Growth Characteristics and Adaptability of Probiotics Using Almond Hull as a Fermentation Substrate

Abstract

This study aimed to develop a high-value plant-based probiotic beverage via the co-fermentation of Lactobacillus plantarum P8 and Bifidobacterium animalis subsp. lactis V9 with almond hull homogenate as the fermentation substrate. Single-factor experiments combined with Box–Behnken response surface methodology were adopted to optimize the key fermentation parameters (compound bacteria ratio, inoculation amount, temperature, and fermentation time), with the probiotic proliferation multiple set as the response value. Furthermore, the physicochemical properties, active component contents, and antioxidant-related indicators of the fermented product were systematically determined and analyzed. The results showed that the optimal fermentation conditions were as follows: a P8:V9 ratio of 1:1, an inoculation amount of 0.1%, a fermentation temperature of 28 °C, and a fermentation time of 66 h. Under these optimal conditions, the fermentation effectively induced the transformation of the bound bioactive components in the almond hull, with the free-flavonoid content increasing by 20.40% and the total polyphenol content decreasing by 6.16% in the fermented product, which reflected the dynamic conversion of nutrient components during the fermentation process. Meanwhile, the antioxidant capacity of the almond hull fermented product was significantly improved compared with the unfermented control. This study confirms the feasibility of almond hull as a suitable matrix for probiotic fermentation, and the findings provide a scientific basis for the development of plant-based synbiotic products and the high-value resource utilization of almond hull as an agricultural by-product.

1. Introduction

The word “probiotics” is defined as “live microorganisms conferring a health benefit on the host when administered in adequate amounts” [1]. Probiotics improve intestinal health, strengthen the immune system, reduce the risk of chronic diseases, promote metabolic health, and support mental and emotional health [2,3,4]. Foods containing probiotics are mainly made by a fermentation process or the direct addition of probiotics. At present, the categories of such foods have formed a multi-system: fermented dairy products, fermented vegetables, fermented soybean products, fermented cereal products, traditional fermented beverages, and modern foods. In addition, fermented meat products, functional products combined with fruits and vegetables, and other ethnic fermented foods have gradually become important types of probiotic carriers [5,6,7].

Compared with fermented dairy products, plant-based fermented products have multiple advantages and are an ideal choice for all kinds of special populations [8,9]. First of all, from the perspective of the adaptation population, plant-based fermented products are lactose-free, thus avoiding abdominal distension, diarrhea, and other discomfort in lactose-intolerant individuals caused by lactose intake from dairy products [10,11]. At the same time, the fat and cholesterol content of these products is generally lower than milk, more friendly to patients with hyperlipidemia [12,13]. Extending beyond suitability, plant-based fermented products also exhibit superior nutritional value, often characterized by higher levels of dietary fiber, essential vitamins, minerals, and bioactive compounds such as polyphenols and flavonoids, which contribute to enhanced gut health and overall well-being [8,9,12,13,14]. At the level of sensory experience, plant-based fermented products also have advantages: the metabolic activity of probiotics in the fermentation process not only increases the complexity of flavor, but also effectively reduces the bad flavors such as those that are “fishy” and “beany” and improves the overall eating experience; at the same time, fermentation can also adjust the texture of the product to make it more palatable and attractive [15,16,17].

The almond hull is the outermost shell of the almond fruit. Studies have shown that it is non-toxic, harmless, and rich in nutrients [18,19], making it suitable for resource utilization in food processing. As the matrix raw material for probiotic fermentation, the functional components such as polyphenols, dietary fiber, and minerals contained in almond hull are transformed into a form that is more easily absorbed by the human body by means of the metabolism of probiotics. A plant-based fermented food with both nutritional and probiotic functions has been developed [20,21,22]. Prebiotics are “a substrate that is selectively utilized by host microorganisms conferring a health benefit”. Synbiotics are “a mixture comprising live microorganisms and substrate(s) selectively utilized by host microorganisms that confers a health benefit on the host” that produce synergistic benefits to health by improving the survival and colonization of host microbiomes [23,24,25]. Therefore, probiotic fermentation products with almond hull as the matrix naturally fit the core characteristics of synbiotics: the dietary fiber, polyphenols and other components in almond hull can be used as natural prebiotics to provide exclusive nutrient substrates for probiotics in the fermentation system; these probiotics can not only achieve their own proliferation and activity improvement in the process of metabolizing prebiotics, but also exert probiotic effects through the synergistic effect of prebiotics and probiotics in the human intestine [26]. Therefore, these fermented products can be used as synbiotics to play a role in the human intestinal microecosystem [27].

