Effects of Inactive Yeast Biostimulants on Mechanical and Color Attributes of Wine Grape Cultivars

Abstract

Background: Biostimulants naturally improve plant growth, stress tolerance, and nutrient use efficiency and activate defenses by increasing protective metabolites (phenols, anthocyanins) in grapes. In viticulture, especially when using inactive yeasts, they modulate genetic expression and improve the skin resistance, color, and aroma profile of wine grapes in line with sustainable practices. Methods: Two wine grape cultivars, Merlot and Cabernet Sauvignon, were sprayed with the inactive yeast Saccharomyces cerevisiae in a single treatment in pre-veraison or in a double treatment in pre-veraison and veraison. Berry weight, must, total polyphenols, anthocyanins, and mechanical and colorimetric properties were measured on fresh grapes. Results: Two-way ANOVA revealed that titratable acidity (TA), pH, and total polyphenol content (TPC) were not affected, while mean berry weight and anthocyanin content varied by cultivar, treatment, and interaction; total soluble solids (TSS) differed only by cultivar. Inactive yeasts reduced weight in the single-treatment thesis but stabilized it in the double-treatment one; anthocyanins decreased in Cabernet Sauvignon but increased in Merlot. Mechanical and colorimetric analyses showed cultivar-dependent responses, with significant improvements in elasticity, skin thickness, and hue of berries, especially in Merlot when the treatment was applied twice. Conclusions: Inactive yeasts (IYs) showed an effect on the weight of the berries, the anthocyanins, the mechanics, and the color; Merlot significantly improved skin thickness, elasticity, and hue; and Cabernet remained less reactive to treatments.

1. Introduction

The growing attention to sustainable viticulture has encouraged the development of innovative strategies aimed at improving production quality while minimizing environmental impact. The quality and yield of grapevine production are increasingly being compromised by adverse climatic conditions associated with ongoing climate change [1]. Moreover, future climate scenarios are already affecting the timing and duration of grapevine phenological stages [2]. In this context, among the emerging technologies, biostimulants of natural origin are gaining a central role due to their potential to enhance plant development, fruit quality, and resistance to abiotic and environmental stress [3,4,5] by improving water and nutrient use efficiency [6].

Biostimulants activate plant defense pathways and promote the synthesis of protective metabolites, improving parameters such as phenolic maturation and polyphenol, anthocyanin, and stilbene content in berries, similar to what has been observed in microbe-associated molecular patterns (MAMPs) [7,8,9]. In particular, it is known that the use of biostimulants in viticulture induces variations in the expression of genes involved in the anthocyanin synthesis pathways, including the Myb family genes and all other genes active in both the early and late phases of biosynthesis, as well as the genes responsible for the transport of anthocyanins into the vacuole [10]. This leads to a direct increase in the anthocyanin content in the berries and the associated colorimetric parameters. These biostimulants align with sustainable and organic practices, helping reduce reliance on chemical fertilizers and pesticides, which negatively impact soil biodiversity [11]. Rich in organic nitrogen, amino acids, and bioactive compounds, biostimulants improve nutrient availability and uptake [12].

Among the various classes of biostimulants currently used in agriculture, inactive yeasts (IYs) represent a promising tool for the viticulture sector [13,14,15], with peculiarities that distinguish them from other biostimulants, such as, for example, seaweed extract. The latter act mainly through hormonal activity thanks to the presence of auxins, cytokinins, polysaccharides, and micronutrients that promote root development, vegetative growth, and resistance to biotic and abiotic stress. On the other hand, IYs stimulate the immune system and secondary metabolism and prove to be particularly effective in improving the qualitative, technological, and nutraceutical properties of fruits [16,17]. Derived from fermentation by-products of Saccharomyces cerevisiae cells, inactive yeasts (IYs) are subjected to specific treatments that inactivate their vitality while preserving functional components such as mannoproteins, β-1,3- and β-1,6-glucans, chitin, lipids, and sterols [7]. Inactive yeasts act as elicitors and retain all the properties of the biostimulants described above. They promote a balanced ripening of the grapes and stimulate the defense mechanisms of the vines and the functioning of the synthesis pathways of secondary metabolites, thus improving the aromatic profile and the volatile components of the grapes and, consequently, of the wines [9,18,19,20]. Additionally, IYs enhance grape quality by improving the mechanical properties of grape berry skins [21]. In particular, they promote skin hardening—as evidenced by increased skin break force—and skin thickening, which can improve the berries’ resistance to physical damage and pathogen attacks. The observed increase in berry skin thickness, likely a defense mechanism triggered by IYs, may also influence the release of anthocyanins during the maceration process. It is important to note that within each grape cultivar, mechanical characteristics—especially skin hardness—are vintage-dependent and correlate with seasonal climatic indices [22]. Furthermore, the effects of IYs on the mechanical properties of grape berries are closely linked to their colorimetric properties. Grape color serves as an indicator of anthocyanin content, which is influenced by the texture of the berries and the properties of the cell walls that affect the extraction of anthocyanins during winemaking [23]. Consequently, structural indices derived from mechanical analysis can provide valuable insights for the optimization of maceration processes and, prior to that, for the protection of the grapes from pathogenic attacks when they are dried for the production of passito wines.

