Effects of Crop Load Management on Berry and Wine Composition of Marselan Grapes

Abstract

The aim of this study was to investigate the effects of the crop load on the berry and wine composition of Marselan grapes. Thus, the appropriate crop load for Marselan wine grapes in Ningxia was determined based on the shoot density and the number of clusters per shoot. Marselan grapes from the Gezi Mountain vineyard, located at the eastern foot of Helan Mountain in the Qingtongxia region of Ningxia, were selected as the research material to conduct a combination experiment with four levels of shoot density and three levels of cluster density. The analysis of the berry and wine chemical composition was combined with a wine sensory evaluation to determine the optimal crop load levels. Crop load regulation significantly affected both the grape berry composition and the basic physicochemical properties of the resulting wine. Low crop loads improved metrics such as the berry weight and soluble solids content. A low shoot density facilitated the accumulation of organic acids, flavonols, and hydroxybenzoic acids in wine. Moderate crop loads were conducive to anthocyanin synthesis—the total individual anthocyanins content in the 10–20 shoots per meter of the canopy treatment group ranged from 116% to 490% of the control group—whereas excessive crop loads hindered its accumulation. Crop load management significantly influenced the aroma composition of wine by regulating the content of sugars, nitrogen sources, and organic acids in grape berries, thereby promoting the synthesis of esters and the accumulation of key aromatic compounds, such as terpenes. This process optimized pleasant flavors, including fruity and floral aromas. In contrast, wines from the high crop load and control treatments contained lower levels of these aroma compounds. Compounds such as ethyl caprylate and β-damascenone were identified as potential quality markers. Overall, the wine produced from vines with a crop load of 30 clusters (15 shoots per meter of canopy, 2 clusters per shoot) received the highest sensory scores. Appropriate crop load management is therefore critical to improving the chemical composition of Marselan wine.

1. Introduction

Achieving an optimal balance between vegetative and reproductive growth is essential for producing high-quality grape berries and meeting yield targets in viticultural practices. Among the various cultivation strategies, regulating the crop load is commonly employed to manage this balance effectively [1,2,3]. This approach primarily involves adjusting the number of shoots per vine and the number of clusters per shoot [4]. Cluster thinning is one of the principal methods used to adjust the crop load. This approach modifies the plant’s source–sink relationship and impacts the accumulation of both primary and secondary metabolites in the berries [5,6,7]. By increasing the leaf-area-to-fruit ratio [8], cluster thinning not only ensures the effective production and accumulation of photosynthates but also balances fruit consumption, thereby maintaining vine health and improving berry quality [9]. In addition, this practice enhances the appearance attributes of the grapes, including the berry shape, size, and color uniformity [10,11].

In winemaking, the concentrations of sugars, acids, phenolic compounds, and volatile constituents in wine grapes directly impact the color, aroma, and overall complexity of the resulting wine [12,13]. Previous studies have shown that different levels of crop load regulation can significantly alter the composition and diversity of the aromatic compounds in wine [14,15]. However, due to the high labor costs associated with manual thinning, the application of cluster thinning in commercial vineyards remains limited. Furthermore, the existing research on the crop load in wine grapes has primarily focused on its impact on berry and wine qualities, whereas comprehensive evaluations that integrate the optimal crop load with economic returns are still lacking and remain a subject of ongoing debate.

This experiment aimed to achieve differences in the crop load of Marselan wine grapes by regulating the shoot density and number of clusters per shoot. It investigated the basic physicochemical properties of Marselan under varying crop load conditions during the ripening period. Particular attention was paid to the effects of different crop loads on the organic acids, phenolic compounds, and volatile aroma compounds in the resulting wines. Sensory evaluation was also employed to assess the influence of the crop load on the overall wine chemical composition. The objective was to optimize wine grapes through crop load regulation, providing a technical reference for winemaking practices.

2. Materials and Methods

2.1. Materials and Reagents

2.1.1. Experimental Design

This experiment was conducted in 2024 at the wine grape vineyard of Ningxia Zhongzexiban Modern Agriculture Technology Co., Ltd. (38°45′97″ N, 106°18′11″ E). The tested variety was Marselan, planted in 2017 at a spacing of 0.8 m × 3.5 m. The training system used was an inclined pruned cordon, and the soil type was sandy loam.

“Shoot density” refers to the number of grape shoots per meter of trellis, while “cluster density” indicates the number of fruit clusters per shoot. The experimental design included four levels of shoot density (10, 15, 20, and 25 shoots per meter of canopy) and three levels of number of clusters per shoot (1.0, 1.5, and 2.0 clusters per shoot), totaling 12 treatment combinations. Each treatment was replicated three times, and each replicate consisted of four consecutive vines, each of which was 6 m long. Meanwhile, a control experiment (CK) was set up without thinning the branches and fruit clusters. Except for the cluster thinning, all the other vineyard management practices followed standard viticultural protocols.

2.1.2. Reagent

The organic acid standards (purity > 98.6%), anthocyanin standards (purity > 97%), polyphenol standards (purity > 98%), and volatile compound standards (purity > 98%) were all purchased from Sigma-Aldrich (St. Louis, MI, USA).

2.2. Instruments and Equipment

The instruments and equipment included a PL202-L-type 1% analytical balance (Mettler Toledo, Zurich, Switzerland), a Thermo TSQ ALTIS ultra-high-performance liquid chromatography–tandem triple quadrupole mass spectrometer (LC/MS/MS) (Thermo Fisher Scientific, Waltham, MA, USA), an LC-15C high-performance liquid chromatograph (equipped with a diode array detector and SIL-10AF auto-sampler), a Waters 2699 high-performance liquid chromatography (HPLC) system equipped with a 2487 UV detector (Waters, Milford, MA, USA), a GC2030-TQ8050 NX triple quadrupole gas chromatograph–mass spectrometer (GC-MS), an InertCap WAX polar chromatographic column (60 m × 0.25 mm, 0.25 μm) (Shimadzu Corporation, Kyoto, Japan), and a PAL-1 digital Abbe refractometer (Horiba Scientific, Shanghai, China Division).

2.3. Methods

2.3.1. Grape Sampling

Grape berries were sampled at different phenological stages starting from veraison on seven dates: 26 July, 7 August, 16 August, 26 August, 2 September, 12 September, and 19 September. Indicators, such as the 100-berry weight, total soluble solids (TSSs), and titratable acidity (TA), were monitored throughout berry maturation.

Yield estimation methods: The vineyard row spacing was 3.5 m × 0.8 m, with approximately 2880 grapevines per hectare. The experimental plot for the yield measurement was 6 m, and the actual yield of the experimental plot could be used to estimate the yield per hectare.