The present study aimed to develop a plant-based probiotic beverage via the co-fermentation of the strains Lactobacillus plantarum P8 and Bifidobacterium animalis subsp. lactis V9. We investigated the proliferation of the two strains under different conditions and comprehensively evaluated the fermentation quality of the fermented almond hull homogenate by determining the pH, probiotic proliferation, reducing sugar content, protein content, total polyphenol content, total flavonoid content, and antioxidant activity.

2. Materials and Methods

2.1. Probiotic Strains

Lactobacillus plantarum P8 (P8) and Bifidobacterium animalis subsp. lactis V9 (V9) were purchased from Beijing Hongce Technology Development Co., Ltd. (Beijing, China). Prior to fermentation, all strains were inoculated into de Man, Rogosa, and Sharpe (MRS) broth medium at an initial inoculum concentration of 1 × 10⁵ CFU/mL for activation and incubated statically under anaerobic conditions at 36 °C for 24 h. Subsequently, the cultures were streaked onto MRS agar plates for isolation; single colonies with uniform morphology were picked and subjected to subsequent scale-up cultivation [28].

2.2. Preparation of Plant Medium

Almond hulls of Monterey variety from California, USA, with no mildew, mechanical damage, or microbial contamination, were selected as the raw material. The hulls were rinsed repeatedly with deionized water to remove surface sediment and impurities, drained of excess water, and dried to constant weight in a forced-air blast-drying oven at 60 °C.

The pretreated almond hulls were converted into fermentation substrate using a standardized three-step protocol (soaking → grinding → enzymatic hydrolysis), with a fixed initial solid-liquid ratio of 1:10 (g dry hulls:mL deionized water) to ensure consistent subsequent dilution:

Soaking: The almond hulls were immersed in deionized water at the above solid-liquid ratio and soaked at 25 ± 2 °C (room temperature) for 3 h;

Grinding: The soaked hull-water mixture was transferred to a colloid mill and continuously ground for 3 h until all particles were reduced to below 100 μm to achieve complete homogenization;

Enzymatic hydrolysis: The homogenate was adjusted to pH 4.5 using 1% citric acid-sodium citrate buffer solution. A mixed enzyme system consisting of cellulase (50,000 U/g) and pectinase (100,000 U/g) at a mass ratio of 3:1 was added. The total enzyme dosage was 6% (w/w), calculated as the sum of the cellulase and pectinase masses relative to the dry weight of the raw almond hulls. Enzymatic hydrolysis was performed at 50 °C with constant orbital shaking at 150 r/min for 8 h. The resulting almond hull enzymatic hydrolysate was rapidly cooled to 25 ± 2 °C and stored at 4 °C until it was used in subsequent fermentation experiments.

2.3. Investigation of Probiotic Proliferation Characteristics Under Single-Factor Conditions

2.3.1. Sterilization of Substrate and Preparation of Inoculum

A 90 mL aliquot of the almond hull enzymatic hydrolysate was transferred to a 250 mL Erlenmeyer flask, subjected to water-bath sterilization at 80 °C for 20 min, and rapidly cooled to room temperature for use. For inoculum preparation, the composite probiotics in the exponential growth phase were collected by centrifugation at 8000 r/min for 10 min at 4 °C. The bacterial precipitate was washed twice with sterile 0.85% (w/v) normal saline to remove residual MRS medium, then resuspended in the same sterile normal saline. The bacterial suspension was serially diluted to a final concentration of 1 × 10⁵ CFU/mL to prepare the seed liquid, with the viable cell concentration quantified via the plate counting method on MRS agar medium.

2.3.2. Single-Factor Experimental Design

Based on the basic fermentation conditions (P8:V9 = 1:1, inoculum size 0.1%, 36 °C, initial pH 6.0, 36 h), single-factor experiments were conducted following the single variable principle (only one factor changed at a time, others kept constant) to explore the independent effects of composite strain ratio, inoculum amount, culture temperature, and fermentation time on probiotic proliferation.