In literature, the most common time for the application of biostimulant treatments in vineyards corresponds to the beginning of the phenological phase of veraison, namely the vegetative period during which the accumulation of anthocyanins in skins begins and reaches its maximum around harvest [24]. Usually, two treatments with biostimulants are performed around veraison: the first one at the beginning and the second one after approximately two weeks [7,25]. Pastore et al. [10] applied the second treatment at an advanced veraison stage, while in other research, the first treatment occurred during the bud burst stage and the next one immediately after flowering [15]. In other studies, treatments were administered at three phenological stages: at bud burst, at full flowering, and between the beginning of fruit set and the pea size [26]. This variation is due both to the commercial recommendations of the products and to the type of vineyard—whether intended for the production of table grapes or wine grapes—and whether the biostimulant is used alone or in combination with other biostimulants of different origins.

In this study, we investigated the efficacy of IYs on different wine grape cultivars through mechanical tests, color analysis, and secondary metabolite content of berries. Despite the growing number of studies dealing with biostimulants in viticulture, very few have addressed the combined influence of IYs on the mechanical integrity and colorimetric properties of grape berries in the context of cultivar-specific responses. This study contributes new insights by integrating multivariate analyses of berry mechanical properties and pigment-related colorimetric shifts after single and double IY application, crucial for the optimization of precision viticulture practices.

2. Materials and Methods

2.1. Field Trial

In 2024, samples of Merlot and Cabernet Sauvignon grape cultivars were harvested from two contiguous vineyards in the experimental farm of the CREA Viticulture and Enology Research Center in the Arezzo–Tuscany (Italy) (43°28′30.80″ N–11°49′33.02″ E, 250 m a.s.l.) (Figure 1) when the technological ripeness was optimal for the production of their wines. The vines were grafted onto Kober 5BB in 2013 with planting distances of 3.00 m × 0.90 m, trained in spurred cordon with 20 buds per plant in a sandy loam soil from a non-irrigated, terraced river origin. The vineyard followed the principles of integrated agriculture, with natural grassing, weeding at the beginning of the season, and subsequent elimination of basal suckers. The vineyard had not been fertilized in the last two years.

For the experimental trial, a biostimulant formulation based on Saccharomyces cerevisiae inactive yeast (IY) was applied via foliar spraying. The product used (XPlant 1—Enologica Vason, Verona, Italy) was obtained through an innovative lysis process conducted at low temperature, under vacuum, and without the use of exogenous enzymes. This formulation is fully water-soluble and specifically designed for foliar application.

Two theses were defined: one subjected to a single treatment in the pre-veraison stage at DOY 186–190 (T1) BBCH-79, while the second included a first treatment in pre-veraison, followed by a second at veraison, at DOY 205–212 (T2) BBCH-83. The main meteorological variables were recorded via an electronic hut from the network of Sites of Regional Interest (SIR) of the Tuscany Region, located in the vineyard and providing daily data for the duration of the test. A summary of monthly climatic conditions from the whole year is presented in

Table 1. Climatic characterization of vegetative season in the experimental vineyard.

	Tmin (°C)	Tmax (°C)	Tmean (°C)	Rain Sum (mm)	PET Sum (mm)
January	2.2	10.9	6.1	52.4	27.3
February	4.0	14.1	8.5	87.5	38.3
March	5.7	15.6	10.4	126.4	58.8
April	7.1	19.1	12.8	65.4	97.1
May	10.6	21.7	16.3	89.9	116.3
June	14.5	27.4	21.0	37.7	147.5
July	18.7	33.3	26.4	2.4	186.2
August	19.7	33.9	26.8	22.3	157.4
September	14.2	24.4	19.0	160.0	89.4
October	12.0	20.5	15.8	257.6	49.3
November	4.5	14.8	9.1	22.8	35.3
December	0.3	10.3	4.4	98.9	24.0
Annual mean/sum	9.5	20.5	14.8	1023.3	1026.8
Vegetative season mean/sum	14.1	26.6	20.4	377.7	793.9

, while Figure 2 reports daily values from the vegetative period of April–September. The two theses, T1 (one treatment) and T2 (two treatments), were compared with an untreated control thesis (test). Each thesis consisted of three replicates with six vines in each replicate in a completely randomized block design. Each treatment was applied at a dose of 0.8 kg/ha, distributing a water volume of about 600–1000 L/ha depending on plant canopy development and spraying all above-ground parts while avoiding dripping.

2.2. Measurements

At harvest, twelve bunches were randomly harvested for each experimental replication and thesis. A total of about 200 berries were pooled from each replicate. From this pool, approximately 30 intact berries without skin defects and with complete stems were then selected for each of the three replicates and for each thesis. Medium berry weight (MBW), total soluble solids (TSS), titratable acidity (TA), and pH were determined according to OIV official methods.

The mechanical and colorimetric properties (CIELab coordinates) of the fresh berries were measured. The remaining parts of the berries were frozen to allow the analysis of total polyphenol content (TPC) and anthocyanin content (ANT).

The mechanical properties of the berries were tested using the Texture Analyser mod.BT1-FR0.5 TND14 from Zwick/Roell (Zwick GmbH & Co.Gk—Ulm, Germany), equipped with a compression load cell with a nominal force of 500 N. The data were recorded with the software TESTXPERT II V. 3.31 in a Windows environment at 500 Hz. The selection of some operating conditions of the device for performing the various tests was based on the criteria reported by Letaief et al. [27].