2.3.2. Winemaking

Upon full ripeness (19 September), 30 kg of grapes were randomly harvested from each treatment group. After destemming and crushing, the must was divided into three replicates and transferred into 20 L fermentation tanks. Sulfur dioxide (60 mg/L) and pectinase (25 mg/L) were added. After 24 h, 200 mg/L of fully activated commercial yeast (Yeast CECA) was inoculated, which was produced by Angel Yeast Co., Ltd. (Yichang, China). During alcoholic fermentation, cap punch-down was performed three times daily, while the temperature and specific gravity were monitored regularly. The fermentation temperature was 20~28 °C, with a fermentation period of 21 days, and after the separation of pomace, the wine was aged for 45 days before bottling. After fermentation, the pomace was separated, and after standing for clarification for approximately 45 days, bottling was performed.

2.3.3. Basic Physicochemical Analyses of Grapes

According to Mengyuan Wei et al. [16], 100 berries were randomly selected and weighed (g), which was repeated three times, and the average was calculated. The pH value was measured using a PHS-3E pH meter. The TSS content was determined using the PAL-1 digital refractometer. The TA and reducing sugar content [17] was measured by acid–base titration.

2.3.4. Determination of Organic Acids

The organic acids were analyzed following the method of Caihong Li et al. [18]. A Waters 2699 high-performance liquid chromatography (HPLC) system equipped with a UV detector and a CAPCELL PAK C18 column (4.6 mm × 250 mm, 5 μm) was used for the detection. Here, 1 g of wine sample (precise to 0.01 g) was weighed accurately and placed in a 50 mL polytetrafluoroethylene centrifuge tube, diluted to 20 mL, vortexed for 1 min to mix thoroughly, centrifuged, the supernatant was aspirated, and filtered through a 0.45 μm aqueous phase filter membrane (polyethersulfone) into a sample vial for analysis. The liquid chromatography conditions were as follows. Column temperature: 30 °C. The detection wavelength was set at 214 nm. Mobile phase: A: 0.1% formic acid in water; B: acetonitrile. Gradient elution program: 0–14 min, 95–70% A, 14–15 min, 70% A, 15–16 min, 70–90% A, 16–18 min, 90% A, 18–19 min, 90–95% A, 19–20 min, 95% A. Injection volume: 2.0 μL.

2.3.5. Determination of Individual Phenolics

The determination of the hydroxycinnamic acids in the wine was conducted according to T/NAIA 084-2021 Method for Determination of Hydroxycinnamic Acids in Wine by HPLC (equipped with a UV detector, Waters, MA, USA) [18]. For hydroxybenzoic acids, 2 g of the wine samples was accurately weighed and mixed with 2 mL of ethyl acetate, followed by vortexing for 1 min and centrifugation. The supernatant was collected into a 50 mL test tube and extracted three times. The combined extract was evaporated to dryness using a rotary evaporator and re-dissolved in 2 mL of methanol. After filtration through a 0.45 µm organic phase membrane, the sample was ready for analysis. The flavanols were quantified based on T/NAIA 085-2021 Method for Determination of Flavanols in Wine by HPLC [19], and the flavonols were measured according to T/NAIA 082-2021 Method for Determination of Flavonols in Wine by HPLC [20]. For the flavonols and flavanols, 5 mL of wine sample was accurately pipetted into a 50 mL test tube, and to this was added 20 mL of 75% methanol solution. This was mixed for 1 min by vortex, then sonicated for 30 min at room temperature. The solution was filtered through a membrane filter, and the filtrate was then ready for instrumental analysis. The liquid chromatography conditions: column temperature: 40 °C, flow rate: 0.9 mL/min; mobile phase: A: 2% acetic acid aqueous solution; B: 0.5% acetic acid—50% acetonitrile aqueous solution. The detection wavelengths for hydroxycinnamic acid, hydroxybenzoic acid, flavonols, and flavanols were 320 nm, 280 nm, 280 nm, and 320 nm, respectively.

2.3.6. Determination of Anthocyanins

First, 5 mL of wine was accurately pipetted, 10 mL of 2% formic acid–methanol solution was added and ultrasonically extracted for 10 min, followed by vortex oscillation for 20 min. Then, centrifugation was performed at 4000 r/min for 15 min, and the supernatant was collected into a 250 mL round-bottom flask. The extraction was repeated 3 times according to the above steps, then the supernatants were combined and rotary evaporated to dryness (120 r/min, 25 °C). Mobile phase A: (V formic acid:V water = 0.1:100), mobile phase B: (V formic acid:V methanol:V acetonitrile = 0.1:50:50). The solution was reconstituted with solvent (phase A:phase B = 9:1) to 10 mL, filtered through a 0.22 μm membrane, and then analyzed. Liquid chromatography conditions: Agilent Poroshell 120 EC-C18 column (150 mm × 4.6 mm × 2.7 μm); column temperature: 40 °C; flow rate: 0.3 mL/min; mobile phase A: 0.1% formic acid in water, mobile phase B: 0.1% formic acid–methanol:acetonitrile (1:1) solution; gradient elution program: 0–17 min, 10–50% B; 17–18 min, 50–10% B; 18–20 min, 10% B. Injection volume: 2.0 μL. Mass spectrometry conditions: ion source type: ESI ion source; scan mode: positive ion mode, selected reaction monitoring (SRM), spray voltage 4000 V; sheath gas 35 Arb; auxiliary gas 10 Arb; ion transfer tube temperature: 300 °C; nebulizer temperature: 350 °C. The qualitative analysis was performed using a self-built mass spectral library optimized based on standard compounds, and the quantification was conducted using the external standard method.

2.3.7. Determination of Volatile Compounds

Based on the approach proposed by Yue Wang et al. [21], 8 mL of the wine sample and 10 μL of internal standard solution (4-methyl-1-pentanol, 394.08 μg/L) were placed in a headspace vial containing 2 g of NaCl. The mixture was equilibrated at 45 °C with shaking at 250 r/min for 30 min. The volatiles were then extracted for 30 min and thermally desorbed at 250 °C for 3 min.

Gas chromatography conditions: the initial temperature was held at 40 °C for 5 min, then it was increased to 120 °C at a rate of 3 °C/min. Carrier gas: helium at a flow rate of 0.8 mL/min. The mass spectrometry conditions included an ion source temperature of 230 °C, an electron impact ionization energy of 70 eV, and a scanning mass range of m/z 33–450. The retention indices of all the compounds were calculated using a standard mixture of n-alkanes (C₆–C₃₂) and determined using the Kovats method [22]. The compounds were first qualitatively identified by comparing the retention times and mass spectra with NIST 14 library data. Quantitative analysis was subsequently performed using a combination of internal and external standard methods. The odor activity values (OAVs) were calculated, and compounds with an OAV > 1 were identified as key aroma contributors [23].

