Optimization of Ale-Type Craft Beer Through the Addition of Cañihua Malt (Chenopodium pallidicaule) and Aguaymanto Juice (Physalis peruviana) Using a D-Optimal Experimental Design

Abstract

The global growth of the craft beer market has driven the use of native ingredients to improve the sensorial and nutritional qualities of the product. This study investigated the optimization of an Ale-type craft beer from Pilsen malt (PM) with the addition of cañihua malt (CM) and aguaymanto juice (AJ), using a D-optimal experimental design. The independent variables were CM (5–25%) and AJ (5–15%), which influenced the physicochemical, technological, and sensorial attributes of the beer. The results show that CM and AJ improve the physicochemical properties of the beer, such as foam stability and alcohol content, while maintaining comparable levels of specific gravity, turbidity, and bitterness with the control sample. The addition of AJ significantly altered the physicochemical profile of the beer, in particular by reducing pH and increasing acidity. Sensory analysis revealed positive consumer acceptance, with favorable evaluations of aroma, appearance, and body, particularly in samples containing moderate levels of CM and AJ. In addition, consumer purchase intention was high for these formulations. Optimization through the desirability function determined that the ideal ingredient concentrations were 74.52% PM, 15.55% CM, and 8.93% AJ. Within the ranges studied, it is concluded that the addition of CM and AJ successfully produced a craft beer with notable nutritional benefits and high sensory acceptability.

1. Introduction

In recent years, the craft beer industry has experienced remarkable global growth, characterized by innovation in production and a focus on creating unique products that stand out for their superior quality and a variety of complex flavors [1]. This trend reflects a shift in consumer preferences towards exclusive craft beer offerings, moving away from mass-produced options. This significant shift began in the early 1990s with the rise of microbreweries in the United States [2], marking a departure from traditional brewing methods and ushering in a new era of craft beers with distinctive and appealing flavor profiles [3].

In this context, the incorporation of indigenous ingredients not only responds to the demand for innovation in the sector, but also encourages the valorization of traditional resources. Cañihua (Chenopodium pallidicaule), an Andean pseudocereal, and aguaymanto (Physalis peruviana), a typical fruit of the Andean region, have great potential to enrich the sensory and nutritional profile of craft beers, creating products with local identity and high added value.

Cañihua is a pseudocereal native to the Peruvian Andes, known for its high content of protein, fiber, minerals, antioxidants, and essential fatty acids, making it a valuable ingredient in the production of foods and beverages due to its flavor and nutritional benefits [4]. Although it already has a high nutritional quality, this can be improved through processes such as germination and malting. Malting has been less studied in pseudocereals such as cañihua. This is because the process is different from that applied to conventional cereals, such as barley, wheat, or corn, due to the physical characteristics and composition of the seeds. Although cañihua seeds germinate quickly due to their small size, their starch has a higher viscosity, which requires a longer maceration time and controlled parameters [5]. However, its malt could provide unique nutritional and organoleptic characteristics to beer, enriching its flavor. Castañeda et al. [6] made an Ale-type beer based on malted barley with the addition of malted quinoa, Bustillo et al. [7] malted quinoa and sorghum for a gluten-free beer, and Montenegro [8] made a craft beer by replacing barley malt with amaranth malt; however, these had a lower density, a higher alcohol content, and turbidity.

Likewise, aguaymanto is a tropical fruit with a sweet and pleasant taste, rich in antioxidants and vitamin C [9]. It is highly valued for its flavor and nutritional properties, especially for its mucilage (pulp), which has interesting functional characteristics for the food industry [10]. Thanks to its low pH, it is suitable for the formulation of fermented products. Its freshness and exotic flavor could complement the organoleptic and nutritional qualities of an Ale-type beer, offering a unique sensory experience.

The production of Ale-type craft beer is distinguished by its fermentation process at higher temperatures, which results in richer, fruitier flavors [11,12]. This type of beer encompasses a wide range of styles, from light and refreshing pale ales to more intense and darker varieties such as porters and stouts. The combination of cañihua malt with aguaymanto juice, both rich in antioxidants, creates a distinctive beer in terms of flavor and aroma while also offering nutritional benefits, positioning this beer as a healthier option in the alcoholic beverage market [13].

The aim of this study was to optimize the formulation of an Ale-type craft beer with a Pilsen malt (PM) base, supplemented with cañihua malt (CM) and aguaymanto juice (AJ), using a D-optimal mixture design.

2. Materials and Methods

2.1. Raw Materials

The raw materials used in this study included Pilsen malt (PM) (32 Best Malz, Heidelberg, Germany), Simcoe hop pellets (Yakima Chief Hops, Washington, EE.UU.), 13.5% alpha acid w/w and Saccharomyces cerevisiae SafAle US-05 yeast (Lima, Peru). Cañihua grains (var. Illpa-INIA) were provided by the Experimental Agricultural Station of the National Institute of Agrarian Innovation (Puno, Peru), and aguaymanto (Physalis peruviana) were supplied from Musho, a town located at 3050 m.a.s.l. in the city of Yungay, Peru.