Color was measured on 30 whole berries using a CM-5 chromameter (Konica Minolta, Chiyoda, Japan) according to the CIELab color system, which is based on a three-dimensional space defined by: the L* axis (lightness), which ranges from 0 (black) to 100 (white); the a* axis, representing the red-green spectrum with positive values indicating red and negative values indicating green; and the b* axis, corresponding to the yellow-blue spectrum, where positive values indicate yellow and negative values indicate blue. Furthermore, the reflectance between the wavelengths 360 nm and 740 nm was recorded for each berry.

The mechanical properties of 30 berries per cultivar were measured by a double compression test with a flat cylindrical steel probe with a diameter of 20 mm, up to a deformation of 20% of the original volume of the berry. The waiting time between the first and second compression was 2 s, while the lowering speed of the crossbar was set to 1 mm/s. The following parameters were measured.

-

Hardness (N): maximum force recorded during the first compression cycle;

-

Cohesiveness (adim.): measurement of the strength of the internal bonds that allow the berry to “reform” its structure;

-

Springiness (mm): height regained by the berry between the end of the first cycle and the beginning of the second;

-

Gumminess (N): energy required to dissolve the berry so that it resembles a semi-solid, deglutible food;

-

Chewiness (mJ): energy required to chew the berry until it is ready for deglutition;

-

Resilience (adim.): ability of the berry to return to its original position after being squeezed.

The penetration test of the grape skin was assessed by placing berries in an equatorial position on a perforated metal platform and lowering a probe with a diameter of approximately 2 mm at a speed of 1 mm/s until it penetrated the berry skin and reached a depth of 2 mm beyond the surface [27]. The puncture resistance of the skin was recorded in the form of a diagram and processed using MATLAB software (version R2019b). Details can be found in Supplementary File S1: Algorithm Summary. The calculated parameters include the following.

-

Maximum breaking force (FB—force break): expressed in Newtons (N), representing the force required to break the skin;

-

Energy required for perforation (EB—energy break): calculated as the area under the time-deformation curve, between the start of the test (zero force or trigger point, i.e., the point at which the probe touches the grape) and the complete breaking point of the skin (yield point).

Skin thickness (Th) was measured by accurately removing berry skin from the lateral surface using a scalpel and blotting skin fragments of 25 cm² with absorbent paper. The prepared skin sample was placed on the metal plate of the device and stretched well, avoiding wrinkles. A cylinder with a flat base and a diameter of 2 mm was used to measure skin thickness by means of a descending rate set at 0.2 mm/s. An instrumental release threshold of 0.05 N was set in order to let the probe fully adhere to the skin sample before data acquisition and reduce or eliminate the so-called tail effect due to the displacement of the contact point [28]. After the position of the probe was calibrated, the skin thickness was calculated by graphical processing using MATLAB software (version R2019b) as the distance between the probe’s contact point with the grape skin (trigger) and the base of the platform during a compression test.

The total phenolic content (TPC) and anthocyanin content (ANT) of the skins from 120 frozen berries were measured. The berries were peeled and the skins were weighted, dried at 37 °C, and powdered. Approximately 0.5 g of the sample powder was incubated overnight in 10 mL of 70% ethanol and 1% hydrochloric acid. Subsequently, the sample extracts were filtered through a 0.45 µm syringe cellulose filter and stored at −20 °C until further analysis. TPC was determined by employing the Folin–Ciocalteu colorimetric method, as delineated by Waterhouse [29]. The reaction mixture was prepared with 1 mL of water, 0.02 mL of sample extract, 0.2 mL of the Folin–Ciocalteu reagent, and 0.8 mL of a 10% sodium carbonate solution. Absorbance was measured at 760 nm following a 90 min incubation period at room temperature with a spectrophotometer Agilent 8453 (Agilent Technologies, Santa Clara, CA, USA). The results were expressed as milligrams of gallic acid equivalent per gram of dry weight, based on a gallic acid calibration curve (50 to 500 mg/L with R² = 0.998).

ANT was determined using a protocol based on the differential pH method proposed by Lee et al. [30]. Appropriate dilutions of grape extract were mixed with buffers of 0.025 M potassium chloride (pH 1) or 0.4 M sodium acetate (pH 4.5). Absorbance was measured at 520 and 700 nm using the Agilent 8453 spectrophotometric system (Agilent Technologies, Santa Clara, CA, USA). The results were expressed in milligrams of cyanidin-3-glucoside equivalents per gram of dried grape skin (mg Cy/g skin).

2.3. Statistical Analysis

The normal distribution of data and their variance were assessed using the Shapiro–Wilk and Levene tests. Outliers were evidenced by Grubb test. Data were subjected to two-way ANOVA in order to verify any significant interactions between the two factors, Cultivar × Treatments. Subsequently, a multiple pairwise comparison using the Tukey post hoc test was applied to separate the means of each variable in both cultivars at a significance level of α = 0.05. Finally, mechanical and color parameters were separately subjected to principal component analysis to verify the effect of IY treatments on the variables in the two cultivars. Statistical analysis was conducted using Statgraphics Centurion XV version 15 (Statgraphics Technologies, Inc., The Plains, VA, USA).