2.3.8. Sensory Assessment

The sensory evaluation was conducted according to GB/T 15038-2006 [24] General Analytical Methods for Wine and Fruit Wine. The sensory panel consisted of 12 trained assessors (six males and six females, aged 30–40). A 70-point scoring system was used, covering four aspects: appearance (clarity and color, 10 points), aroma (intensity, complexity and variation, 20 points), taste (balance, body, tannin texture and degree, 30 points), and overall impression (style and typicality, 10 points). Each sample was evaluated at least twice by each panelist to ensure consistency.

2.4. Data Analysis

All the data were subjected to one-way analysis of variance (ANOVA) and Tukey’s test using SPSS 25.0 software. Orthogonal partial least squares discriminant analysis (OPLS-DA) was conducted using SIMCA-P 14.1 software. Each sample was measured in triplicate. The cluster heatmap was generated using TBtools-II, with hierarchical clustering as the algorithm and the Euclidean distance as the distance metric. The results were expressed as the mean ± standard deviation. Prior to the multivariate statistical analysis, all the chemical variables were normalized (Z-score normalization).

3. Results

3.1. Effect of the Crop Load on the Yield of Marselan

Table 1. Yield statistics of Marselan grapes under different crop loads.

Group	Targeted Shoot Density (Shoots/Meter of Canopy)	Targeted Number of Clusters per Shoot (Number of Clusters/Shoot)	Targeted Total Number of Clusters	Yield (t/ha)
F11	10	1.0	10	7.512
F12	10	1.5	15	8.328
F13	10	2.0	20	7.176
F21	15	1.0	15	8.940
F22	15	1.5	22.5	10.488
F23	15	2.0	30	11.028
F31	20	1.0	20	10.980
F32	20	1.5	30	10.566
F33	20	2.0	40	11.928
F41	25	1.0	25	9.696
F42	25	1.5	37.5	11.028
F43	25	2.0	50	11.130
CK	28	2.0	56	11.098

presents the experimental design and yield statistics of Marselan under different crop load levels. Based on the actual yield from each experimental plot, the yield was estimated. This study found that under low loading conditions, the yield was relatively low, where the yield of treatment group F11 was only 67.7% of that of the control group. As the loading increased, the yield initially rose and then stabilized. Under the same crop load condition (e.g., F12 vs. F21, F13 vs. F31, F23 vs. F32), the grape yield in the treatment group with low shoot density was higher than that in the treatment group with high shoot density, indicating that the shoot treatment had a greater impact on the grape fruit yield.

Table 2. Variation in berry weight during grape ripening.

	26 July	7 August	16 August	26 August	2 September	12 September	19 September
CK	0.693 ± 0.005 hE	0.906 ± 0.003 deBC	1.015 ± 0.023 bA	0.937 ± 0.025 eB	0.867 ± 0.054 cdC	0.774 ± 0.054 gD	0.864 ± 0.021 abC
F11	1.029 ± 0.019 bB	1.116 ± 0.058 aAB	1.149 ± 0.019 aAB	1.055 ± 0.036 aB	1.201 ± 0.144 aA	1.036 ± 0.026 aB	1.051 ± 0.016 abB
F12	1.109 ± 0.019 aA	1.074 ± 0.067 abAB	1.057 ± 0.033 bAB	0.943 ± 0.029 deC	0.964 ± 0.022 bcC	0.974 ± 0.044 abcdC	1.021 ± 0.013 bB
F13	0.898 ± 0.016 eC	1.074 ± 0.038 abA	1.024 ± 0.025 bB	0.987 ± 0.035 bcdB	0.891 ± 0.012 bcdC	0.911 ± 0.063 deC	0.999 ± 0.018 cdeB
F21	0.924 ± 0.008 dB	1.026 ± 0.060 bcA	1.051 ± 0.052 bA	1.023 ± 0.013 abA	1.001 ± 0.023 bA	1.015 ± 0.032 abA	0.987 ± 0.026 deA
F22	0.949 ± 0.022 cBC	0.986 ± 0.043 cdB	0.971 ± 0.015 cB	0.998 ± 0.017 bcAB	0.977 ± 0.053 bcB	0.916 ± 0.003 cdeC	1.037 ± 0.018 bA
F23	0.888 ± 0.016 eC	0.950 ± 0.017 cdB	1.025 ± 0.028 bA	0.915 ± 0.056 eBC	0.899 ± 0.024 bcdC	0.956 ± 0.026 bcdB	0.955 ± 0.027 efB
F31	0.960 ± 0.003 cA	0.967 ± 0.048 dA	0.822 ± 0.025 eBC	0.803 ± 0.018 fC	0.841 ± 0.017 dBC	0.862 ± 0.033 eB	0.764 ± 0.028 hD
F32	0.963 ± 0.014 cCD	0.923 ± 0.052 deD	1.138 ± 0.034 aA	0.955 ± 0.019 cdeCD	1.005 ± 0.061 bC	0.988 ± 0.030 abcC	1.086 ± 0.056 aB
F33	0.807 ± 0.009 fD	0.972 ± 0.039 cdB	1.037 ± 0.023 bA	0.928 ± 0.017 eBC	0.894 ± 0.026 bcdC	1.043 ± 0.041 aA	0.933 ± 0.026 fBC
F41	0.805 ± 0.005 fE	0.868 ± 0.042 eD	1.111 ± 0.042 aA	1.021 ± 0.028 abB	0.997 ± 0.031 bBC	0.952 ± 0.053 bcdC	1.019 ± 0.041 bB
F42	0.802 ± 0.005 fC	0.991 ± 0.010 cdA	0.953 ± 0.025 cdAB	0.907 ± 0.060 eAB	0.912 ± 0.102 bcdAB	0.923 ± 0.020 cdeAB	0.881 ± 0.021 gB
F43	0.702 ± 0.005 hD	0.942 ± 0.030 cdC	0.921 ± 0.020 dC	0.933 ± 0.041 eC	0.978 ± 0.068 bcB	1.024 ± 0.032 abB	0.983 ± 0.044 aB

Note: The unit of the berry weight is g. The lowercase letters indicate significant differences (p < 0.05) among the treatments at the same maturity stage, and the uppercase letters represent significant differences (p < 0.05) among the maturity stages within the same treatment. The same notation applies to the following tables.

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Table 3. Variation in total soluble solids (TSSs) during grape ripening.