The gradient settings of each variable were as follows: culture temperatures: 24, 28, 32, 36, and 40 °C; fermentation times: 12, 24, 36, 48, 60, 72, and 84 h; composite strain ratios (P8:V9): 5:1, 3:1, 1:1, 1:3, and 1:4 (v/v); and inoculum sizes: 0.1%, 0.5%, 1.0%, 2.0%, and 5.0% (v/v).

All treatments used 90 mL of sterile almond hull enzymatic hydrolysate as the fermentation substrate, with a blank control group (uninoculated almond hull enzymatic hydrolysate under the same culture conditions) set for each experiment. The viable cell count of probiotics was determined after fermentation to identify the optimal level range of every single factor for probiotic proliferation. Each treatment was performed in three biological replicates with three technical replicates.

2.4. Response Surface Optimization Experimental Design

Based on the results of the single-factor experiments, three key influencing factors (fermentation time, culture temperature, and composite bacteria ratio) and their optimal level ranges were selected for response surface methodology optimization. A three-factor, three-level Box–Behnken design was implemented using Design-Expert 13 software, with the viable count proliferation multiple of total probiotics (calculated as the ratio of viable count after fermentation to the initial inoculation viable count) as the response value. The coded levels and actual ranges of experimental factors are presented in

Table 1. Factors and levels of Box–Behnken response surface design.

Factor	Level
	−1	0	1
Fermentation time (h, A)	54	60	66
Culture temperature (°C, B)	26	28	30
P8:V9 (v/v, C)	1.75	1	0.25

. All experiments were conducted with three biological replicates and three technical replicates to ensure data reliability.

2.5. Organoleptic Investigation

Sensory evaluation was performed in a standard sensory tasting room compliant with ISO 8589:2007 [29] and GB/T 31326-2014 [30]. A panel of 10 professionally trained assessors (5 females, 5 males, aged 18–35 years) conducted blind testing of almond hull fermentation broths for appearance, texture, flavor and taste using a randomized complete block design. In accordance with the 2016 Measures for the Ethical Review of Biomedical Research Involving Human Subjects (Article 2), this non-biomedical research is not subject to ethical review as the scope is limited to “medical institutions” [31]. Additionally, under Article 32 of the 2023 Measures for the Ethical Review of Clinical Research Involving Human Subjects issued by the National Health Commission, “routine food sensory evaluation” is explicitly excluded from requiring ethical review [32]. Therefore, this research is ethically exempt.

All samples were presented at 25 ± 1 °C in identical white plastic cups labeled with unique three-digit random codes, with fully randomized presentation order per assessor. Each sample was evaluated in triplicate, with a 5 min rest interval between samples during which assessors rinsed their mouths with purified water. The detailed evaluation criteria are provided in

Table 2. Sensory rating form.

Evaluation Item	Single Score	Marking Criterion	Sensory Scores
Appearance	20	Orange-brown, uniform color, opaque.	16–20
		Orange-brown, slightly uniform, slightly clear.	10–15
		Other colors, not opaque.	0–9
Texture	30	Natural stratification, the upper layer is clear and transparent, the lower layer shows material precipitation.	20–30
		Layers visible, upper layer opaque.	10–19
		Turbid, layers unclear.	0–9
Tasted	20	Suitable balance of sweet and sour, soft taste, smooth and refreshing.	16–20
		The taste is neutral, the sour taste is slightly prominent.	10–15
		The taste is harsh and astringent-heavy with a sour/unpleasant mouthfeel.	0–9
Flavour	30	The plant has a strong aroma and moderate taste.	20–30
		The plant aroma is light and the sour taste is obvious.	10–19
		No plant aroma, with a sour and astringent taste, rancid taste or other peculiar smell.	0–9

.

2.6. Determination of Viable Cell Count

Viable cell counts were determined for samples at the start of fermentation (0 h) and at the fermentation endpoint. Under aseptic conditions, 1.0 mL of the sample was mixed thoroughly with 9.0 mL sterile saline to prepare a 10⁻¹ dilution, followed by serial 10-fold dilutions. Three appropriate consecutive dilutions were selected, and 0.1 mL aliquots of each were spread evenly onto MRS agar plates in triplicate. After full absorption of the inoculum, plates were inverted and incubated at 28 ± 1 °C for 48 ± 2 h. Colonies were counted on plates with 30–300 CFU, and results were expressed as colony-forming units per milliliter (CFU/mL) of sample.