3. Results

The climatic vintage in the vineyard was in line with the trend from recent years, with persistent heat waves during the months of July and August and an average maximum temperature exceeding 33 °C (Table 1). In particular, between June and August, the maximum temperature exceeded 30 °C in 65 days, reaching its peak on 12 August at 38.1 °C at the end of a heat wave lasting several days. The rainfall of the winter months ensured good soil water availability at the beginning of spring and may have affected the initial phase of berry development. During the month of July, in particular, close to veraison, i.e., from 8 July to 25 July, the time window in which the IY treatments were applied, the absence of rain led to conditions of water stress, while in the following month of August, meteorological events recorded a total of 22.3 mm of rainfall. When we took into consideration the entire growing year, the water balance between rainfall and potential evapotranspiration was found to be in equilibrium; however, when the calculation field was restricted to the growing season, an irrigation deficit of more than 300 mm was observed.

Table 2. Two-way ANOVA and means separation by Tukey test for must and qualitative traits of two wine grape cultivars treated with one (T1) or two (T2) inactive yeast applications vs. Test (Control) and their interaction.

	Factors			Cabernet Sauvignon			Merlot
	Cultivar	Treatment	Interaction	Test	T1	T2	Test	T1	T2
MBW (g)	***	***	*	0.78 ab	0.73 b	0.85 a	1.17 a	0.95 b	1.04 ab
TSS (°Brix)	***	n.s.	n.s.	25.0	24.5	25.2	26.9	26.9	25.9
TA (g/L)	n.s.	n.s.	n.s.	4.35	4.62	4.57	3.93	3.99	4.13
pH	n.s.	n.s.	n.s.	3.60	3.52	3.55	3.65	3.62	3.62
TPC (mg GAE/g)	n.s.	n.s.	n.s.	45.88	44.14	42.42	37.87 b	40.92 a	39.84 ab
ANT (mg Cy/g skin)	*	*	*	16.35 ab	18.02 a	14.79 b	16.36 b	17.30 ab	18.35 a

MBW = medium berry weight; TSS = total soluble solids; TA = titratable acidity; TPC = total polyphenol content; ANT = anthocyanins; * = p ≤ 0.05; *** = p ≤ 0.001; n.s. = not significant. Different letters indicate significant difference at p < 0.05 by Tukey test.

shows the two-way ANOVA results on the must and qualitative traits of Merlot and Cabernet S. berries treated with inactive yeasts (IYs) in pre-veraison (T1) and veraison (T2). Titratable acidity (TA), pH, and total polyphenolic content (TPC) were not statistically influenced by the factor Cultivar, the factor Treatment, or the interaction between the two factors. On the contrary, MBW and ANT were influenced by both factors and their interaction, while total soluble solids (TSS) showed variations only in relation to the factor Cultivar. Given the evident effect of the Cultivar on the parameters considered, we further investigated how the individual variables within each cultivar behaved as a function of treatment level. The table contains pairwise comparisons of the mean values for the analyzed variables within each cultivar, which were determined using the Tukey test. A detailed analysis of the cultivars revealed that MBW decreased significantly under T1 for both Merlot and Cabernet Sauvignon, with reductions of −18.8% (Merlot: from 1.17 g to 0.95 g) and −6.4% (Cabernet: from 0.78 g to 0.73 g), respectively. However, under T2, MBW in Merlot partially recovered (1.04 g), while in Cabernet Sauvignon, it increased significantly above control (0.85 g), suggesting a compensatory effect of the second IY application in both cultivars. Contrasting results were instead obtained for ANT, which decreased significantly for Cabernet Sauvignon in T2, while increasing significantly for Merlot, especially in T2. On the other hand, TPCs in Cabernet Sauvignon were almost unchanged despite the theses, while in Merlot, their increase was particularly evident in thesis T1 (37.87 vs. 40.92 mg GAE/g). T2 also showed an increase, although it was not significant compared to Test (Control).

Table 3. Two-way ANOVA and means separation by Tukey test for mechanical parameters of two wine grape cultivars treated with one (T1) or two (T2) inactive yeast applications vs. Test (Control) and their interaction.

	Factors			Cabernet Sauvignon			Merlot
	Cultivar	Treatment	Interaction	Test	T1	T2	Test	T1	T2
H (N)	***	*	n.s.	3.88 a	3.48 b	3.85 ab	4.59	3.97	3.93
Ch (mJ)	*	*	n.s.	2.69	2.21	2.95	3.93 a	2.62 b	3.22 ab
Co	n.s.	n.s.	n.s.	0.47	0.43	0.5	0.48	0.45	0.49
E (mm)	n.s.	***	***	1.47 b	1.27 c	1.66 a	1.65 a	1.36 b	1.34 b
G (N)	*	*	n.s.	1.83	1.59	1.89	2.31	1.86	2.05
R	n.s.	n.s.	n.s.	0.35	0.34	0.34	0.35	0.34	0.35
FB (N)	***	**	n.s.	0.74 b	0.83 ab	0.84 a	1.16	1.18	1.3
EB (mJ)	***	*	n.s.	0.62	0.73	0.73	1.22	1.24	1.41
Th (mm)	***	***	*	0.18	0.19	0.18	0.20 b	0.30 a	0.22 b