	26 July	7 August	16 August	26 August	2 September	12 September	19 September
CK	13.9 ± 1.1 cdE	17.9 ± 0.6 cD	19.8 ± 0.5 bcCD	21.5 ± 1.1 bBC	24.6 ± 2.1 abcA	23.4 ± 1.0 abcAB	23.7 ± 0.4 abA
F11	20.3 ± 0.4 aD	21.8 ± 1.4 aCD	24.1 ± 2.0 aABC	26.0 ± 1.4 aAB	27.0 ± 2.6 aA	23.7 ± 1.0 abcBC	24.6 ± 1.5 abABC
F12	17.8 ± 1.2 abD	20.9 ± 0.8 abC	22.3 ± 1.2 abBC	24.1 ± 1.1 abAB	26.2 ± 2.3 abA	20.5 ± 0.8 cC	24.3 ± 0.4 abAB
F13	18.7 ± 1.8 abD	21.2 ± 1.5 abCD	22.2 ± 0.6 abC	25.7 ± 2.3 aAB	26.1 ± 1.0 abA	23.0 ± 1.4 bcBC	23.4 ± 1.7 abBC
F21	18.6 ± 1.7 abC	19.3 ± 1.4 abcBC	22.1 ± 1.9 abAB	24.5 ± 1.5 abA	23.4 ± 2.0 abcA	22.7 ± 2.0 bcA	25.1 ± 1.3 abA
F22	16.9 ± 1.5 bcC	20.2 ± 0.4 abcB	21.5 ± 0.6 abB	25.5 ± 1.9 aA	22.1 ± 1.1 abcB	24.5 ± 1.3 abA	24.6 ± 1.1 abA
F23	18.0 ± 0.8 abD	19.9 ± 0.5 abcCD	22.9 ± 1.4 abB	20.1 ± 2.0 bCD	24.3 ± 0.0 abcB	26.7 ± 2.0 aA	22.2 ± 1.2 abBC
F31	15.2 ± 1.6 bcdC	18.8 ± 0.9 abcB	20.7 ± 1.3 abcAB	20.4 ± 1.7 bAB	23.6 ± 1.2 abcA	21.9 ± 1.5 bcAB	23.5 ± 2.9 abA
F32	15.8 ± 1.6 bcdC	17.2 ± 1.8 cC	21.7 ± 0.4 abAB	20.4 ± 1.2 bB	23.0 ± 1.8 abcA	22.8 ± 0.3 bcA	23.1 ± 0.4 abA
F33	15.4 ± 0.8 bcdD	19.4 ± 1.4 abcC	22.1 ± 1.3 abAB	20.4 ± 0.8 bBC	22.6 ± 1.8 abcAB	24.4 ± 2.1 abA	22.6 ± 1.4 abAB
F41	16.1 ± 1.3 bcdE	18.6 ± 1.8 abcD	20.9 ± 1.9 abcCD	21.4 ± 1.0 bBC	20.6 ± 1.1 cCD	23.8 ± 0.9 abcB	26.3 ± 1.6 aA
F42	15.8 ± 0.8 bcdC	18.3 ± 1.4 bcBC	19.6 ± 1.6 bcB	21.1 ± 1.4 bB	24.1 ± 2.3 abcA	24.7 ± 1.2 abA	24.6 ± 2.0 abA
F43	13.4 ± 1.0 dC	17.2 ± 0.8 cB	18.1 ± 0.5 cB	21.5 ± 2.4 bA	21.5 ± 1.0 bcA	22.5 ± 0.4 bcA	23.3 ± 0.6 abA

The lowercase letters indicate significant differences (p < 0.05) among the treatments at the same maturity stage, and the uppercase letters represent significant differences (p < 0.05) among the maturity stages within the same treatment.

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Table 4. Variation in reducing sugar content during grape ripening.

	26 July	7 August	16 August	26 August	2 September	12 September	19 September
CK	120.3 ± 4.8 cD	128.8 ± 11.2 bD	162.6 ± 12.9 bcdeC	175.1 ± 10.9 dC	238.9 ± 8.6 aA	180.8 ± 8.3 cdC	206.9 ± 13.6 abB
F11	169.2 ± 15.0 aD	159.8 ± 11.1 aD	183.1 ± 14.6 bcdCD	219.7 ± 17.4 abB	262.6 ± 14.6 aA	205.8 ± 10.3 bcBC	184.2 ± 16.9 bCD
F12	159.4 ± 12.5 abC	140.3 ± 10.9 abC	137.8 ± 15.9 eC	207.0 ± 10.9 bcB	270.3 ± 8.1 aA	250.8 ± 20.1 aA	205.3 ± 18.8 abB
F13	171.4 ± 15.7 aBC	141.5 ± 7.5 abC	178.1 ± 5.3 bcdB	242.3 ± 19.8 aA	260 ± 29.9 aA	167.5 ± 13.1 dBC	193.7 ± 10.2 bB
F21	163.6 ± 8.5 abD	136.2 ± 12.2 abE	185.0 ± 5.5 bcCD	207.4 ± 7.4 bcC	270.1 ± 5.4 aA	168.6 ± 10.5 dD	246.5 ± 28.4 aB
F22	153.1 ± 7.0 abD	149.5 ± 10.8 abD	161.2 ± 7.4 bcdeD	186.6 ± 11.4 cdC	250.8 ± 20.5 aA	171.7 ± 10.7 dCD	223.1 ± 9.7 abB
F23	161.9 ± 15.4 abCD	144.7 ± 5.8 abD	178.4 ± 13.9 bcdBC	147.1 ± 10.6 eD	273.4 ± 7.2 aA	190.6 ± 9.9 bcdB	184.1 ± 14.6 bBC
F31	141.3 ± 9.9 abcC	123.0 ± 11.7 bC	165.8 ± 7.6 bcdeB	177.8 ± 7.1 dB	230.9 ± 10.1 aA	185.1 ± 14.8 cdB	230.8 ± 20.1 abA
F32	153.3 ± 11.1 abE	136.5 ± 3.6 abF	177.3 ± 12.4 bcdCD	162.3 ± 1.6 deDE	267.9 ± 9.7 aA	192.7 ± 8.8 bcdC	209.7 ± 12.7 abB
F33	134.4 ± 4.6 bcEF	120.8 ± 8.7 bF	152.0 ± 4.0 deD	145.7 ± 11.0 eDE	243.4 ± 2.4 aA	178.5 ± 7.8 cdC	210.2 ± 11.7 abB
F41	159.9 ± 8.5 abD	139.4 ± 18.4 abD	192.6 ± 14.5 abC	242.3 ± 15.1 aAB	257.1 ± 18.0 aA	218.7 ± 20.9 bBC	203.8 ± 19.4 abC
F42	150.4 ± 13.8 abCD	138.1 ± 6.3 abD	210 ± 16.4 aB	174.2 ± 4.6 dC	251.4 ± 26.2 aA	159.7 ± 5.7 dCD	206.7 ± 2.1 abB
F43	118.2 ± 7.7 cE	100.2 ± 6.0 cE	154.0 ± 9.2 cdeD	190.7 ± 15.3 cdC	244.5 ± 13.6 aA	163.1 ± 13.0 dD	218.8 ± 18.7 abB

Note: The unit of the reducing sugar content is g/L. The lowercase letters indicate significant differences (p < 0.05) among the treatments at the same maturity stage, and the uppercase letters represent significant differences (p < 0.05) among the maturity stages within the same treatment.

