolerans CONCERTO: pH 4.50) indicates that not all strains of this species are suitable for sour beer applications.

3.2. Secondary Metabolites

A great number of yeast by-products from alcoholic fermentation deeply contribute to the final taste/aroma of beer [39]. Therefore, knowledge of the synthesis of and/or reduction in different groups of metabolites is essential for the selection of the proper yeast strain. The results for the content of esters, aldehydes, and higher alcohols in the beers produced with sweet and hopped wort are presented in

Table 4. Secondary metabolites produced by yeast strains during fermentation of sweet and hopped wort.

		Esters		Aldehydes		Higher Alcohols
		SW	HW	SW	HW	SW	HW
S. pastorianus	Saflager W34/70	88.15 ± 5.56	136.5 ± 5.47	8.07 ± 0.33	6.05 ± 0.85	10.26 ± 1.02	14.62 ± 1.43
S. cerevisiae	Safale US-05	88.15 ± 4.87	72.03 ± 4.43	12.11 ± 0.40	8.07 ± 1.10	9.7 ± 1.10	9.95 ± 1.05
M. pulcherrima	B-NATURE	72.03 ± 3.88	72.03 ± 4.56	32.29 ± 1.22	8.07 ± 1.10	3.53 ± 0.42	2.39 ± 0.32
T. delbrueckii	NSTD	184.85 ± 8.63	168.73 ± 3.26	10.09 ± 1.10	76.69 ± 4.43	11.09 ± 1.23	8.19 ± 1.10
T. delbrueckii	PRELUDE	168.73 ± 5.21	72.03 ± 4.56	10.09 ± 1.60	10.09 ± 1.11	4.91 ± 0.56	4.03 ± 0.33
L. thermotolerans	CONCERTO	104.27 ± 5.44	120.38 ± 4.76	26.24 ± 2.26	12.11 ± 1.83	11.59 ± 1.59	12.98 ± 0.88
L. thermotolerans	NEVEA	152.61 ± 5.86	200.96 ± 7.86	26.24 ± 2.72	12.11 ± 1.85	11.34 ± 1.34	14.49 ± 1.05
L. thermotolerans	JAZZ	152.62 ± 6.33	72.03 ± 4.56	10.09 ± 1.10	16.15 ± 1.66	4.92 ± 0.66	1.76 ± 0.23
L. thermotolerans	NS CHANCE	120.38 ± 7.75	72.03 ± 3.45	10.09 ± 1.10	22.2 ± 2.26	3.28 ± 0.43	8.06 ± 1.10
S. cerevisiae/T. delbrueckii	WILD and PURE	72.03 ± 4.59	184.84 ± 6.34	26.23 ± 2.10	8.07 ± 0.99	8.82 ± 1.00	24.32 ± 1.33

SW—sweet wort; HW—hopped wort.

.

Esters are formed during fermentation by the enzymatic condensation of alcohol and organic acid. They are the largest group of flavor-active compounds, which impart fruity flavors to beer. Esters generally have a low odor threshold, and minor changes in their concentrations dramatically impact beer quality. Esters’ contribution to beer aroma strongly depends on the ratio between esters and higher alcohols. In lager beer, a 3–4:1 ratio of higher alcohols to esters is acceptable, while a higher ratio results in a dry taste and a less aromatic characteristic of the beer [40]. The highest ester concentrations were reported for the non-conventional yeast T. delbrueckii NSTD and L. thermotolerans NEVEA. Particularly notable were the results for L. thermotolerans NEVEA in hopped wort (200.96 mg/L) and T. delbrueckii NSTD in sweet wort (184.85 mg/L). The results for L. thermotolerans NEVEA were 1.5-times higher than the results for esters in hopped beer produced with the lager strain and 2.8-times higher than the results for the ale strain. Moreover, the ester concentration in two of hopped beers produced with T. delbrueckii strains was approximately 1.3-times higher than the lager strain and more than 2-times higher than the ale strain. This is another conformation to the fact that T. delbrueckii strains are considered good producers of esters, which is why they are proposed as cultures that can enrich the aroma of beer with fruity and floral notes [3]. Such high ester production is associated with an intense fruit and floral aroma profile sought in beer styles such as Saison, specialty ales and fruited sour ales.