Prior to malting and analysis, cañihua grains were manually sifted to discard damaged and visibly discolored seeds. Seed moisture content was analyzed according to AACC method 44-01.01 [14], prior to malting.

In the case of aguaymanto, fruits were washed with running water, disinfected in a 200 mg/L solution for 15 min, and rinsed with distilled water. Physicochemical characterization of the fruit included measurement of the equatorial diameter of 15 randomly selected fruits using a digital Vernier (IP54, Fujian, China). To obtain AJ, the pulp was processed using a multipurpose pulping and refining machine (Electronica Venetta, PAS/EV, Treviso, Italy). The instrumental color of the juice was evaluated using a Minolta CR-310 colorimeter (Osaka, Japan), with results obtained in triplicate in the CIE-Lab* color space. The pH was determined according to the AOAC method [15] and the titratable acidity was determined following the IRAM 14520 [16] standard, expressed as a percentage of citric acid.

Figure 1 presents a flowchart illustrating the processing of raw materials prior to beer production. The various steps involved are described, from the initial selection of ingredients to the preparation stages, highlighting the critical processes that contribute to the final product.

2.2. Production of Cañihua Malt

For malting of cañihua grains, the optimal germination parameters of Abderrahim et al. [17] were used, with some modifications. Therefore, four steps were developed: hydration, germination, drying, and deculming. First, the grains were washed and disinfected using a sodium hypochlorite solution (0.1%) for 30 min. The grains were washed with running water until reaching neutral pH and soaked in distilled water for 6 h in a grain/water ratio of 1:5 at 25 °C and in darkness. Then, for the germination process, the hydrated grains were placed in plastic trays covered with damp paper towels and placed in a pilot-scale germination chamber (Maquilak, Lima, Peru). The optimal germination conditions were 72 h at 20 °C to increase the content of bioactive compounds and antioxidant capacity. When the radicles reached a length of 7–10 mm, germination was stopped and the germinated grains were dried by air convection in an oven (Poleko, Poland) at 45 °C for 24 h, reaching a moisture content of 5–8%. Then, the radicles were removed manually. Finally, the cañihua base malt (Figure 1) was obtained after being ground in a blade mill (Brabender, Buisburg, Germany), sieved to obtain 0.25 mm particle size, and stored in the dark in vacuum plastic bags at −20 °C, before being analyzed.

The physicochemical characterization of the cañihua malt was performed as follows. The germination rate was performed on 100 g of grains in triplicate and the percentage was determined using the Valenzuela methodology [18]. The proximate composition of the cañihua malt was determined using the methods of the American Association of Cereal Chemists International [14], which include moisture content (method 44-01.01), protein (method 46-13.01, with a nitrogen-to-protein conversion factor of 5.7), ether extract (method 30-25.01), dietary fiber (method 32-05.01), and ash (method 08-01.01). Digestible carbohydrate content was calculated by subtracting the values for protein, ether extract, dietary fiber, and ash from the sample’s initial dry weight. Additionally, total starch and phytic acid content were measured using enzymatic kits K-TSTA-100A and K-PHYT (Megazyme, Wicklow, Ireland), respectively.

2.3. D-Optimal Experimental Design

A D-optimal experimental design was used to optimize the production of craft beer, considering three independent variables: PM (X₁), CM (X₂), and AJ (X₃). The combinations were designed within the following ranges: 70–80% for PM, 5–25% for CM, and 5–15% for AJ. The Design Expert 7.0 software generated 13 formulations, the details of which are presented in

Table 1. D-optimal experimental design for craft beer optimization.

Experiment	PM (%)	CM (%)	AJ (%)
E1	70	21	9
E2	75	15	10
E3	74	16	10
E4	75	15	10
E5	80	13	7
E6	70	15	15
E7	75	15	10
E8	76	19	5
E9	70	15	15
E10	70	25	5
E11	80	13	7
E12	78	7	15
E13	75	11	14

Abbreviations: Pilsen base malt, PM; cañihua malt, CM; aguaymanto juice, AJ.

.