2.7. Determination of pH

The pH values of samples at the initial fermentation stage (0 h) and the fermentation endpoint were determined. The pH value of the fermented and control samples was measured using a digital pH meter (PHS-3C, Shanghai Instrument and Electrical Science Instrument Co., Ltd., Shanghai, China) calibrated with standard buffer solutions (pH 4.00 and 6.86) prior to detection.

2.8. Determination of Reducing Sugars

The reducing sugar content of the samples was determined using a commercial reducing sugar assay kit (Solarbio, Cat. No. BC0235, Beijing, China). Briefly, 0.1 mL of sample solution was thoroughly mixed with 1 mL of extraction solution. The mixture was incubated in an 80 °C water bath for 40 min, with vigorous shaking 8–10 times to ensure a complete reaction. After incubation, the mixture was centrifuged at 8000 rpm for 10 min at room temperature, and the supernatant was collected as the test solution. A 175 μL aliquot of the appropriately diluted test solution was mixed with 125 μL of 3,5-dinitrosalicylic acid (DNS) reagent. The mixture was heated in a boiling water bath for 5 min, cooled to room temperature, and mixed thoroughly. A 200 μL portion of the mixture was transferred to a 96-well plate, and the absorbance was measured at 540 nm. A standard curve was plotted using glucose as the standard, with the regression equation: y = 4.1892x − 0.2287 (R² = 0.9980). The reducing sugar content was calculated based on the standard curve, as described in Equation (1).

$$\mathrm{Sugar} \mathrm{content} (\mathrm{mg}/\mathrm{g})={\displaystyle \frac{\mathrm{x} \times {\mathrm{N}\times \mathrm{V}}_1}{\mathrm{W}}}$$

(1)

where y is the absorbance at 540 nm (A); x is the reducing sugar concentration, mg/mL; N is the dilution factor; V₁ is the total volume of sample extract, mL; and W is the sample mass, g.

2.9. Determination of Soluble Protein

The soluble protein content was determined using a BCA protein assay kit (Solarbio, Cat. No. PC0020, Beijing, China). The detailed procedure was as follows: Firstly, a BCA working solution was prepared by mixing BCA reagent and Cu⁺ reagent at a volume ratio of 50:1 according to the required volume of standards and samples, and the well-mixed working solution remained stable for 24 h at room temperature. The sample was centrifuged to collect the supernatant. Then 20 μL of the appropriately diluted supernatant was mixed with 200 μL of the BCA working solution, and the mixture was incubated at 37 °C for 15–30 min. After incubation, the absorbance was measured at 562 nm using a microplate reader. A standard curve was plotted using bovine serum albumin (BSA) as the standard, with the regression equation: y = 0.8251x − 0.0012 (R² = 0.9994). The soluble protein content was calculated according to the standard curve, as described in Equation (2):

$$\mathrm{Protein} \mathrm{concentration} (\mathrm{mg}/\mathrm{g}) = {\displaystyle \frac{\mathrm{x} \times {\mathrm{N}\times \mathrm{V}}_2}{\mathrm{W}}}$$

(2)

where y is the absorbance at 562 nm (A); x is the reducing protein concentration, mg/mL; N is the dilution factor; V₂ is the total volume of sample extract, mL; and W is the sample mass, g.

2.10. Determination of Total Flavonoids

Total free-flavonoid content was determined using a plant total flavonoid assay kit (K-019, Shaanxi Yirong Da Cheng Biotechnology Co., Ltd., Xi’an, China). Briefly, the sample was centrifuged to collect the supernatant. A 1.2 mL aliquot of the appropriately diluted supernatant was thoroughly mixed with 0.2 mL of 5% (w/v) sodium nitrite solution, and the mixture was allowed to stand for 6 min. Then, 0.2 mL of 10% (w/v) aluminum nitrate solution was added, and the mixture was shaken and left to stand for another 6 min. Subsequently, 1 mL of 4% (w/v) sodium hydroxide solution and 1.4 mL of distilled water were added. After standing for 15 min, the absorbance was measured at 510 nm. A standard curve was established using rutin as the reference standard, with the regression equation y = 0.0127x − 0.0036 (R² = 0.9989). The total flavonoid content was calculated based on the standard curve, as presented in Equation (3).

$$\mathrm{Total} \mathrm{flavonoid} (\mathrm{mg}/\mathrm{g})={\displaystyle \frac{\mathrm{x} \times {\mathrm{V}}_3\times \mathrm{D}}{\mathrm{m}\times 1000}}$$

(3)

where y is the absorbance at 510 nm (A); x is the flavonoid concentration, μg/mL; V₃ is the total volume of sample extract, mL; D is the sample dilution factor; and m is the mass of the fermented sample, g.