Acetaldehyde is a compound produced by yeast as a by-product of ethanol fermentation that, in low concentrations, gives beverages a pleasant aroma (green apple); however, higher concentrations of this aldehyde significantly degrade flavor (etheric, pungent aroma). Increased aldehyde content may be an indicator of incomplete fermentation or cellular stress, but some aldehydes contribute to the aroma profile at low concentrations [41]. The highest value was recorded for T. delbrueckii NS-TD in hopped wort (76.69 mg/L) and M. pulcherrima B-NATURE in sweet wort (32.29 mg/L). However, most strains maintained moderate levels (<20 mg/L) in hopped wort fermentation, which is suitable from an aromatic point of view.

Higher alcohols are formed by yeast during fermentation via the catabolic (Ehrlich) and the anabolic (amino acid metabolism) pathways and can be used as precursors for ester synthesis. Higher alcohols contribute to the alcoholic or solvent-like aroma of beer and produce a warm feeling in the mouth [42]. Amounts of higher alcohols higher than 300 mg/L can lead to a pungent smell and taste in beer, whereas optimal levels impart desirable characteristics. The optimum concentration of higher alcohols in 12.0 °P beers brewed via bottom fermentation is 70–120 mg/L [40]. The highest production of higher alcohols was observed for the beer produced with hopped wort and mixed yeast cultures, which were 1.7-fold higher than the lager strain and almost 2.5-fold higher than the lager strain. The lowest higher alcohols concentration was measured in beer, produced with L. thermotolerans JAZZ.

The analysis of the results of the secondary metabolites shows significant differences in aroma formation. In non-conventional yeasts, this process is enhanced, and for individual groups of metabolites, the concentration reaches up to 2.8-times higher values than in standard yeast strains used in brewing. These elevated ester levels suggest strong potential for enriching beer aroma with fruity and floral notes, making such strains promising candidates for specialty and aromatic beer styles. Aldehyde production remained moderate in most samples, comparable to standard strains and below the sensory threshold for off-flavors, with some exceptions (T. delbrueckii NS-TD and M. Pulcherrima), which showed increased values, likely due to fermentation stress or the incomplete reduction of acetaldehyde. Regarding higher alcohols, levels in beers fermented with non-conventional yeasts were generally within acceptable sensory limits. Of course, these observations depend strictly on the fermentation conditions used and, above all, the temperature regime used, through which beers with different sensory characteristics can be obtained.

The study of the influence of different fermentation temperatures, as well as hopping (varying different hop types and concentrations), is the subject of future research.

3.3. Comparative Assessment of the Fermentation Kinetics: Statistical Processing and PCA

The kinetic parameters of the fermentation process provide an understanding of the influence of various parameters, such as pH, aeration, type and amount of substrate, as well as the influence of parameters such as hopping, for example. These parameters can be used to predict the dynamics of the fermentation process when changing conditions, as well as to scale the processes from the laboratory to industrial scale. Additionally, kinetic analysis allows for a comparative assessment of different types of yeast when the parameters of the environment are dimensionless.

Combining the kinetics of the fermentation process with statistical processing such as correlation analysis and principal component analysis provides a thorough analysis of the obtained results and allows for the grouping of yeasts into different groups. Such an approach has not been applied in the scientific literature to date when studying non-conventional yeast strains for beer production and represents a significant novelty in the present study. PCA reduces the dimensionality of the data by transforming intercorrelated variables into independent principal components, preserving the maximum variation. Correlation analysis allows for an assessment of the strength and direction of the relationship between different variables characteristic of the fermentation process and, in this case, an assessment of the hopping parameter.