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Table 5. Variation in titratable acidity (TA) during grape ripening.

	26 July	7 August	16 August	26 August	2 September	12 September	19 September
CK	0.91 ± 0.055 bcA	0.97 ± 0.085 abA	0.57 ± 0.049 cdefB	0.44 ± 0.049 cCD	0.53 ± 0.047 dBC	0.46 ± 0.024 abCD	0.39 ± 0.010 cD
F11	0.98 ± 0.090 abA	0.98 ± 0.094 aA	0.48 ± 0.024 efB	0.45 ± 0.039 cB	0.41 ± 0.0041 eB	0.39 ± 0.020 cdB	0.36 ± 0.012 cB
F12	0.93 ± 0.043 abcA	0.93 ± 0.025 abA	0.50 ± 0.015 defC	0.46 ± 0.051 cC	0.63 ± 0.060 abcdB	0.52 ± 0.010 aC	0.39 ± 0.014 cD
F13	0.95 ± 0.066 abcA	0.79 ± 0.067 cB	0.75 ± 0.061 aB	0.67 ± 0.052 aB	0.68 ± 0.031 abB	0.49 ± 0.047 abC	0.39 ± 0.018 cD
F21	0.97 ± 0.070 abA	0.84 ± 0.015 bcB	0.60 ± 0.024 bcdeC	0.45 ± 0.016 cD	0.58 ± 0.027 bcdC	0.38 ± 0.027 cdeD	0.45 ± 0.043 bcD
F22	0.90 ± 0.080 bcdA	0.98 ± 0.052 aA	0.68 ± 0.080 abcB	0.49 ± 0.045 cC	0.66 ± 0.060 abcB	0.44 ± 0.038 bcC	0.41 ± 0.015 cC
F23	0.80 ± 0.040 dA	0.87 ± 0.046 abcA	0.54 ± 0.056 defC	0.51 ± 0.020 bcC	0.69 ± 0.038 abB	0.43 ± 0.057 bcD	0.39 ± 0.028 cD
F31	0.84 ± 0.075 cdA	0.89 ± 0.096 abcA	0.53 ± 0.040 defBC	0.42 ± 0.037 cC	0.62 ± 0.022 abcdB	0.44 ± 0.019 bcC	0.50 ± 0.053 bC
F32	0.96 ± 0.029 abA	0.88 ± 0.063 abcB	0.69 ± 0.042 abC	0.44 ± 0.012 cD	0.72 ± 0.019 aC	0.48 ± 0.013 abD	0.49 ± 0.030 bD
F33	0.95 ± 0.041 abcA	0.95 ± 0.057 abA	0.47 ± 0.043 fC	0.51 ± 0.018 bcC	0.66 ± 0.046 abcB	0.36 ± 0.031 deD	0.37 ± 0.017 cD
F41	0.97 ± 0.044 abA	0.91 ± 0.072 abcA	0.61 ± 0.053 bcdB	0.59 ± 0.045 abB	0.64 ± 0.055 abcB	0.47 ± 0.054 abD	0.57 ± 0.060 aC
F42	0.93 ± 0.034 abcA	0.97 ± 0.078 abA	0.73 ± 0.032 aB	0.45 ± 0.025 cD	0.60 ± 0.043 bcdC	0.43 ± 0.0074 bcD	0.45 ± 0.043 bcD
F43	1.03 ± 0.047 aA	0.95 ± 0.083 abA	0.57 ± 0.057 cdefB	0.66 ± 0.078 aB	0.56 ± 0.037 cdB	0.33 ± 0.023 eC	0.40 ± 0.035 cC

Note: Titratable acid is expressed as a percentage content. The lowercase letters indicate significant differences (p < 0.05) among the treatments at the same maturity stage, and the uppercase letters represent significant differences (p < 0.05) among the maturity stages within the same treatment.

and

Table 6. Variation in pH during grape ripening.

	26 July	7 August	16 August	26 August	2 September	12 September	19 September
CK	2.95 ± 0.051 aB	2.97 ± 0.22 aB	3.40 ± 0.12 aA	3.43 ± 0.29 aA	3.66 ± 0.097 aA	3.72 ± 0.26 aA	3.66 ± 0.17 aA
F11	2.89 ± 0.15 aB	2.92 ± 0.18 aB	3.40 ± 0.32 aAB	3.51 ± 0.25 aA	3.66 ± 0.26 aA	3.81 ± 0.20 aA	3.73 ± 0.46 aA
F12	2.80 ± 0.24 aB	2.87 ± 0.20 aB	3.32 ± 0.20 aA	3.44 ± 0.091 aA	3.56 ± 0.28 aA	3.60 ± 0.24 aA	3.71 ± 0.23 aA
F13	2.89 ± 0.10 aC	2.98 ± 0.26 aBC	3.25 ± 0.26 aABC	3.37 ± 0.38 aAB	3.55 ± 0.16 aA	3.55 ± 0.18 aA	3.60 ± 0.095 aA
F21	2.91 ± 0.23 aB	2.99 ± 0.23 aB	3.39 ± 0.15 aA	3.50 ± 0.093 aA	3.62 ± 0.31 aA	3.70 ± 0.29 aA	3.77 ± 0.10 aA
F22	2.84 ± 0.18 aB	2.87 ± 0.13 aB	3.26 ± 0.29 aAB	3.43 ± 0.12 aA	3.55 ± 0.34 aA	3.65 ± 0.33 aA	3.64 ± 0.30 aA
F23	2.99 ± 0.11 aB	3.05 ± 0.11 aB	3.35 ± 0.10 aA	3.43 ± 0.17 aA	3.58 ± 0.036 aA	3.62 ± 0.33 aA	3.74 ± 0.099 aA
F31	2.92 ± 0.32 aB	3.00 ± 0.15 aB	3.43 ± 0.25 aAB	3.52 ± 0.070 aAB	3.63 ± 0.26 aA	3.72 ± 0.44 aA	3.79 ± 0.20 aA
F32	2.95 ± 0.21 aB	2.99 ± 0.29 aB	3.24 ± 0.43 aAB	3.41 ± 0.15 aAB	3.58 ± 0.072 aA	3.61 ± 0.25 aA	3.60 ± 0.32 aA
F33	2.84 ± 0.24 aB	2.89 ± 0.058 aB	3.36 ± 0.15 aA	3.43 ± 0.30 aA	3.56 ± 0.18 aA	3.70 ± 0.23 aA	3.56 ± 0.16 aA
F41	2.88 ± 0.14 aB	2.95 ± 0.26 aB	3.30 ± 0.25 aA	3.47 ± 0.24 aA	3.57 ± 0.20 aA	3.67 ± 0.29 aA	3.65 ± 0.36 aA
F42	2.85 ± 0.25 aB	2.91 ± 0.058 aB	3.27 ± 0.17 aAB	3.43 ± 0.27 aA	3.59 ± 0.44 aA	3.54 ± 0.094 aA	3.59 ± 0.095 aA
F43	2.83 ± 0.057 aB	2.91 ± 0.20 aB	3.30 ± 0.41 aA	3.43 ± 0.12 aA	3.52 ± 0.15 aA	3.56 ± 0.16 aA	3.63 ± 0.26 aA

The lowercase letters indicate significant differences (p < 0.05) among the treatments at the same maturity stage, and the uppercase letters represent significant differences (p < 0.05) among the maturity stages within the same treatment.

present the variation of the berry weight, TSSs, reducing sugars, TA, and pH during grape ripening.