2.11. Determination of Total Polyphenols

Total polyphenol content was determined via the Folin-Ciocalteu colorimetric method: the sample was centrifuged to collect the supernatant, and 200 μL of appropriately diluted supernatant was mixed with 400 μL of 20% (v/v) Folin-Ciocalteu (Sinopharm Chemical Reagent Co., Ltd., Shanghai, China)reagent and allowed to stand for 1 min at room temperature. Subsequently, 200 μL of 15% (w/v) Na₂CO₃ solution was added, and the mixture was incubated in the dark for 40 min. The absorbance was measured at a wavelength of 765 nm. A standard curve was prepared using gallic acid as the standard. The regression equation was y = 0.0145x + 0.0359 (R² = 0.9979).

The total polyphenol content was calculated from the standard curve and expressed as mg gallic acid equivalents (GAEs) per gram of sample.

$$\mathrm{Total} \mathrm{polyphenols}={\displaystyle \frac{\mathrm{x}\times {\mathrm{V}}_4\times \mathrm{N}}{\mathrm{M}}}$$

(4)

where y is the absorbance at 765 nm (A); x is the polyphenol concentration, mg/mL; V₄ is the total volume of sample extract, mL; N is the sample dilution factor; and M is the mass of the fermented sample, g.

2.12. Determination of Total Antioxidant Capacity

The total antioxidant capacity of the samples was comprehensively characterized by DPPH radical scavenging capacity and FRAP ferric reducing antioxidant power.

2.12.1. Determination of DPPH Radical Scavenging Capacity

DPPH radical scavenging capacity was determined using a DPPH assay kit (BIOISCO, AO043, Iseju (Lianyungang, China) Biotechnology Co., Ltd., Lianyungang, China).

Fermentation broth samples were centrifuged at 4000 rpm and 4 °C for 10 min, and the supernatant was collected. The supernatant was appropriately diluted with pre-chilled kit extraction solution to prepare test samples, with three parallel replicates for each sample. The test sample was mixed with DPPH solution at a volume ratio of 1:18, and the mixture was incubated in the dark at room temperature for 20 min. Absorbance at 515 nm was measured to record the blank absorbance (A₂) and sample absorbance (A₁). The absorbance difference was calculated as ΔA = A₁ − A₂.

The total antioxidant capacity was quantified as Trolox equivalents. The regression equation was y = 0.7072x − 0.0081 (R² = 0.9977), where x is the Trolox concentration (μmol/mL) and y is ΔA. The volume-based total antioxidant capacity of the fermentation broth was calculated according to Equation (5):

Total antioxidant capacity (µmoL·Trolox/mL) = 1.414 × (△A + 0.0081)

(5)

2.12.2. Determination of TRAP Ferric Reducing Antioxidant Power

FRAP ferric reducing antioxidant power was determined using a FRAP assay kit (BIOISCO, AO037, Iseju (Lianyungang, China) Biotechnology Co., Ltd., Lianyungang, China).

Prior to the assay, fresh working reagent was prepared by mixing FRAP buffer, 2,4,6-tripyridyl-s-triazine (TPTZ) solution, and ferric chloride solution at a volume ratio of 10:1:1. The reagent was pre-warmed in a 37 °C water bath before use. Fermented samples were prepared in the same manner as described in Section 2.12.1. Each sample was analyzed in three parallel replicates. The diluted sample was mixed with the working reagent at a volume ratio of 1:19. After thorough mixing, the mixture was incubated at room temperature for 20 min. Absorbance was measured at 593 nm to record blank absorbance (A₄) and test absorbance (A₃). The absorbance difference was calculated as ΔA = A₃ − A₄. The regression equation was y = 2.4832x + 0.0134 (R² = 0.9996), where x represents Trolox concentration (μmol/mL) and y represents ΔA. The volume-based total antioxidant capacity of the fermentation broth was calculated according to Equation (6).