To determine the fermentation kinetics, the fermentation dynamic was calculated as described in Section 2.5.1. The data on the fermentation dynamics of M. pulcherrima B-NATURE on sweet and hopped wort are shown in Figure 1. The data on the other fermentations can be found in the Supplementary File (Figures S1–S9).

The kinetic parameters for each of the fermentations are presented in

Table 5. Kinetic parameters of fermentations with Saccharomyces and non-Saccharomyces yeast strains.

		μmax (Day−1)		Yx/s		Yp/s		Yp/x		qs		qp		Qp
		SW	HW	SW	HW	SW	HW	SW	HW	SW	HW	SW	HW	SW	HW
S. pastorianus	Saflager W34/70	0.081	0.053	0.100	0.090	0.561	0.539	5.606	5.969	0.809	0.583	0.453	0.314	0.740	0.431
S. cerevisiae	Safale US-05	0.070	0.047	0.100	0.092	0.542	0.549	5.420	6.000	0.702	0.508	0.381	0.279	0.496	0.375
M. pulcherrima	B-NATURE	0.046	0.014	0.100	0.090	0.552	0.475	5.520	5.300	0.464	0.159	0.256	0.075	0.308	0.265
T. delbrueckii	NSTD	0.017	0.007	0.100	0.091	0.533	0.524	5.330	5.791	0.168	0.082	0.090	0.043	0.400	0.431
T. delbrueckii	PRELUDE	0.071	0.067	0.100	0.089	0.531	0.505	5.310	5.680	0.713	0.755	0.379	0.381	0.648	0.530
L. thermotolerans	CONCERTO	0.064	0.047	0.100	0.089	0.537	0.541	5.370	6.110	0.639	0.528	0.343	0.285	0.612	0.367
L. thermotolerans	NEVEA	0.067	0.057	0.100	0.089	0.566	0.508	5.660	5.690	0.668	0.641	0.378	0.325	0.600	0.367
L. thermotolerans	JAZZ	0.028	0.014	0.100	0.089	0.625	0.532	6.245	6.000	0.281	0.153	0.175	0.082	0.383	0.300
L. thermotolerans	NS-CHANCE	0.062	0.046	0.100	0.092	0.536	0.556	5.359	6.050	0.620	0.500	0.332	0.278	0.568	0.303
S. cerevisiae/T. delbrueckii	WILD and PURE	0.069	0.046	0.100	0.088	0.509	0.546	5.086	6.245	0.685	0.526	0.349	0.287	0.590	0.383

SW—sweet wort; HW—hopped wort.

. The data in Table 5 show that the specific growth rate μ was the highest for both lager and ale strains in sweet wort, due to the fact that these strains are typical beer strains and, therefore, they grow best in wort. However, the specific growth rate of T. delbrueckii PRELUDE in sweet wort was comparable to that of the ale strain. Moreover, the highest specific growth rate in hopped wort was calculated for T. delbrueckii PRELUDE, and it could explain the shortest fermentation time for this yeast strain (Table 1).

The yield coefficients (Y_x/s, Y_p/s, Y_p/x), especially Y_p/s, were quite variable compared to the lager and ale fermentations, which indicated different fermentation pathways, which is typical for non-Sacharomyces yeasts. Some of the non-conventional yeasts used (e.g., Lachancea, Torulaspora) showed high yields of product per unit substrate, which indicated good adaptation to the wort as a substrate (Table 5,

Table 6. Comparison between kinetic parameters of non-Saccharomyces and lager yeast strains.