Table 2 displays the evident differences in the berry weight among the treatments at the same ripening stage. In the early stages of ripening, the treatments with lower shoot density generally showed higher berry weight compared to those with higher shoot density. However, in the late stages, no consistent pattern was observed. Within each treatment, the berry weight also varied significantly over time, showing a trend of increasing, then decreasing, and increasing again as the berries matured.

According to Table 3, in the low crop load treatments, the TSSs exhibited an initial increase followed by a decrease, peaking on 2 September and slightly declining in mid-September. In contrast, the TSSs in the high crop load treatments demonstrated a continuous upward trend. Under the same shoot density, the TSSs decreased with an increasing number of clusters per shoot.

Table 4 indicates that the reducing sugar content showed a fluctuating increase followed by a decrease during ripening, reaching its peak on 2 September. At the same time point, the treatments with lower shoot density exhibited relatively higher reducing sugar content.

As seen in Table 5, the TA differed notably among the treatments, though no clear pattern was associated with the crop load. As the grapes matured, the TA in all the treatments generally declined, especially during the early stages of maturation. However, a slight rebound in the TA was observed on 2 September.

According to Table 6, the pH of the grape berries in all the treatments increased during ripening, with a pronounced rise in early to mid-August. However, no significant differences in the pH were found among the treatments at the same sampling time.

3.2. Effects of Crop Load on Wine Chemical Composition

3.2.1. Basic Physicochemical Parameters

As shown in

Table 7. Basic parameters of Marselan wine under different crop load treatments.

Group	Ethanol Content/%	pH	TA Concentration/(g/L)	Residual Sugar Content/(g/L)
F11	16.32 ± 1.02 a	3.58 ± 0.16 a	7.54 ± 0.54 a	2.30 ± 0.18 defg
F12	14.75 ± 1.21 ab	3.73 ± 0.33 a	6.60 ± 0.17 ab	2.60 ± 0.069 cde
F13	15.48 ± 1.35 ab	3.55 ± 0.25 a	7.38 ± 0.45 a	2.90 ± 0.34 bc
F21	15.51 ± 1.50 ab	3.66 ± 0.35 a	6.74 ± 0.47 ab	2.50 ± 0.17 cdef
F22	15.14 ± 1.32 ab	3.79 ± 0.29 a	6.3 ± 0.39 ab	2.40 ± 0.12 defg
F23	14.60 ± 0.15 ab	3.66 ± 0.30 a	6.72 ± 0.59 ab	2.71 ± 0.054 bcd
F31	14.93 ± 1.05 ab	3.70 ± 0.26 a	6.38 ± 0.17 ab	2.20 ± 0.079 efg
F32	15.01 ± 1.20 ab	3.70 ± 0.32 a	6.76 ± 0.41 ab	2.50 ± 0.11 cdef
F33	14.62 ± 0.64 ab	3.81 ± 0.29 a	6.05 ± 0.22 b	2.20 ± 0.20 efg
F41	13.49 ± 1.46 ab	3.68 ± 0.29 a	6.49 ± 0.49 ab	3.00 ± 0.26 b
F42	15.15 ± 1.06 ab	3.65 ± 0.32 a	6.36 ± 0.57 ab	2.10 ± 0.13 fg
F43	12.85 ± 0.68 b	3.78 ± 0.076 a	5.81 ± 0.59 b	2.00 ± 0.04 g
CK	13.17 ± 0.60 ab	3.54 ± 0.36 a	6.51 ± 0.46 ab	3.61 ± 0.22 a

The lowercase letters indicate significant differences (p < 0.05) among the treatments at the same maturity stage.

, the ethanol content of the wine samples ranged from 12.85% to 16.32%, showing significant differences only in the F11 and F43 treatment groups. The pH was between 3.54 and 3.81, with no significant differences among the treatments. The highest and lowest TA concentrations were observed in treatment groups F11 and F43, respectively. The highest and lowest residual sugar contents were found in the control and F43 groups, respectively.

3.2.2. Analysis of Organic Acids

Figure 1A depicts eight organic acids identified in the wine samples, with the total organic contents ranging from 9137.78 to 10,607.50 mg/L. No significant differences in the total organic acid contents were observed among the treatment groups (p > 0.05). Tartaric acid had the highest concentration, followed by malic acid and succinic acid.

Crop load regulation had diversified effects on various organic acids. At the same shoot density, the tartaric and malic acid contents initially elevated and then declined with the cluster density. Under the same number of clusters per shoot, as the density of shoots increased, the malic acid content first rose and then declined. The succinic acid content increased with an increasing number of clusters per shoot in the low shoot density treatments but lowered in the high shoot density treatments. Except for the 20 shoots/meter of canopy group, the other treatments showed a similar trend in the citric acid content. This pattern was also observed for the lactic acid content in all the treatments except the 25 shoots/meter of canopy group. Under the same crop load, the treatments with lower shoot density presented higher total organic acid contents, with the highest and lowest values being observed in groups F13 and F42, respectively.

As illustrated in Figure 1B, the lactic acid content in the control group (618.84 mg/L) was significantly higher than that in the treatment groups. The shikimic acid and malic acid contents were remarkably higher in the F32 group compared to the other treatments, while the F43 group had a relatively high citric acid content. Tartaric acid was most abundant in the F11 and F13 treatments, whereas succinic and pyruvic acids reached their highest concentrations in F13 and F22, respectively.

Hierarchical clustering analysis (heatmap) showed that all the wine samples could be grouped into three distinct clusters. One cluster contained the treatments with medium to high crop loads (F33, F43, F32, F31, and F41), while another included the medium to low crop load treatments (F12, F22, F23, F11, and F13).

3.2.3. Analysis of Phenolic Compounds

The 21 individual phenolics were categorized and analyzed according to four groups: hydroxybenzoic acids, hydroxycinnamic acids, flavanols, and flavonols, as shown in Figure 2.