Total antioxidant capacity (µmoL·Trolox/mL) = 0.4027 × (ΔA − 0.0134)

(6)

2.13. High-Performance Liquid Chromatography–Tandem Mass Spectrometry

Liquid Chromatography Parameters:

The sample injection volume was 2.0 μL, the total flow rate was 0.3 mL/min, and the column temperature was maintained at 40 °C. The separation was performed on a ZORBAX Eclipse Plus C18 rapid-resolution chromatographic column (3.0 mm × 150 mm, 1.8 µm) (Agilent Technologies, Inc., Santa Clara, CA, USA). The mobile phase consisted of solvent A (water containing 0.1% formic acid, v/v) and solvent B (methanol). Gradient elution was carried out as follows: 0–0.5 min, 5% B; 0.5–4 min, 5–40% B; 4.0–12 min, 40–95% B; 12–16 min, 95% B; 16–20 min, 5% B. One QC sample was injected every 5 samples to monitor the stability of the analytical system.

Mass Spectrometry Parameters:

Electrospray ionization (ESI) was used. The spray voltage was set at 3.5 kV for positive mode and 3.0 kV for negative mode. The capillary temperature was 320 °C, and the heater temperature was 350 °C. The sheath gas flow rate was 40 arb. The data were acquired in positive/negative ion switching mode. The mass scan range was m/z 80–1200. The automatic gain control (AGC) target was set at 1 × 10⁵, with a maximum ion injection time of 50 ms. The mass resolution was 17,500. The collision-induced dissociation energies were set at 20, 30, and 40 eV. Full-scan MS was used for quantitative analysis, while data-dependent full MS/dd-MS² was used for compound identification. MS data were collected using Xcalibur 2.2 software (Thermo Fisher Scientific, Waltham, MA, USA) and saved in RAW format.

All raw mass spectrometry data were acquired using Xcalibur software (Thermo Fisher Scientific) and saved as raw files. Data preprocessing was performed using Compound Discoverer 3.3 software, including peak extraction, retention time alignment, background signal removal, and peak integration. Batch normalization and quality control were carried out with reference to quality control (QC) samples. Peak signals with a relative peak area relative standard deviation (RSD) > 30% in QC samples were removed to ensure data reliability. Adduction annotation of the extracted metabolite peaks was performed using the CAMERA tool, and preliminary metabolite identification was achieved by combining full-scan MS and MS/MS information. Multivariate statistical analysis of the normalized three-dimensional data matrix was performed using MetaboAnalyst 6.0 and the Bioinformatics platform. Data were normalized by sum, log10-transformed, mean-centered, and scaled to unit variance. Principal component analysis (PCA) was applied to observe the overall separation trend and intragroup variation among samples and to evaluate model stability. Partial least squares-discriminant analysis (PLS-DA) was used to establish a correlation model between metabolite expression levels and sample groups, maximize the separation of metabolic differences between groups, and calculate variable importance in projection (VIP) values to screen metabolites that contributed significantly to group discrimination. Screening of differential metabolites combined univariate and multivariate analyses. Metabolites were considered significantly different only when they simultaneously met the following criteria: fold change (ratio) ≥ 2 or ≤1/2, two-tailed t-test p-value < 0.05, and VIP value from PLS-DA ≥ 1.0.

2.14. Statistical Analysis

All experiments were performed with three biological replicates and three technical replicates. All data are expressed as mean ± standard deviation (n = 3). The normality of all datasets was confirmed by the Shapiro-Wilk test. One-way analysis of variance (ANOVA) was used to assess the overall differences among groups, followed by a Tukey HSD post hoc test for pairwise comparisons and false discovery rate (FDR) correction using the Benjamini-Hochberg procedure. In all bar graphs, different lowercase letters indicate statistically significant differences among treatment groups (p < 0.05); the same lowercase letters mean no statistically significant differences among treatment groups (p > 0.05).

3. Results

3.1. Effect of Temperature on the Proliferation Characteristics of Probiotics

Fermentation temperature exhibited a significant temperature-dependent regulatory effect on the proliferation of P8 and V9 in almond hull enzymatic hydrolysate (Figure 1). The probiotic proliferation multiple presented a typical rise-peak-fall trend with increasing temperature from 24 °C to 40 °C, reaching the maximum value ((312.48 ± 9.09) times) at 28 °C, which was significantly higher than that of all other temperature groups (p < 0.05). Proliferation was moderately inhibited at 24 °C, 32 °C, and 36 °C ((295.37 ± 8.54) times, (287.98 ± 8.51)times, and (260.33 ± 7.56) times, respectively), and severely suppressed at 40 °C ((121.76 + 2.00) times), with the proliferation multiple declining to the lowest level in the test range. This result indicated that 28 °C was the optimal temperature for the co-proliferation of the two strains, matching their microaerophilic growth characteristics and enabling the highest activity of functional enzyme systems for nutrient utilization in the almond hull substrate.