2.4. Production of Ale-Type Craft Beer

The production process for craft ale beer is detailed in Figure 2. First, it began with the reception and weighing of the ingredients, followed by the grinding of the grains. Then, the mashing phase was carried out to obtain the wort, for which a 1:4 ratio (grain/water) was used, using a combination of grain formed by PM and CM in proportions defined according to the experimental design (Table 1). Then, the water was heated to 76 °C, at which time the grain mixture was immersed in a nylon bag, causing the temperature to decrease to a range of 65–68 °C and maintaining it there for 90 min to allow the enzymatic conversion (saccharification) of the starches into fermentable sugars. During this stage, the water was constantly stirred to ensure an even distribution of heat and enzymes. Afterwards, the wort was subjected to a filtration and recirculation process to clarify and eliminate suspended particles. This stage consisted of separating the grains from the wort with the help of a strainer and the wort was recirculated 15 times in the form of rain through the grain so that it would filter out the small particles that remained. In this way, the grain would clean the wort and in the end we would obtain a much more crystalline beer as well as oxygenating the wort. To finish extracting all the sugars from the grain, the malt in the strainer was sprinkled with hot water at 77 °C.

Next, the wort was cooked for 1 h, which will allow the bitterness of the hops to be extracted. When the water reached 100 °C, the hops were added in three phases, and the AJ was added before finishing the boiling. The wort was then rapidly cooled to a temperature between 15 °C and 25 °C. At this point, the wort density was measured and ranged between 1.044 and 1.052 g/cm³. The cooled wort was transferred to a 30 L food-grade polyethylene fermenter fitted with a bottling spout and an airlock cap (Vintners Best, model 30FT, Lima, Peru), inoculated with US-04 yeast, and fermented at 16 °C for 5 days. After fermentation, the final gravity was measured (1.010–1.015 g/cm³), followed by maturation at 0–4 °C for 3 days. Carbonation was achieved by adding 6 g/L of sucrose to the product, followed by bottling and the second stage of maturation which lasted for 2 weeks at 16 °C, allowing for complete carbonation. Finally, the beer was cooled to 4–6 °C, ready for further analysis.

2.5. Physicochemical and Technological Characterization of the Beer

The pH was determined using method 9.35 of EBC Analytica [19] with a pH meter, and the final pH for the beers ranged from 3.0 to 4.8. Soluble solids were measured with a refractometer, according to the food analysis manual of Kirk et al. [20]. Acidity was evaluated with potentiometric titration, following the IRAM 14520 [16] standard with slight modifications: 10 mL of sample was placed in a 20 mL Erlenmeyer flask and titrated dropwise with a 0.1 N NaOH solution until a pH of approximately 8.3 was reached, and the NaOH volume was recorded. Density was determined with a portable digital densitometer measured at 20 °C, according to AOAC method 988.06 [21], and the values are expressed in g/mL. Viscosity was determined using an Ostwald viscometer according to ASTM Standard [22], which measures the relative viscosity of a liquid by recording the flow time. Alcohol content was determined via gas chromatography with a flame ionization detector. Color was measured following method 9.6 of EBC Analytica [23], using a spectrophotometer to record absorbance at 430 nm after degassing the sample; in cases of turbidity, the sample was clarified via filtration before measurement, and results were expressed in EBC color units.

Turbidity was measured using a turbidimeter, following method 9.29 of EBC Analytica [24], and results were expressed in Nephelometric Turbidity Units (NTUs). Foam capacity was evaluated using the method described by Garduño et al. [25], which describes stirring 40 mL of beer manually until foam is generated to measure the volume of liquid (VL), the total volume (VT), and the foam volume (VE).

Bitterness was determined using method 9.8 of EBC Analytica [26] with a spectrophotometer to extract alpha acids from the hops; absorbance was measured at 275 nm, and results were expressed in International Bitterness Units (IBU = 50 × Abs 275 nm).

2.6. Sensory Analysis Test

Sensory analysis was conducted with the participation of 120 panelists in the sensory evaluation booths of the Agroindustrial Pilot Plant at the Universidad Nacional del Santa. The analysis was carried out in two stages. In the first stage, an acceptability test was conducted, where each consumer evaluated samples of 13 experimental formulations, distributed across three sessions under a balanced complete block design. Beer samples (150 mL) were served in clear glass cups (250 mL) coded with random three-digit numbers. The evaluated variables were aroma, color, taste, and appearance, using an unstructured scale from 0 to 10, where 0 indicated “dislike very much” and 10 “like very much”. Additionally, purchase intent was measured using a 5-point hedonic scale, where 1 represented “definitely would not buy” and 5 “definitely would buy”. In the second stage, a “Check-All-That-Apply” (CATA) test was applied, where participants selected all terms they considered relevant for each sample. Thirty-seven attributes were included, randomly organized into five categories: appearance (opaque, shiny, transparent, foamy, low foam, bubbly), color (yellow, golden, caramel, brown, light, medium, intense), aroma (fruity, herbal, roasted grains, spicy, floral, citrus), taste (roasted cereal, malt, bitter, sour, citrus, sweet, fruity, herbal, refreshing, caramel, light, intense), and mouthfeel (viscous, watery, astringent, effervescent, foamy, delicate). The data for each characterization were recorded in Excel sheets and later processed using established data processing techniques for this research.