H = hardness; Ch = chewiness; Co: Cohesiveness; E = elasticity; G = gumminess; R = resilience; FB = force break; EB = energy break; Th = skin thickness; * = p ≤ 0.05; ** = p ≤ 0.01; *** = p ≤ 0.001; n.s. = not significant. Different letters indicate significant difference at p < 0.05 by Tukey test.

reports the two-way ANOVA and Tukey test results on mechanical properties. Hardness (H), chewiness (Ch), gumminess (G), force break (FB), energy break (EB), and skin thickness (Th) were cultivar dependent, while no statistically significant differences were observed for cohesiveness (Co), elasticity (E), or resilience (R). The effects of the IY treatments on the mechanical variables partly overlapped with those of the cultivars, although some discrepancies can be observed in E, in particular, which appeared to be highly influenced by the factor Treatment. Moreover, E and Th were influenced by a high interaction between the two factors, suggesting the efficacy of IYs on both cultivars in enhancing E and promoting skin thickness (Th). As previously reported, we examined how each cultivar behaved as a function of treatment level. In Cabernet Sauvignon, the application of IY affected H, E, and Th. H and E, in particular, appeared to be compromised in the theses that underwent a single treatment, with H reduced from 3.88 N to 3.48 N and E decreased from 1.47 mm to 1.27 mm. Meanwhile, in the thesis with double IY application, H remained unchanged compared to the Test thesis (3.85 N), while E showed a significant increase to 1.66 mm, which was even higher than in the untreated thesis. Of particular interest is FB, which increased after IY treatment over 16%, especially in the theses with double application (T2).

In Merlot, Ch and E seemed to be negatively affected by the treatment. Ch decreased by −33% in T1 and −18% in T2, while E was reduced by about 18% in both T1 and T2. The treatments were not shown to have any effect on the other parameters. Interestingly, Th improved in the T1 thesis (from 0.20 mm to 0.30 mm), while returning to values similar to those of the untreated thesis in T2 (0.22 mm).

Finally,

Table 4. Two-way ANOVA and means separation by Tukey test for CIELAB coordinates of two wine grape cultivars treated with one (T1) or two (T2) inactive yeast applications vs. Test (Control) and their interaction.

	Factors			Cabernet Sauvignon			Merlot
	Cultivar	Treatment	Interaction	Test	T1	T2	Test	T1	T2
L*	***	n.s.	n.s.	35.53	35.48	33.64	31.19	32.02	31.53
a*	***	n.s.	n.s.	−0.78	−0.77	−0.61	−0.07	0.04	0.02
b*	***	n.s.	n.s.	−4.54	−4.49	−4.15	−3.03	−3.29	−2.93
C*	n.s.	**	***	3.67 a	2.00 c	2.98 b	2.75 ab	3.19 a	2.35 b
h*	*	n.s.	**	266.35 b	271.71 a	268.69 ab	270.4	269.1	274.0

L* = lightness; a* = red/green scale; b* = yellow/blue scale; C* = chroma; h* = hue angle; * = p ≤ 0.05; ** = p ≤ 0.01; *** = p ≤ 0.001; n.s. = not significant. Different letters indicate significant difference at p < 0.05 by Tukey test.

reports the two-way ANOVA and Tukey test results on CIELab coordinates. These results showed statistically significant differences between the cultivars, with the exception of Chroma (C*), which, in turn, was influenced only by the treatment. Significant interactions were observed both for C* and Hue angle (h). From the analysis of the effects of the treatments on a single cultivar, there was no effect on the components lightness (L*), red/green scale (a*), or yellow/blue scale (b*), while C* decreased significantly, especially in the theses subjected to double application (T2). At the same time, h*, which remained unchanged between Merlot theses, seemed to be positively influenced by the treatments themselves to some extent.

With regard to PCA, the mechanical and colorimetric parameters were analyzed separately (Figure 3 and Figure 4). Figure 3 shows the distribution of the combined mechanical profiles, from which it is evident that the first two principal components account for 79.8% of the total variance. In particular, PC1 is predominantly characterized by H, G, and Ch, while FB and EB are positively correlated in almost the same way in both PC1 and PC2. Similarly, the variables R, Co, and E are positively correlated with PC1 but negatively correlated with PC2. Meanwhile, Th is the only variable that correlates strongly and positively with PC2. All these results illustrate the different behavior of Merlot and Cabernet Sauvignon cultivars in response to the different IY treatments.

On one hand, Merlot exhibited better mechanical properties per se compared to Cabernet Sauvignon, regardless of the IY treatments. Moreover, Merlot seems to benefit from the IY treatments, especially in terms of Th, with a more pronounced effect at T1 and a less intense effect at T2, which nevertheless seems to confer greater resistance to skin penetration. On the other hand, as the ANOVA results in Table 3 already show, Cabernet Sauvignon seems to benefit much less from the IY treatments. For this cultivar, a second treatment (T2) seems to be completely ineffective in improving mechanical parameters, as shown by the PCA, where T2 and Test (Control) almost overlap, although some discrete properties in E are preserved.

Referring to Figure 4, which shows the PCA of the CIELab coordinates together with the parameters C and h, the different responses of the two cultivars to the IY treatments are confirmed. In general, the two principal components almost completely explain the variability examined. PC1 is positively characterized by a and b and negatively (i.e., in the opposite way) by L, while C, on the other hand, seems to be more strongly and positively correlated with PC2.