3.2. Effect of Fermentation Time on the Proliferation Characteristics of Probiotics

Fermentation time significantly affected probiotic proliferation (p < 0.05), with the proliferation multiple showing a clear ascending (12–60 h)-peak (60 h)-descending (60–84 h) trend (Figure 2). The proliferation of multiple increased continuously with extended fermentation time and reached the maximum value ((344.31 ± 8.98) times) at 60 h, which was significantly higher than all other time gradients. After 60 h, the proliferation multiple decreased gradually, which was attributed to the combined effects of gradual nutrient depletion in the substrate and accumulation of probiotic metabolic by-products (e.g., organic acids) that created an adverse microenvironment for continuous growth. Thus, 60 h was identified as the optimal fermentation time for maximum probiotic proliferation.

3.3. Effect of the Proportion of Compound Bacteria on the Proliferation Characteristics of Probiotics

The ratio of P8 to V9 had a significant impact on co-proliferation efficiency (p < 0.05), with the proliferation multiple showing a rise-peak-fall trend with the strain ratio changing from 5:1 to 1:5 (Figure 3). The minimum proliferation multiple ((152.69 ± 3.00) times) was observed at a P8:V9 ratio of 5:1; as the proportion of P8 decreased and V9 increased, the proliferation multiple increased gradually and reached the maximum value ((251.5 ± 5.99) times) at a 1:1 ratio. With a further increase in V9 proportion (1:3 and 1:5), the proliferation multiple decreased significantly. This confirmed that a 1:1 strain ratio achieved the optimal synergistic balance between the two strains, minimizing nutrient competition and maximizing the positive interaction of metabolic by-products, which was the key to the highest co-proliferation efficiency.

3.4. Effect of the Inoculation Amount of Compound Bacteria on the Proliferation Characteristics of Probiotics

Inoculum amount (0.1–5.0%, v/v) showed a significant negative correlation with probiotic proliferation multiple in almond hull enzymatic hydrolysate (p < 0.05, Figure 4). The maximum proliferation multiple was obtained at a 0.1% inoculum amount, which was significantly higher than that of all high-inoculum-amount groups (0.5–5.0%). With an increase in inoculum amount, the proliferation multiple decreased continuously, and the minimum value was observed at a 5.0% inoculum amount. At a low inoculum amount (0.1%), the initial probiotic biomass was far lower than the substrate nutrient content, resulting in weak inter-strain competition and efficient nutrient utilization, thus exhibiting a high proliferation multiple. High inoculum amounts led to rapid nutrient consumption, massive accumulation of metabolic inhibitory substances, and intense inter-strain competition, all of which significantly reduced proliferation efficiency.

3.5. Optimization of Response Surface Experiment Results

Based on single-factor experiment results, fermentation time (A), temperature (B), and strain ratio (C) were selected as independent variables, and probiotic proliferation multiple as the response value (Y), for a three-factor, three-level Box–Behnken experiment. A quadratic polynomial regression equation was fitted: Y = 333.26 − 1.84A + 3.72B + 2.07C − 6.45AB − 5.65AC − 10.00BC − 13.55A² − 25.45B² − 26.99C².

Analysis of variance (ANOVA,

Table 3. ANOVA for the quadratic polynomial model.

Source	Sum of Squares	Degree of Freedom	Mean Square	F Value	p Value	Significance
Model	8128.06	9	903.12	33.88	<0.0001	significant
A	27.05	1	27.05	1.01	0.3473
B	110.41	1	110.41	4.14	0.0813
C	34.32	1	34.32	1.29	0.2939
AB	166.15	1	166.15	6.23	0.0412	*
AC	127.80	1	127.80	4.79	0.0647
BC	400.00	1	400.00	15.01	0.0061	**
A2	772.92	1	772.92	28.99	0.0010	**
B2	2726.36	1	2726.36	102.28	<0.0001	**
C2	3066.92	1	3066.92	115.05	<0.0001	**
Residual	186.60	7	26.66
Lack of Fit	36.67	3	12.22	0.3262	0.8079	not significant
Pure Error	149.92	4	37.48
Cor Total	8314.66	16
	R2	0.9776
	Adjusted R2	0.9487

Note: ** indicates a highly significant difference (p < 0.01), and * indicates a significant difference (p < 0.05).