2.7. Statistical Analysis

The statistical analysis of the experimental data was carried out using Design Expert software, version 7.0, applying multiple regression models to assess the impact of variables on the physicochemical and technological properties of the craft beer. The relationship between response variables and factors was analyzed through response surface methodology (RSM). Linear, quadratic, and cubic regression models were tested, selecting the most significant (p < 0.05) and best-fitting model (R² > 0.70).

To identify significant differences between samples, a one-way analysis of variance (ANOVA) was applied. When significant differences (p < 0.05) were detected, multiple comparisons were performed using the Bonferroni post hoc test, particularly in sensory trials, to determine the most accepted samples. In analyzing the CATA test data, Cochran’s Q test was used to identify discriminant terms (p ≤ 0.10). Significant terms were further evaluated by correspondence analysis and principal coordinate analysis to examine the relationship between sensory attributes and overall product acceptance. All analyses were performed using XLSTAT software, version 2018.5 (ADDINSOFT, New York, NY, USA).

3. Results and Discussion

3.1. Raw Material Characteristics

Physalis peruviana—“Aguaymanto”.

The aguaymanto fruits had a small round shape with orange-yellow skin and are wrapped in a thin shell with an average diameter of 17.6 mm.

Regarding AJ, the pH was recorded at 3.64, with an acidity of 2.01%, and a soluble solids content of 12.5 °Brix. These values are comparable to those reported by Ramos et al. [27], who documented a pH of 3.95 and an acidity of 1.93%, since its flavor is a mixture of sweet and sour. Likewise, given its higher acidity than other analyzed samples, it may be suggested that it is due to the content of organic acids influenced by agroecological factors [28,29].

Regarding soluble solids (°Brix), the comparative studies by Muñoz et al. [28] and Bazalar et al. [29] indicate values of 13.40 ± 0.10 and 14.80 ± 0.10, respectively, much higher than the 6.82 °Brix recorded in AJ [30]. This difference may be due to variations in fruit ripeness at the time of juice extraction or differences in cultivation techniques and post-harvest handling. The color parameters L*, a*, and b* are important for the visual characterization of the fruit and its sensory acceptance. In this case, L* (lightness) is 43.96, a* (green–red axis indicator) is 38.94, and b* (blue–yellow axis indicator) is 52.58, which describes a bright color with yellowish and reddish tones. The average fruit diameter is 1.76 cm, consistent with the description of the fruit as a small to medium-sized berry.

Color analysis of AJ revealed instrumental values of luminosity (L*) of 43.96, chromacy (a*) measuring the red–green component of 38.94, and chromacy (b*) reflecting the yellow–blue component of 52.58, indicating a strong tendency towards yellowish and reddish tones, probably related to its acidity and anthocyanin content, compounds responsible for the red color in many fruits [31].

The luminosity (L*) values suggest an intermediate tone, while the b* values indicate a shift towards orange, attributable to the presence of carotenoids [32]. In addition, AJ is rich in vitamin C and other volatile compounds, which also influence its chromatic and sensory characteristics [33].

Cañihua malt (CM).

The physicochemical characterization of cañihua malt is presented in

Table 2. Nutritional composition of cañihua malt Illpa-INIA variety.

Composition	Cañihua Malt (g/100 g)
Moisture	10.01
Protein	20.61
Fat	6.18
Ash	2.76
Starch	41.21
Total dietary fiber	8.08
Insoluble dietary fiber	6.32
Soluble dietary fiber	1.76

, showing parameters such as moisture, protein, fat, ash, starch, and dietary fiber content. The cañihua malt exhibited moderate levels of moisture (10.01%), protein (20.61%), fat (6.18%), and total dietary fiber (8.08%), with a significant amount of starch (41.21%). When compared to the values reported of Repo-Carrasco-Valencia et al. [34], it is observed that the moisture content of the germinated cañihua in this study is slightly higher, while the protein concentration is significantly greater (20.61% vs. 15.6–17.0%). This discrepancy may be attributed to differences in germination and processing conditions, which can influence enzymatic activity and, consequently, the final composition of the germinated grain. In terms of fat content, our results show a slightly lower percentage (6.18% vs. 6.8–8.2%), which could be explained by variations in oil extraction techniques or differences in cañihua varieties used. The ash content was lower (2.76% vs. 3.6–4.0%), indicating differences in mineral content following germination.

When compared with the germinated cañihua flour of Paucar-Menacho et al. [35], the moisture content is consistent with our results (10.01%), suggesting similar drying processes. However, the protein content is slightly lower (19.11% vs. 20.61%), which may be due to variations in grain quality or germination conditions. The fat content is similar (6.23% vs. 6.18%), confirming consistency in lipid composition. Minor differences in ash content (2.68% vs. 2.76%) could be attributed to slight variations in analytical methods or the origin of the grain.