		μmax (day−1)		Yp/s		qp		Qp
		SW	HW	SW	HW	SW	HW	SW	HW
M. pulcherrima	B-NATURE	0.574	0.271	0.985	0.881	0.565	0.240	0.416	0.615
T. delbrueckii	NSTD	0.208	0.141	0.950	0.973	0.198	0.137	0.541	1.000
T. delbrueckii	PRELUDE	0.882	1.274	0.947	0.937	0.836	1.212	0.876	1.229
L. thermotolerans	CONCERTO	0.790	0.887	0.958	1.004	0.757	0.907	0.827	0.851
L.thermotolerans	NEVEA	0.826	1.086	1.010	0.942	0.834	1.035	0.811	0.851
L.thermotolerans	JAZZ	0.347	0.258	1.114	0.988	0.387	0.259	0.517	0.696
L. thermotolerans	NS CHANCE	0.767	0.873	0.956	1.032	0.733	0.885	0.768	0.702
S. cerevisiae/T. delbrueckii	WILD and PURE	0.848	0.873	0.907	1.014	0.769	0.914	0.797	0.887

and

Table 7. Comparison between kinetic parameters of non-Saccharomyces and ale yeast strains.

		μmax (day−1)		Yp/s		qp		Qp
		SW	HW	SW	HW	SW	HW	SW	HW
M. pulcherrima	B-NATURE	0.661	0.306	1.018	0.866	0.672	0.270	0.621	0.707
T. delbrueckii	NSTD	0.239	0.160	0.983	0.955	0.235	0.154	0.806	1.150
T. delbrueckii	PRELUDE	1.016	1.444	0.980	0.920	0.995	1.367	1.306	1.413
L. thermotolerans	CONCERTO	0.910	1.005	0.991	0.986	0.900	1.022	1.234	0.979
L.thermotolerans	NEVEA	0.951	1.231	1.044	0.925	0.992	1.167	1.210	0.978
L.thermotolerans	JAZZ	0.400	0.292	1.152	0.970	0.460	0.292	0.771	0.800
L. thermotolerans	NS CHANCE	0.884	0.989	0.989	1.014	0.872	0.997	1.145	0.807
S. cerevisiae/T. delbrueckii	WILD and PURE	0.976	0.990	0.938	0.996	0.915	1.030	1.190	1.020

).

The values of the specific substrate consumption rate (q_S) and specific product accumulation rate (q_P) of non-conventional yeast were significantly different than the Saccharomyces yeasts. In some cases, especially for q_P, it was 20–30% higher than the control variants with Saccharomyces yeasts, and in some cases, the parameter was up to 70% lower than the controls. This was due to the different adaptation of the cells to the wort, a substrate that was rather atypical for these yeast types (Table 5, Table 6 and Table 7).

The volumetric productivity Q_p of most non-conventional yeasts in hopped wort was lower than the lager yeast strain. Only T. delbrueckii PRELUDE showed 1.2–fold higher volumetric productivity. However, when a comparison was made with the ale strain, three non-conventional yeasts showed higher results—two T. delbrueckii strains and the mixed culture, which contained T. delbrueckii. This showed that T. delbrueckii strains have high potential for beer production, especially with good optimization of the fermentation process parameters (Table 6 and Table 7).

The results of the comparison of some of the kinetic parameters are presented in Figure 2. The data showed that part of the strains were distributed in a group that gave us a reason for searching for such a distribution using principal component analysis (PCA).