Figure 2A illustrates that the polyphenols are primarily composed of flavanols, followed by hydroxybenzoic acids. Overall, the low crop load treatment groups had higher hydroxybenzoic acid contents, but the hydroxycinnamic acid content was relatively low, and it reached the highest value in the control group. The highest levels of flavanols and hydroxybenzoic acids appeared in the F22 treatment group. The flavonol content was generally higher in the low crop load groups than in the high crop load groups, with the highest value found in the F13 treatment group.

Figure 2B discloses that the clustering of the treatment groups does not follow a clear pattern based on the crop load. The F32, F11, and F13 treatment groups were clustered into one category due to the higher levels of flavonols and hydroxycinnamic acids in these wines. The F43, F31, F42, F12, and F23 treatment groups were clustered into another category, while the F21, F22, F33, F41 treatment groups and the control group formed a third category.

3.2.4. Anthocyanin Composition Analysis

A total of 11 anthocyanins were detected in the wine, with malvidin and malvidin-3-O-glucoside being the dominant compounds, accounting for 39.7–47.2% and 45.6–54.2% of the total anthocyanin content, respectively. Other significant anthocyanins included delphinidin-3-O-glucoside, peonidin-3-O-glucoside, peonidin, and petunidin-3-O-glucoside, with concentrations ranging from 2.8 to 25.7 mg/L. The contents of the other anthocyanins were close to or less than 1 mg/L.

Figure 3A shows the significant differences (p < 0.05) in the individual anthocyanin components and their total content among the treatment groups. Except for malvidin-3,5-O-diglucoside, the other anthocyanin components and the total content were lowest in the F43 treatment group and the control group. The total individual anthocyanins content in the 10–20 shoots per meter of canopy treatment group ranged from 116% to 490% of the control group, with cyanidin-3-O-glucoside in the F11 treatment group reaching 8.05 times that of the control group. The medium crop load treatment groups generally exhibited higher anthocyanin contents.

In Figure 3B, all the samples are divided into two major categories. The F42 and F43 treatment groups and the control group were clustered into one category, where the anthocyanin contents were lower. The other treatment groups were grouped into a second category, with the F13 and F32 treatment groups showing higher anthocyanin contents.

3.2.5. Analysis of Aroma Components

HS-SPME-GC×GC-TOFMS was used to analyze the volatile components in the wine under different crop load treatments, identifying 92 volatile aromatic compounds from 10 categories. Esters were the most abundant, with 32 compounds detected, followed by alcohols (24 compounds), acids (9 compounds), alkanes (9 compounds), terpenes (7 compounds), phenols (3 compounds), aldehydes (3 compounds), ketones (3 compounds), ethers (1 compound), and olefins (1 compound). Their mass concentrations ranged from 172,267.9 μg/L to 285,503.5 μg/L, with the maximum value in the F11 treatment group and the minimum in the control group.

Trans-3-hexen-1-ol was a unique aroma detected exclusively in the F11 treatment group, contributing significantly to the herbal aroma. 3-Furaldehyde was also specific to this group, playing a crucial role in the caramel aroma. 2-Propyl-1-heptanol was unique to the F12 group and positively contributed to the fruity aroma. Octanol was detected only in the F23 group, also playing a positive role in the formation of fruity notes. Nonanal, characteristic of the F32 group, imparted fatty and citrus-like aromas. Terpinolene was a unique aroma in the F41 group and contributed positively to the pine aroma. Phenylethanol, present only in the F43 group and the control group, played a positive role in the formation of floral notes. Gamma-nonanolactone was unique to the control group and contributed positively to the fruity aroma.

A total of 30 volatile compounds were detected in all the treatment groups, including 18 esters, eight alcohols, three acids, and one terpene. These 30 compounds could effectively distinguish between the control and high crop load treatment groups. Specifically, the F23 and F42 groups were clustered together, while the F43 and control groups formed another cluster, show as in Figure 4A.

A total of 11 key volatile compounds influencing aroma characteristics were identified based on an OAV > 1. These included six esters, three alcohols, one acid, and one terpene. β-damascenone and ethyl caprylate exhibited the highest OAVs (OAV > 1320.82) among all the treatment groups, indicating their critical role in shaping the aroma characteristics of Marselan wine. These findings are further validated in the clustering heatmap of the aroma compounds, show as in Figure 4B.

3.2.6. Sensory Evaluation

In order to evaluate the effect of the crop load on the chemical composition of Marselan wine, this study performed a sensory evaluation based on seven dimensions: balance, style and typicality, body, complexity and variation, tannin texture and degree, intensity, and clarity and color. As illustrated in Figure 5, the wine produced by the F23 treatment group scored the highest in multiple indicators, including style and typicality, tannin texture and degree, body, and intensity. However, the F41 treatment group performed the best in terms of clarity and color. Overall, the F23 treatment group achieved the optimal sensory quality, with a top score of 63.8 points, significantly higher than the treatment groups with shoot densities of 20 and 25 shoots per meter of trellis surface.

4. Discussion

The composition of grapes directly influences both the fermentation process of wine and the quality of the final product. The crop load affects not only fruit ripening and composition but also vine growth and resistance, thereby impacting the overall development of both the fruits and the plants [25,26]. Previous studies have shown that appropriate crop load control can enhance the composition of grapes and wine [27]. However, excessively low crop loads significantly reduce the grape yield [28]. This is consistent with the findings of the present study, where the yield of the lowest crop load treatment group (F11) was only 67.7% of the control group, possibly due to excessive vegetative growth leading to an imbalance in the nutrient distribution and, consequently, a reduced fruit yield. When the crop load is excessively high, the photosynthetic capacity of the leaves may be insufficient to support the full development of all fruits, resulting in limited actual yield growth. Additionally, a high crop load demands more water and fertilizer inputs. If resources are inadequate, the actual yield will be constrained. Therefore, in this experiment, even though the designed crop loads for wine grapes varied significantly, the differences in the actual yield were relatively small. Meanwhile, the Helan Mountain East Foothill region experienced substantial rainfall in 2024, leading to severe gray mold infections during the late fruit maturation stage. Diseased fruits rotted and dropped, causing a decline in the effective yield per vine.

Moreover, this study found that crop load regulation affected the physicochemical properties of grape berries and the basic physicochemical indices of wine. Lower crop loads were conducive to improving parameters such as the hundred-berry weight and soluble solids content, which favored early berry enlargement. For instance, in the early maturity period, the treatment groups with lower shoot density exhibited higher hundred-berry weights compared to the groups with higher shoot density. Appropriate shoot densities can establish a reasonable leaf area index, improving the light energy utilization of the leaves, enhancing the vine photosynthetic capacity, and increasing the accumulation of flavor compounds in the fruit [29]. As the berries matured, the hundred-berry weight initially increased, then decreased, and increased again, suggesting that excessively low shoot densities might promote overly vigorous vegetative growth, impeding the accumulation of photosynthates in the grapes.