3.2. Physicochemical and Technological Characteristics of Craft Beers

Table 3. Effect of beer formulation on the physicochemical and technological characteristics * of craft beer.

Exp.	pH	Titratable Acidity (g/mL)	Soluble Solids (°Brix)	Turbidity (NTU)	Absolute Density (g/mL)	Viscosity (mPa·s)	Color (EBC)	Alcohol Content (%)	Foaming Capacity (%E)	Bitterness (IBU)
E1	4.21 abc	0.42 h	4.75 f	28.13 d	1.019 ab	1.43 cdef	10.08 ab	5.53 def	52.50 bc	16 b
E2	4.12 a	0.36 fgh	4.50 e	21.41 bc	1.018 ab	1.42 cde	8.62 a	5.89 ef	42.50 b	16 b
E3	4.24 abc	0.32 a	3.75 b	25.88 cd	1.015 ab	1.39 bc	9.23 ab	5.32 cdef	12.50 a	16 b
E4	4.12 a	0.35 cde	4.50 e	22.06 bcd	1.018 ab	1.43 cdef	9.16 ab	6.07 f	102.50 fg	16 b
E5	4.20 abc	0.34 cd	4.25 d	102.32 g	1.017 ab	1.48 fg	9.05 ab	5.41 cdef	85.00 e	15 a
E6	4.33 cde	0.41 gh	5.50 g	41.71 e	1.022 b	1.40 cd	9.63 ab	4.73 bcd	47.50 bc	16 b
E7	4.12 a	0.35 bc	4.50 e	21.49 bc	1.018 ab	1.49 g	11.64 b	4.71 bc	72.50 d	16 b
E8	4.41 e	0.36 cdef	4.50 e	15.85 ab	1.018 ab	1.33 a	7.65 ab	4.62 bc	107.50 g	16 b
E9	4.26 bcd	0.39 fg	5.50 g	42.70 e	1.022 b	1.46 efg	8.60 a	4.24 b	42.50 b	16 b
E10	4.42 e	0.32 ab	5.75 h	13.34 a	1.018 ab	1.38 cd	7.95 a	3.24 a	72.50 d	16 b
E11	4.22 abc	0.37 ef	4.25 d	121.22 h	1.017 ab	1.34 ab	9.13 ab	4.81 bcd	57.50 c	15 a
E12	4.17 ab	0.40 fgh	3.50 a	26.02 cd	1.014 a	1.44 defg	17.31 c	4.67 bc	60.00 c	16 b
E13	4.39 de	0.34 cd	3.75 b	45.75 e	1.015 ab	1.39 cd	28.30 d	4.12 b	92.50 ef	16 b
Control	4.26 bcd	0.29 a	4.00 c	56.84 f	1.018 ab	1.57 h	16.50 c	5.07 cde	52.50 bc	16 b

* Different letters indicate statistical differences between different formulations (ANOVA, Bonferroni post hoc test, p ≤ 0.05). Abbreviations: NTU, Nephelometric Turbidity Unit; EBC, European Brewery Convention; %E, foam capacity; IBU, International Bitterness Unit.

presents the results corresponding to the physicochemical and technological characteristics of the craft beer, including pH, soluble solids, titratable acidity, turbidity, specific gravity, viscosity, instrumental color, alcohol content, foam capacity, and bitterness. Also in this table, it is observed that different letters (a–g) indicate significant differences between the physicochemical and technological characteristics of the different formulations.

In terms of pH, the values range between 4.12 and 4.42, with significant differences observed between samples. Formulations with higher CM content and lower AJ content (E10 and E8) show higher pH values. It is worth noting that all samples remain close to the recommended pH value of around 4.0. This value is crucial for maintaining beers free from pathogenic microorganisms and minimizing contamination risk [36,37]. These pH levels are comparable to those found in craft beers made with acerola and pineapple (pH 4.24) and quince (pH 4.33) [38,39].

Regarding titratable acidity, a significant increase was observed in formulations with higher AJ concentrations (E12 and E6). These results remained within the acceptable limits established by NOM-199-SCFI-2007 [40], which stipulates a maximum of ≤10,000 mg of lactic acid/L for fermented products. The increase in acidity is attributed to the formation of acids during fermentation, expressed as lactic acid [41].

The soluble solids in the evaluated beers ranged from 3.50 to 5.75 °Brix, with the formulations containing a higher concentration of CM (E10) showing the highest values. However, these results were lower than those reported in craft beers made with rice flakes and soursop pulp, which had soluble solids concentrations ranging from 5.8 to 7.0 °Brix [38]. The decrease in soluble solids levels is attributed to yeast consumption of sugars during fermentation, leading to a progressive reduction of these compounds as the production process advances [42]. The proportion and quality of ingredients used in artisanal production also significantly influence the final soluble solids content [43].