Overall, Merlot shows a stronger correlation with the parameters a, b, and h. The hue (h) seems to benefit the most from T2, as the berries show a tendency towards a bluish hue, in contrast to T1 and the Test control, which look similar and show predominantly reddish hues. Conversely, with respect to C, T1 seems to confer greater color brilliance than T2. It is noteworthy that lightness (L) is generally more strongly associated with Cabernet Sauvignon, regardless of IY treatment. For Cabernet Sauvignon, T1 appears to favor an increase in hue (h), which is then lost at T2, where the profile is again similar to that of the Test control, as previously observed for mechanical properties. The positioning of Cabernet Sauvignon profiles in PCA space, opposite to the direction of the a and b indices, generally indicates that Cabernet Sauvignon berries tend to be lighter, with a color tilt towards blue/green, while Merlot shows a tendency towards yellow/red hues.

To further investigate the color differences between the two cultivars induced by the two IY treatments, an analysis of the individual colorimetric spectra in a wavelength range from 360 nm to 740 nm was performed based on the reflectance measured by the colorimeter.

Figure 5 shows the spectral behavior of the three profiles in Cabernet Sauvignon, where significant differences are observed from 480 nm up to 740 nm in a continuous manner, while in the range between 369 nm and 470 nm—covering the ultraviolet and violet spectra and part of the blue range—no significant differences are observed. These differences occurred mainly in the green, yellow, orange, and partially in the red region of the spectrum. Overall, Figure 5 shows almost identical spectral behavior between the Test control and T1, especially between 480 nm and 670 nm, with differences that are present between 680 nm and 740 nm but tend to weaken. As can also be observed in the PCA of the CIELAB parameters (Figure 4), there is a greater variability in color between the different treatments for Cabernet Sauvignon than for Merlot. In Merlot (Figure 6), the three profiles are very similar, at least up to 660 nm, with statistically significant differences only occurring between 670 nm and 740 nm.

4. Discussion

The results indicate that both the grape cultivar and the inactive yeast (IY) treatment significantly influenced a range of mechanical, colorimetric, and physicochemical parameters in Cabernet Sauvignon and Merlot. Overall, while inherent cultivar differences accounted for many traits, the application of IY treatments produced measurable changes that differed between the two cultivars. Moreover, multivariate analyses provided an integrated perspective by revealing how clusters of variables jointly explained variability, thereby highlighting contrasting patterns between cultivars and among treatment levels.

In relation to must traits, total soluble solids (TSS), pH, and titratable acidity (TA) were not affected by IY treatments in either of the cultivars considered, consistent with other studies [7,8,19,22] but contrasting with Villangò et al. [25], who reported that foliar spraying significantly affected grape titratable acidity and pH in Syrah; however, these effects are often influenced by vintage.

The ANOVA clearly showed that grape cultivar exerted a strong influence on many measured parameters. For instance, significant differences in mechanical variables—such as H, Ch, G, FB, EB, and Th—underscored the intrinsic differences between Cabernet Sauvignon and Merlot.

With respect to berry texture, differences between the cultivars are also apparent. Merlot maintained a consistent hardness (H), whereas Cabernet Sauvignon exhibited a drop in hardness at T1, followed by partial recovery at T2, although overall, H showed a declining trend post-treatment. This suggests that the parameters of hardness and skin thickness (Th) do not necessarily correlate directly [27].

Furthermore, no significant treatment effects were observed on most other mechanical properties—with the notable exception of elasticity (E), which improved in Cabernet Sauvignon at T2, while Merlot experienced a decrease in E after T1. Similar trends were noted for firmness parameters FB and EB, which varied only modestly overall. However, when comparing the two cultivars directly, significant differences in FB and EB emerged—likely due to differences in TSS, resulting in higher FB and EB in Merlot [27]. FB and EB have also been proposed as useful indicators of anthocyanin extractability in wine grapes [24].

When examining grape skin characteristics, the IY treatment did not affect Th in Cabernet Sauvignon, which remained unchanged, whereas in Merlot, it increased significantly at T1 and then returned to levels similar to control at T2. This suggests that a second treatment may be unnecessary. The reduced efficacy—or even decline—in parameters such as skin thickness under T2 in Merlot suggests that excessive or closely repeated IY applications may trigger desensitization phenomena or feedback inhibition within secondary metabolite synthesis pathways. This interpretation aligns with studies demonstrating dosage-dependent effects of biostimulants, where beneficial responses occur only within a critical concentration range, while applications outside this window—either too low or too high—can result in detrimental physiological outcomes and impaired metabolic activity [31].

In Merlot, the second treatment (T2) was associated with a decrease in both anthocyanins and polyphenols that paralleled the reduction in Th. In Cabernet Sauvignon, despite unaltered Th between treatments, we observed a non-significant reduction in TPC and a transient increase in anthocyanins at T1 that diminished again by T2. Other studies [32] on different wine grapes indicated that not all the cultivars respond in the same way to biostimulant treatments, and while Barbera showed no treatment effect during maceration, Nebbiolo exhibited higher anthocyanin content. The different behavior of cultivars could be explained by the vine’s interaction with pathogens and its capacity to recognize the yeasts in the foliar spray—thereby activating specific defense mechanisms [33] and stimulating secondary metabolism for enhanced phenolic synthesis [34]. Moreover, contrasting responses between cultivars may be attributed to a dose-dependent effect. Some researchers [7] noted that the increase in total anthocyanins was not uniform across different concentrations of inactive yeast, implying a cultivar dependence [22]. In our study, the treatment improved total polyphenols and anthocyanins in Merlot predominantly at T1, while in Cabernet Sauvignon, the polyphenol content remained stable and anthocyanins only showed a transient increase at T1.