In order to comprehensively assess the fermentation characteristics and aromatic potential of the tested yeasts, a principal component analysis (PCA) was performed (Figure 3). As shown in Figure 3, PC1 (47.0% variance explained) and PC2 (17.6% variance explained) effectively grouped the yeasts based on kinetic parameters and secondary metabolites. These two PCs were important to distinguish the tested yeast strains according to their technological properties. PCA revealed that yeast fermentations conducted in sweet wort exhibited more pronounced clustering compared to those in hopped wort, particularly among non-Saccharomyces strains. Most sweet wort samples were distributed throughout the positive range of PC1, suggesting comparable fermentation kinetics, likely reflecting similar sugar consumption rates and ethanol production across strains. Within this region, strains, such as S. pastorianus Saflager W34/70, S. cerevisiae Safale US-05, T. delbrueckii PRELUDE, L. thermotolerans Ns-CHANCE, L. thermotolerans CONCERTO L. thermotolerans NEVEA, and the mixed culture, were grouped closely, indicating metabolic similarity in the absence of hop-derived stress. Vertical dispersion along PC2 (ranging from approximately −1 to +1) within this cluster may reflect subtle differences in secondary metabolite production, such as esters, higher alcohols, or aldehydes. Outliers such as M. pulcherrima B-NATURE and L. thermotolerans JAZZ, positioned on the negative side of PC1, may represent strains with slower fermentation kinetics or distinct metabolic pathways. Notably, control strains in sweet wort were located within the same PC1 range as the non-Saccharomyces cluster, suggesting that under non-hopped conditions, yeast strains displayed more uniform fermentation profiles. Collectively, these observations indicated that sweet wort promoted similar fermentation behavior in different yeast species, with strain-specific differences primarily manifesting in metabolite composition rather than fermentation efficiency.

In contrast to the patterns observed in sweet wort, yeast fermentations in hopped wort displayed a markedly broader distribution across the PCA space, particularly among non-Saccharomyces strains (Figure 3). This dispersion likely reflected the different sensitivity to hop-derived antimicrobial compounds, such as iso-alpha acids, which are known to disrupt yeast membrane integrity and suppress growth in susceptible strains [1]. Some strains, including L. thermotolerans JAZZ, T. delbrueckii NSTD, and M. pulcherrima B-NATURE, are clustered in the lower quadrants, suggesting impaired fermentation or altered metabolite profiles under hop-induced stress. The ale and lager yeast strains in hopped wort were more tightly grouped and generally occupied central or upper-left positions, indicating more consistent behavior relative to the dispersed non-Saccharomyces group. Others, such as L. thermotolerans NEVEA and L. thermotolerans CONCERTO, which were positioned next to the lager strain, possibly showed enhanced metabolic adaptation or increased production of flavor-active compounds such as esters or fusel alcohols. These findings highlighted the role of hops as a tool for the selection of yeast strains suitable for brewing fermentation.

PCA analysis demonstrated the following:

• Some of the non-Sacharomyces yeasts have profiles similar to classic brewing strains.
• Others could be used as alternatives with distinctive aromatic capacity.
• The type of wort (sweet or hopped) had a significant impact on the positioning in the component space, which should be taken into account in technological selection.

An overall correlation analysis was performed to establish the relationship between the main kinetic parameters of the fermentation (specific growth rate—μ, volumetric productivity—Q_p, and product yield from substrate—Y_p/s) and the main secondary metabolites (esters, aldehydes, and higher alcohols). The values of the correlation coefficients (Pearson) are presented in

Table 8. Overall correlation coefficients between kinetic parameters and secondary metabolite concentration.

Metabolites	μ	Qp	Yp/s
Esters	0.55	0.62	0.48
Aldehydes	−0.15	−0.33	−0.21
Higher Alcohols	0.44	0.38	0.35

. The results showed a positive correlation between the ester and higher alcohols concentration and kinetic parameters, suggesting that more intense fermentation activity resulted in enhanced higher alcohols and ester synthesis. The negative correlation between aldehydes and fermentation parameters indicated that better fermentation activity led to a reduction in aldehydes.

3.4. Influence of Hopping on the Growth and Fermentation Activity of Non-Conventional Yeasts

Hopping is an important process in beer production, through which the drink is given a typical hop flavor and aroma, and its composition is stabilized. The characteristic beer flavor profile is due to various hop components (bitter acids and essential oils), but, at the same time, these components can have a significant impact on the beer stability. It is known that hop components can have antimicrobial effects, mainly on lactic acid bacteria but also on some yeast species [14,15,43]. The method and time of hop addition are of great importance for the hop flavor and aroma but also for the antimicrobial effects of some components [43,44,45,46,47,48].