According to the results, the turbidity values ranged from 15.85 to 121.22 NTU (Table 3) and the effect of MC and AJ concentration on beer turbidity is shown in Figure 3. According to the European Brewery Convention (EBC), beers have different degrees of turbidity, where a beer is considered to be turbid when it exceeds 15 NTU (Nephelometric Turbidity Units). Below this value, the beer is considered clear or bright and, above 30 NTU, it is considered cloudy. Therefore, our beer is considered cloudy because we have values higher than 15 and 30 NTU. The response surface indicates that turbidity increases as the concentration of MC and AJ (E5 and E11) decreases; this can be attributed to the absence of a filtration process in beers produced on a laboratory scale. In addition, all the storage and forced aging methods employed result in an increase in turbidity [44].

The purpose of using clarifiers is to improve the clarity of the beer, since they provide the sedimentation of the molecules that cause turbidity, and are usually added at the end of fermentation or before bottling or kegging [45]. On the other hand, Guimaraes [46], tells us that, in the maturation stage of the beer, it is cooled to a temperature between −1 and 4 °C for a period of 3 days; here, the yeasts and other components that cause turbidity slowly sediment, which clarifies the beer. That is why the maturation time and temperature of an Ale-type beer is very important, since it is desirable to obtain a clear beer with low turbidity, because turbidity is generally unacceptable to the consumer and, in addition, a concern for the brewer.

The specific gravity varied between 1.014 and 1.022 g/mL, with samples containing higher concentrations of CM (E10 and E9) showing higher specific gravity, exceeding the value reported for soursop craft beer (1.012 g/cm³) [39]. Measuring beer density is a key indicator for monitoring the alcoholic fermentation process. As yeast consumes sugars and produces alcohol, density values tend to decrease, since sugars are denser than alcohol [47]. The specific gravity values obtained were within the acceptable range (1.001–1.040) established by the Beer Judge Certification Program [48].

The viscosity of the beer ranged from 1.33 to 1.49 mPa·s. A higher starch content in barley can increase viscosity, prolonging filtration times [49]. On the other hand, the breakdown of simple carbohydrates during the process contributes to a decrease in viscosity [50].

The color results (Figure 3) obtained range from 7.65 to 28.26 EBC. According to decree n° 6.871 of 2009 [51], beers with values below 20 EBC are classified as pale, while those with values above 20 EBC are considered dark. Significant differences were observed between formulations, with the AJ content (E13) notably influencing the color, which is largely determined by the presence of polyphenols and Maillard reaction products. Although these Maillard reactions typically accelerate during wort boiling, they can also occur at ambient temperature [52].

The alcohol content of the beer samples ranged from 3.24% to 6.07%, with formulations containing higher proportions of PM (E5) showing the highest alcohol levels. It has been reported that alcohol content in industrial beers ranges between 4.5% and 5.3% (v/v) while, in craft beers, the variation is broader, from 4.4% to 9.1% (v/v) [53], suggesting that alcohol content tends to be higher in craft beers.

As the proportion of CM increased and AJ content decreased, an increase in the foam capacity of the beer was observed, with values ranging from 0.13 to 1.08%E. Foam formation capacity depends on both the protein content and the specific characteristics of those proteins [54]. Furthermore, foam stability in beer is directly related to the amount of protein derived from malt and hops [55]. The foam capacity of beers brewed with wheat malt ranges from 0.1% to 3% [56].

The bitterness of the beer ranged from 15 to 16 IBU, being higher in samples with lower PM and CM content. Generally, International Bitterness Units (IBU) in beer range from 5 to 120, and the use of higher amounts of hops, especially more bitter varieties, tends to significantly increase IBU levels in craft beers [57].

3.3. Sensory Analysis

Table 4. Average scores * of acceptance and purchase intention of the beers.

Exp.	Aroma	Color	Flavor	Appearance	Purchase Intention
E1	7.07 abc	7.23 a	6.13 abc	6.89 abc	3.30 ab
E2	7.13 bc	6.86 a	6.85 c	7.25 abc	3.87 c
E3	6.22 a	6.95 a	5.73 ab	6.76 abc	3.19 a
E4	7.13 bc	6.86 a	6.85 c	7.25 abc	3.87 c
E5	6.63 ab	7.20 a	5.84 abc	7.02 abc	3.49 abc
E6	6.66 abc	7.19 a	5.29 a	6.58 a	3.21 a
E7	7.13 bc	6.86 a	6.85 c	7.25 abc	3.87 c
E8	7.52 c	7.47 a	6.90 c	7.41 abc	3.83 c
E9	6.81 abc	7.24 a	6.55 bc	7.41 abc	3.63 bc
E10	7.38 bc	7.57 a	6.66 bc	7.53 c	3.70 bc
E11	6.78 abc	7.34 a	6.22 abc	7.33 abc	3.49 abc
E12	6.60 ab	6.96 a	6.07 abc	7.12 abc	3.36 ab
E13	6.99 abc	7.10 a	5.87 abc	6.66 ab	3.49 abc
Control	7.24 bc	7.03 a	5.94 abc	7.41 bc	3.78 c