The improvement in mechanical and phenolic responses observed in Merlot can be attributed to its specific genotypic traits, particularly the regulation of anthocyanin biosynthesis by transcription factors such as VvMYBA1, which are known to be modulated by biostimulant treatments [10,35]. Although direct comparisons with Cabernet Sauvignon are currently lacking, existing evidence suggests that VvMYBA1 is dynamically regulated during grape development and responds to environmental cues, such as light [36]. It is, therefore, plausible that Merlot and Cabernet Sauvignon differ in their expression profiles, contributing to their distinct sensitivities to biostimulants and variations in anthocyanin accumulation [33,34].

In addition to gene regulation, the structural features of berry skin play a crucial role. Merlot exhibited significant increases in skin thickness and elasticity after IY application, suggesting structural adaptations that may enhance anthocyanin accumulation and retention. This aligns with previous findings showing that mechanical properties, particularly skin break force (Fsk), are closely associated with anthocyanin extractability [23]. Thicker and more resilient skins, as observed in Merlot, likely favor phenolic retention during berry maturation and a more efficient release during maceration.

In the mechanical PCA, Merlot samples clustered with higher values of force break (FB) and skin thickness (Th), which are likely associated with increased anthocyanin content [24]. This pattern suggests a potential synergistic relationship between skin mechanical resistance and pigment accumulation. Conversely, Cabernet Sauvignon showed less separation, indicating limited treatment responsiveness, which may be due to intrinsic differences in skin morphology or reduced sensitivity to IY stimuli. The biplot also suggests a possible collinearity between variables such as elasticity (E) and anthocyanin extractability (FB, EB), reinforcing the importance of mechanical traits in color-related outcomes [22,27].

Beyond mechanical traits, the composition and microarchitecture of the berry skin cell wall further modulate phenolic extractability. Studies on Tempranillo and Cabernet Sauvignon have demonstrated that anthocyanin release is influenced by the content of cell wall polysaccharides, including cellulose and pectins [37,38,39]. Moreover, Neves et al. [40] confirmed that not only the concentration but also the extractability of anthocyanins differs among varieties such as Merlot, Cabernet Sauvignon, and Tannat, underscoring the interplay between cell wall architecture and genetic background.

Taken together, these findings suggest that biostimulant treatments such as IYs may influence both the activation of anthocyanin biosynthetic pathways and modifications to berry skin structure, enhancing phenolic extractability in a cultivar-dependent manner. Consequently, optimizing biostimulant strategies requires a tailored approach that considers the unique genetic and anatomical traits of each grape cultivar to fully exploit their oenological potential.

In addition to genotypic and anatomical factors, environmental conditions during the treatment period may also play a crucial role in modulating the efficacy of IY applications. Regarding the influence of seasonal climatic conditions on the effectiveness of IY treatments, it is difficult to draw definitive conclusions, as the experiment was conducted within a single growing season, albeit with two different cultivars under the same environmental conditions. However, the exceptionally hot year and the heat wave that occurred just before the treatment period may have influenced the IY foliar uptake. For example, Salem-Fnayou et al. [41] reported that heat stress can induce anatomical changes in grapevine leaves, particularly an increase in cell wall thickness, which could reduce foliar permeability. Such effects may have been particularly relevant in Cabernet Sauvignon, which showed a lower responsiveness to IY treatments in this study. However, as no microscopic analysis of the leaf tissue is available, this remains speculative.

Under extreme heat conditions, vines prioritize physiological processes that are essential for survival, such as water conservation and the control of reactive oxygen species (ROS), over growth and secondary metabolism. These priorities can override or reduce the plant’s responsiveness to elicitors such as IYs, especially as the mechanisms of action of many biostimulants under abiotic stress remain poorly understood [42]. In general, heat stress may limit the efficacy of biostimulants, including inactivated yeast, due to altered plant physiology and the redistribution of resources to mitigate stress.

Regarding the optimal timing of the foliar IY application, the nature and composition of the biostimulants used in viticulture and reported in the literature does not always refer to Saccharomyces cerevisiae. In many cases, the composition of the biostimulants used is not known, although the treatment period and the number of treatments appear to be crucial aspects.

Jindo et al. [6] report different types of treatments with biostimulants in viticulture, of which the details of both the products used and the bioactive substances are often unknown. The only paper reporting the foliar spray of Saccharomyces cerevisiae is the one by Işçı et al. [43], who operated on Vitis champini in a greenhouse, yielding increased rooting. However, although several works report interventions ranging from a single application to multiple applications of biostimulants during the vegetative cycle, all the reported research shares a common factor: the veraison phase, which appears to be the most suitable for applying treatments, particularly under cooler or less optimal vintage conditions [25]. Other research [44] has tested different applications of Ascophyllum nodosum beginning at the end of dormancy and continuing through blooming, fruit set, and veraison, yielding higher microelement intake in table grapes. In other cases [45], foliar application of Ascophyllum was performed at veraison, improving fruit color in Sangiovese. The effect of biostimulants on berry color appears once again to be related to the cultivar, and the multivariate analysis in this research further supports this finding. In Merlot, the clustering along color coordinates (a, b, and hue, h) indicates higher color saturation and a pronounced hue shift, particularly after the second treatment (T2), where a bluish tint appears. In contrast, for Cabernet Sauvignon, the PCA shows that the T2 profile largely overlaps with the control group, suggesting that additional or alternative treatments might be needed to achieve improvements similar to those observed in Merlot.