Hops are a source of α-acids (mainly humulones) and β-acids, which, after isomerization during brewing, exhibit a pronounced antimicrobial effect. Although these compounds are traditionally effective against bacteria [49,50], the influence of hopping on the growth of non-conventional yeast species would be of interest. The results of this study revealed that hopping has a statistically significant effect on the growth and the metabolic activity of non-conventional yeast strains used in the fermentation process. This effect is expressed both at the level of biomass growth, on the kinetics of substrate uptake and the formation of secondary metabolites.

The PCA and correlation analysis performed show that Metschnikowia pulcherrima and Lachancea thermotolerans are sensitive to the presence of hop compounds, which affects the kinetic parameters (reduction in the specific growth rate) and, hence, the secondary metabolism associated with the growth of the microorganisms. Other strains, such as Torulaspora delbrueckii (PRELUDE and NSTD), show higher resistance to hopping and the preservation of fermentation activity, which makes them suitable for beers with a higher degree of hopping.

The study of the influence of hops on non-conventional species is an interesting scientific area that can be investigated. Such a study should include a wide range of parameters—hop species, hopping method and regime, amount of hops added, etc. The interaction between yeast selection and hopping methods (e.g., timing and intensity of hop additions) also significantly shapes the beer’s flavor profile, emphasizing the need for brewers to consider yeast and hop strategies synergistically [9,51]. From the point of view of the beer aroma profile, it should be noted that the richer enzyme profile of non-Saccharomyces species can have a significant impact on the beer flavor profile. For example, Brettanomyces custersii has shown higher β-glucosidase activity, which leads to the release of aroma components from hop glucosides [52]. Similarly, analogous dependencies in the studied strains can be sought in the future.

3.5. Sensory Evaluation and Application of the Studied Strains for the Production of Different Beer Styles

All variants showed good organoleptic characteristics. A detailed evaluation of the organoleptic characteristics is planned to be carried out when optimizing the fermentation regimes for each individual yeast species, which will be carried out in future studies. Based on the results obtained, we can summarize the application of the studied species for different beer styles (

Table 9. Key traits and beer styles.

Yeast Strain	Key Traits	Suggested Beer Styles
Torulaspora delbrueckii (Prelude, NS-TD)	Fruity esters Good hop tolerance Moderate higher alcohols	Blonde Ale Specialty Ales Dry-Hopped Pale Ales
Lachancea thermotolerans (Nevea, Concerto, NS-Chance)	Low pH Moderate alcohol	Sour Beers Fruited Gose
Metschnikowia pulcherrima (B-Nature)	Slow fermentation Low alcohol	Low-Alcohol Beers Specialty Herbal Beers
S. cerevisiae/T. delbrueckii (Wild and Pure)	Aromatic complexity Moderate acidity High higher alcohols	New England IPA Mixed-Fermentation Beers

).

4. Conclusions

A comparative analysis of the possibilities of using non-conventional yeasts for beer production was conducted, and the data were compared with the fermentation carried out with two brewing strains—one lager and one ale. The data from the study showed that non-conventional yeasts had significant potential for application in beer fermentation. Strains from the genera Metschnikowia, Torulaspora, and Lachancea demonstrated potential for the production of fermentation metabolites, in some cases higher than those obtained with traditional brewing yeasts. Wort hopping had a clear impact on the yeast kinetics and metabolite production, with the suppression of μ and qs observed in certain strains. The obtained data confirm the possibility of the targeted selection of strains and technological parameters with the aim of optimizing fermentation processes and developing innovative products with a sustainable nature.

An important aspect of the results obtained is the assessment of the influence of hopping on the fermentation capacity of the studied species from the genera Metschnikowia, Torulaspora and Lachancea. The obtained data show that when using these species for beer production, the type and amount of hops should be carefully considered in order to obtain high-quality beer. For this purpose, additional analyses can be conducted to assess the type of hops, the stages of their use and, last but not least, the amount used. The combined approach with kinetic analysis and multivariate analysis can allow for the assessment of more parameters.