* Different letters indicate statistical differences between different experiments (ANOVA, Bonferroni post hoc test, p ≤ 0.05).

presents the mean values obtained from the acceptability test. However, none of the organoleptic parameters fit a mathematical model (R² < 0.70). Therefore, the Bonferroni post hoc test was applied to identify significant differences between the formulations. Also in this table, it is observed that different letters (a–c) indicate significant statistical differences in the sensory characteristics of the formulations.

Overall results show a high level of acceptance in terms of aroma, color, taste, and appearance, with most formulations scoring an average of 6–7 and 3–4 in purchase intent, indicating moderate preference. Formulation E10, composed of 70% X₁, 25% X₂, and 5% X₃, was the most accepted, with average scores of 7.38 for aroma, 7.57 for color, 6.66 for taste, 7.53 for appearance, and 3.70 for purchase intent. These values fall within the “like” range on the sensory scale. In contrast, formulations with higher concentrations of AJ, such as E13 and E6, received the lowest scores, with means of 6.65 and 6.43, respectively.

A clear preference for CM was observed, as it appears to contribute sensory characteristics that enhance the overall perception of craft beer. In contrast, higher concentrations of AJ may negatively affect certain sensory attributes, introducing flavors less appreciated by consumers. These findings align with those of dos Santos et al. [58], who identified a significant correlation between the chemical composition of beer and its acceptability index, highlighting a preference for lighter-colored beers with more body and lower acidity. Although craft beers are often distinguished by their complex and novel flavors [59], these more intense profiles may not appeal to all consumers, especially those more accustomed to traditional beers.

In this study, the Check-All-That-Apply (CATA) method was used to evaluate the sensory profile of craft beer. This method allows the collection of useful sensory profiles for food products based on data provided by participants [60]. During the CATA analysis, consumers were asked to check all terms that described the craft beer samples in terms of appearance, color, aroma, taste, and texture.Through Cochran’s Q test, it was identified that 21 of the 37 evaluated attributes clearly distinguished the beer samples. These attributes were related to appearance, color, flavor, aroma, and mouthfeel. Beers with higher concentrations of cañihua malt (E10) were associated with terms such as foamy, sweet, and bubbly. On the other hand, beers with higher concentrations of AJ (E9 and E13) were linked to fruity flavor and floral aroma attributes (Figure 4).

Penalty analysis revealed that attributes related to color and mouthfeel, such as golden, shiny, effervescent, viscous, and toasted grain aroma, were considered desirable. However, attributes like opaque color, bitter taste, and watery mouthfeel reduced acceptability scores by up to −0.4 points (Figure 5). Based on these results, beer E8 aligned most closely with the attributes preferred by consumers.

3.4. Regression Models and Response Surface Analysis

In this study, regression models and response surface methodology were used to evaluate the impact of independent variables on the physicochemical and sensory properties of craft beer. The predictive models obtained described the relationship between the proportions of ingredients and the beer’s quality parameters, such as pH, acidity, turbidity, specific gravity, viscosity, color, alcohol content, and foam capacity (Table 3).

Analysis of variance (ANOVA) was performed to determine the significance of the models and fit the experimental data using linear and quadratic regression models. For each parameter analyzed, the coefficients of determination (R²) were calculated to assess the predictive power of the models, accepting those with R² values greater than 0.7 for predictive purposes (

Table 5. Predictive regression models that describe the relationships between the physicochemical and technological parameters of beers and their composition.

Dependent Variables	Model *	p-Value	R2 (Predicted)	R2 (Adjusted)
pH	Y = 8.76X1 + 4.42X2 + 5.49X3 − 6.25X1X2 − 10.42X1X3 − 2.63X2X3 − 90.45X12X2X3 + 20.92X1X22X3 + 62.63X1X2X32	0.0043	97.89	93.66
Titratable acidity	Y = −1.03X1 + 0.32X2 − 0.12X3 + 2.14X1X2 + 3.19X1X3 + 1.18X2X3 + 27.37X12X2X3 − 15.65X1X22X3 − 13.08X1X2X32	0.0221	95.06	85.19
Turbidity	Y = 1115.28X1 + 13.3X2 + 44.1X3 − 1562.35X1X2 − 2024.98X1X3 + 54X2X3 − 8114.38X12X2X3 + 2577.74X1X22X3 + 8105.85X1X2X32	0.0018	98.64	95.91
Absolute density	Y = 0.01X1 + 0.01X2 + 0.02X3 − 0.00002X1X2 − 0.0001X1X2 − 0.00001X2X3	0.0134	82.74	70.42
Color	Y = 76.52X1 + 7.95X2 − 3.56X3 − 99.39X1X2 − 154.65X1X3 + 27.67X2X3 − 548.31X12X2X3 − 674.91X1X22X3 + 2694.21X1X2X32	0.0021	98.51	95.54

* Independent variables of the study: Pilsen malt (X1), cañihua malt (X2) and aguaymanto juice (X3).