The foliar spray also influenced pigment accumulation differently in the two cultivars. In general, increasing treatment intensity tended to reduce color saturation in both cultivars, which may be associated with modifications in ripening or alterations in anthocyanin structure [25]. Colorimetric analysis reinforces that treatment effects are cultivar dependent. Specifically, the PCA of CIELAB coordinates indicates that lightness (L) is more strongly associated with Cabernet Sauvignon, while color intensity (C) and hue (h) shift markedly following IY treatments.

Furthermore, in Cabernet Sauvignon, supplementation with two types of IY did not result in significant changes in the colorimetric parameters L*, a*, or b* [46,47]. The significant differences observed in hue (h) in Cabernet Sauvignon—but not in Merlot—might be due to different ripening states or cultivar responses attributable to differential gene activation, especially since berry color variation is closely linked to genetic variation at the VvmybA1 gene [35].

Spectral reflectance data collected across the 360–740 nm range further illustrate the effects of IY treatments. In Cabernet Sauvignon, significant differences in the spectral range between 480 and 740 nm among treatments suggest that IY application alters the pigment profile—particularly within the green and red wavelengths. Normally, the minimum of reflection in black wine grape berries, mainly related to chlorophyll absorption, is observed around 680 nm, with values less than 5% of radiation until ~700 nm [48]. In our case, the two cultivars, Cabernet Sauvignon and Merlot, showed lower reflectance, regardless of treatments, at 650 nm (around 7%) and 620 nm (around 6%), respectively. In Cabernet Sauvignon, the similarity between the control and T1 in the central wavelengths implies that the most substantial changes occur at the spectral extremes, likely reflecting ripening-related pigment modifications. By contrast, in Merlot, the spectral behavior remains largely homogeneous up to 660 nm, with significant differences only emerging beyond this range. This relative stability at lower wavelengths may be due to a more stable skin structure or a pigment composition that is less affected by the treatment. A more detailed examination of the light spectra reveals further differences in pigment accumulation between 400 and 700 nm following treatment, as also reported by Gutierrez-Gamboa [20]. In our study, Cabernet Sauvignon appears to benefit more from the T2 treatment, in terms of pigment accumulation across the entire spectral range, compared to T1 and the control. In Merlot, however, differences in reflectance caused by T2 are evident only for wavelengths above 670 nm—corresponding to the red part of the reflectance spectrum, indicating that the treatment has a greater effect on chlorophyll than on anthocyanins. Additionally, analysis of the a and b components of the CIELab coordinates shows that Merlot exhibits a higher red index (a), whereas Cabernet Sauvignon, with more negative b values, expresses deeper shades in the violet-blue region (approximately 380–490 nm).

In this broader context, and in light of the increasingly frequent and intense abiotic stress events associated with climate change, the use of biostimulants is expected to become not only a promising strategy but also a fundamental paradigm for reducing reliance on synthetic chemical inputs in viticulture. The transition towards more sustainable, resilient, and environmentally friendly production systems necessitates agronomic practices that enhance the natural interactions between plants and their environment. However, to ensure optimal efficacy, it is essential to deepen our understanding of the physiological and biochemical mechanisms they trigger. A comprehensive understanding of the metabolic responses elicited by biostimulants is essential for optimizing the timing, method, and frequency of application, thereby maximizing benefits in terms of yield, fruit quality, and input reduction. In this regard, the integration of multidisciplinary tools—ranging from functional genomics to advanced phenotyping—offers promising opportunities to establish reliable, targeted application protocols aligned with the overarching goal of promoting climate-resilient and sustainable viticultural practices.

5. Conclusions

This study provides clear evidence that inactive yeast (IY)-based biostimulants influence the mechanical and colorimetric traits of wine grape berries in a cultivar-dependent manner. Specifically, Merlot berries showed marked improvements in skin thickness, elasticity, and hue, particularly after a single application at pre-veraison. In contrast, Cabernet Sauvignon exhibited only minor changes, mainly in elasticity and skin thickness.

These findings emphasize the need to tailor biostimulant applications to the physiological characteristics of each cultivar. In Merlot, the enhanced mechanical resistance and colorimetric properties (notably the hue shift towards blue) suggest potential benefits for improving grape quality traits relevant to winemaking and to resistance against mechanical damage and pathogens.

The results demonstrate that biostimulant effects cannot be generalized across cultivars and underline the importance of considering genetic background in precision viticulture strategies.

Future research should validate these findings across different grape cultivars and growing environments, assess the impact of repeated applications over multiple seasons, and clarify the physiological mechanisms involved, particularly those related to skin structure and anthocyanin metabolism. Additionally, studies on the interaction between IY treatments and environmental stress factors such as drought and heat will help to refine their role within sustainable viticulture practices.