Typically, the soluble solids content of wine grapes continues to rise during ripening [30]. However, in this study, the soluble solids content in the low crop load treatment groups initially increased and then declined, which may be attributed to rapid early sugar accumulation followed by decreased metabolic activity. The concentration of reducing sugars, a key indicator of fruit ripeness and winemaking potential, primarily originates from the translocation of photosynthates from the leaves to the berries. The treatment groups with low shoot density exhibited relatively higher reducing sugar content, which may be attributed to the shoot density regulation adjusting the canopy microclimate and the distribution of photosynthetic products, thereby influencing the accumulation of reducing sugars in the wine grapes [31]. The subsequent decline in the reducing sugar content in the late ripening stage may be caused by consecutive rainfall events reducing the photosynthetic efficiency.

The total acidity decreased sharply during the early stages of fruit maturation but rose slightly thereafter, which could also be attributed to prolonged rainfall reducing the sunlight exposure, delaying sugar accumulation and slowing acid degradation, thereby causing an increase in the total acidity content. However, the differences in the crop load had minimal effects on the pH of the grape berries.

A significant difference in the ethanol content among the treatments was observed only between the F11 and F43 groups, suggesting that crop load regulation concentrates sugars in grapes, leading to increased ethanol levels after fermentation [32,33]. Crop load control was found to enhance both the ethanol and total acid contents, thereby elevating the complexity of the wine body. Organic acids are critical for the chemical and microbial stability of wine, forming its structural backbone [34]. They are key determinants of wine acidity, pH, and mouthfeel. Tartaric and malic acids—primarily derived from grapes—primarily contribute to wine acidity and astringency. Other acids are generally produced by yeast metabolism during fermentation.

In this study, compared to cluster thinning, shoot management demonstrated a more pronounced impact on the wine composition improvement, a finding that contrasts with previous studies [35]. This may be due to differences in the grape variety and environmental conditions. Polyphenols, pivotal secondary metabolites produced during plant growth and development, constitute the structural basis of wine [36]. As a processed product, wine’s phenolic composition and concentration are influenced by multiple factors, including the grape variety, cultivation factors (climatic conditions, soil type, vineyard management), and winemaking techniques [37]. Previous studies have shown that the effects of cluster thinning on flavonoid accumulation in grapes and wine vary with the climatic conditions and experimental varieties. Generally, cluster thinning promotes flavonoid accumulation in berries, thus increasing the flavonoid content of wine [38,39]. However, the results of this study differ: the low crop load treatment groups exhibited higher levels of hydroxybenzoic acids, possibly due to the higher fruit maturity enhancing hydroxybenzoic acid synthesis. The hydroxycinnamic acid content was lower under low crop loads, with the highest value observed in the control group, potentially attributed to the vineyard management and vinification processes. Flavanols and flavonols impart bitterness and astringency to wine, constituting its structural framework [40]. Meanwhile, they also play a role in fortifying the wine color [41]. Existing research has reported inconsistent effects of cluster thinning on the flavanol and flavonol contents of wine. In this study, the shoot density treatments promoted the flavonol content but did not significantly alter the flavanol levels. Additionally, notable differences in the total polyphenol content were observed only between certain treatment groups, inconsistent with the findings of Guihua Zeng et al. [42]. These discrepancies may result from variations in the cultivation practices, climate, or grape cultivar. Anthocyanins, the principal contributors to wine color, have a profound impact on wine’s visual attributes and flavor [43]. It has been demonstrated that cluster thinning can affect both the content and the composition of anthocyanins in grapes and wine [44]. Malvidin, delphinidin, petunidin, cyanidin, and peonidin are the primary anthocyanins present in grapes and wines [45]. They usually exist in glycosylated forms but are also capable of acylation through esterification with various acids, forming acylated anthocyanins [46,47]. This study revealed that most individual anthocyanins and the total anthocyanin contents were lowest in the F43 treatment and control groups, with the intermediate crop load treatments exhibiting higher anthocyanin levels. These findings suggest that crop load control favors anthocyanin synthesis, aligning with previous research [48]. From the perspective of the plant source–sink relationship, an optimal crop load is essential for plant growth. Excessively high crop loads lead to nutrient diversion, impeding berry development, whereas overly low crop loads induce vigorous vegetative growth and suppress berry development, causing photosynthates to accumulate in vegetative organs and hindering source-to-sink nutrient transport or even reversing the flow from sink back to source [49]. High crop loads can weaken photosynthesis, thereby reducing the berry anthocyanin concentration and delaying grape maturation, whereas moderate crop loads balance yield and quality [50,51].

The complexity and diversity of volatile compounds in wine are influenced by the climate, grape variety, fermentation processes, and other factors. The F11 treatment group exhibited the highest concentration of volatile compounds, suggesting that crop load management can significantly enhance the richness of aromatic components in wine, supporting the findings of Bowen et al. [13,52]. The sensory evaluations of the wines from the moderate crop load treatments scored higher. This implies that when the crop load is in an optimal range, the grapes receive more photosynthetic energy, impelling flavor compounds to reach a balanced state [12]. The economic benefits of grape cultivation are primarily reflected in the unit price and total yield of fruits of different qualities, while the economic benefits of wine production are reflected in the unit price and total yield of wines made from fruits of varying qualities. Therefore, balancing the relationship between grape and wine quality and yield can effectively enhance the overall economic benefits.

5. Conclusions

This study explored the impact of crop load regulation on Marselan berry and wine qualities. A low crop load improves the sensory composition of grapes. Moderately reducing the crop load significantly enhances the sensory composition of wine. The response of phenolic compounds showed significant differences, with lower loading levels being more conducive to the accumulation of flavonols and hydroxybenzoic acids. Moderate crop load control can enhance anthocyanin synthesis by promoting photosynthesis and significantly increase the total aroma compounds in wine, whereas an excessive crop load (F43) inhibits secondary metabolism due to nutrient competition, leading to reduced anthocyanin and aroma components. The organic acid content showed significant differentiation between medium–high loading and low loading, with the latter exhibiting higher concentrations. The sensory evaluation results showed that the wine from the 30-cluster crop load (15 shoots per meter of canopy, 2 clusters per shoot) treatment achieved the best overall quality, which increased the composition of grape wine without reducing the yield, thereby enhancing the overall economic benefits of the wine. The findings underscore that crop load regulation reshapes metabolic pathways, influencing wine flavor characteristics. This provides a theoretical foundation for the cultivation of high-quality wine grapes.

Future research should verify the stability of crop load regulation in field production, analyze its interaction with environmental factors to elucidate its role in the wine style, and establish quantitative models linking key aromatic compounds (e.g., ethyl caprylate and β-damascenone) with the crop load, optimizing the collaborative strategy between cultivation and winemaking.