).

Response surface graphs were generated to identify the optimal combination of ingredients that maximizes the desired properties of craft beer. It was observed that PM and CM showed significant coefficients in the regression models, notably influencing parameters such as pH, turbidity, and specific gravity. In particular, CM had a positive impact on specific gravity, contributing to a higher consistency in the beer’s texture. On the other hand, AJ had a significant effect on improving the beer’s color and acidity.

Figure 6 presents contour plots of predicted values for pH, acidity, turbidity, specific gravity, and color in craft beers brewed with different formulations of PM, CM, and AJ. The contour plots help identify optimal ingredient combinations that result in desirable characteristics for each parameter. The pH graph shows how different proportions of CM and AJ influence the beer’s acidity, where a proper balance can maintain a pH that supports the stability and flavor of the final product. The acidity graph indicates that a higher proportion of AJ increases total acidity, which may be desirable for certain flavor preferences or beer styles requiring a more acidic profile. In the turbidity graph, it is observed that turbidity increases significantly with the addition of higher amounts of cañihua base malt, suggesting that it contributes to a hazier appearance, possibly due to the presence of proteins and suspended particles. The specific gravity graph shows how the proportions of CM and AJ affect the beer’s density, which influences mouthfeel and the perception of body in the product. The color graph, measured in EBC, illustrates how AJ can contribute to a lighter or darker color depending on the amount used, which is essential for achieving the desired color in craft beer.

3.5. Optimal Formulation for Physicochemical and Technological Characteristics

To determine the optimal formulation of BM, CM, and AJ for craft beer, a multiple response optimization was conducted with the aim of reducing pH, acidity, color, and turbidity while keeping specific gravity within range. The significant variables identified earlier (p < 0.05 and R² > 0.70) were included in the analysis. This procedure allowed the identification of the ideal combination of ingredient proportions, giving equal importance to all responses.

Table 6. Selected parameters for desirability optimization method, optimal craft beer formulations at optimal desirability value (D).

Response Variables	Criterion	Lower Limit	Upper Limit	Importance	Expected Values
PM	In range	70	80	3	74.52
CM	In range	5	25	3	15.55
AJ	In range	5	15	3	8.93
pH	Minimize	4.12	4.42	3	4.14
Titratable acidity	Minimize	0.32	0.42	3	0.342
Turbidity	Minimize	13.34	121.22	3	12.63
Absolute density	In range	1.001	1.040	3	1.017
Color	Minimize	7.65	28.30	3	4.879
Optimum desirability value (D)				0.915

Abbreviations: Pilsen base malt, PM; cañihua malt, CM; aguaymanto juice, AJ.

shows the evaluated criteria and the optimal formulations identified, along with their predicted values according to the mathematical model. The optimal desirability value achieved was 0.915, and the optimal formulation obtained was 74.52% BM, 15.55% CM, and 8.93% AJ. These formulations demonstrate an appropriate balance in terms of physicochemical and technological parameters, indicating that these optimized combinations can significantly improve performance in craft beer production. Based on the sensory evaluation, it is estimated that the optimal formulation will have high acceptability among consumers, similar to formulations E2 and E4. The moderate use of AJ ensures that the fruity profile does not dominate, while a higher CM content enhances the perception of body and texture in the craft beer.

4. Conclusions

Within the studied ranges, it can be concluded that the optimal formulation of Ale-type craft beer with the addition of cañihua malt (CM) and aguaymanto juice (AJ) was a mixture of 74.52% Pilsen base malt (PM), 15.55% CM, and 8.93% AJ. The optimal desirability value was 0.915, achieving a suitable balance from both physicochemical and technological perspectives. From a sensory standpoint, the craft beer with the highest overall acceptability score was formulation E10 (composed of 70% PM, 25% CM, and 5% AJ). Based on CATA results, the craft beer produced with formulation E8 (composed of 76% PM, 19% CM, and 5% AJ) closely matched the attributes preferred by consumers. These findings suggest that CM and AJ are valuable raw materials that can contribute to physicochemical, technological, and sensory improvement in the production of craft beers. Furthermore, it is recommended that future studies optimize the production of craft beer made with Andean grains, focusing on the content of bioactive compounds and antioxidant capacity, since these properties could improve its functional and nutritional value.