) showed the model was extremely significant (F = 33.88, p < 0.0001), with a non-significant lack of fit (p = 0.8079), a coefficient of determination R² = 0.9776, and an adjusted R² = 0.9487. This indicated the model had a high fitting degree and strong predictability, explaining 97.76% of the variation in the proliferation multiple. The order of factor influence on the response value was strain ratio (C) > fermentation temperature (B) > fermentation time (A). The interaction term BC (temperature × strain ratio) was extremely significant (p = 0.0061), AB (time × temperature) was significant (p = 0.0412), and AC (time × strain ratio) was non-significant (p = 0.0647). All quadratic terms (A², B², C²) were extremely significant (p < 0.01), confirming the factors had a non-linear effect on probiotic proliferation.

As shown in Figure 5, when fermentation time and fermentation temperature exerted a synergistic effect, the proliferation multiple of probiotics fluctuated with the adjustment of these two factors, showing a trend of first increasing and then decreasing. The contour plot exhibited an obvious elliptical shape, indicating that the synergistic effect of fermentation time and temperature had a significant interactive effect on probiotic proliferation, which was consistent with the results obtained from analysis of variance.

As shown in Figure 6, when fermentation time and composite strain ratio exerted a synergistic effect, the proliferation multiple of probiotics fluctuated with the adjustment of these two factors, also presenting a trend of first increasing and then decreasing. The contour plot was circular, suggesting that the synergistic effect of fermentation time and composite strain ratio had no significant interactive effect on probiotic proliferation. This result was consistent with that of analysis of variance.

As shown in Figure 7, when fermentation temperature and composite strain ratio exerted a synergistic effect, the proliferation multiple of probiotics fluctuated with the adjustment of these two factors, showing a trend of first increasing and then decreasing. The contour plot displayed a distinct elliptical shape, indicating that the synergistic effect of fermentation temperature and composite strain ratio had a significant interactive effect on probiotic proliferation, which was in agreement with the results of analysis of variance.

Design-Expert software predicted the optimal fermentation conditions for the maximum proliferation multiple as: a fermentation time of 65.89 h, a temperature of 27.92 °C, and a strain ratio of 0.96 (P8/V9). Considering industrial production practicality and operability, the conditions were fine-tuned to the following optimal parameters: a fermentation time of 66 h, a temperature of 28 °C, a P8:V9 ratio of 1:1 (v/v), and an inoculum amount of 0.1% (v/v). The verification experiment yielded an actual proliferation multiple of 320 times, which was consistent with the predicted value (318.55 times, p > 0.05), further confirming the model’s reliability and practical applicability for industrial scale-up.

3.6. Organoleptic Investigation

Based on the human sense of smell, vision, taste and so on, a sensory evaluation of the fermentation broth of the almond hull was carried out, so as to judge the color, flavor, taste and texture of the fermentation broth of the almond hull, and then the quality of the fermentation broth of almond hull was analyzed in all aspects. In this study, sensory analysis and comparison were performed on the 1st, 2.5th and 4th days for the unfermented almond hull homogenate and probiotic mixed fermented almond hull homogenate. The results are shown in

Table 4. Sensory evaluation score sheet.

Peer Group	Appearance (0–20)	Texture (0–30)	Taste (0–20)	Flavor (0–30)	Aggregate Score (0–100)
Control group	6.6	6.2	5	6.1	23.9
1.0-day fermentation group	13.1	15.1	13.4	16.3	57.9
2.5-day fermentation group	16.7	26.1	16.4	26.3	85.5
4.0-day fermentation 4.0 group	16.9	24.5	15.4	22.4	79.2

. The following conclusions can be drawn: the fermented almond hull homogenate was superior to the unfermented almond hull homogenate in appearance, taste, tissue state and flavor, and the sensory evaluation score of pear juice fermented for 2.5 days reached 85.5 points.

3.7. Changes in pH and Probiotic Count of Fermentation Broth

The pH of the fermentation group decreased significantly from 6.0 in the control group to 4.8 (p < 0.05) (Figure 8). This was a direct metabolic consequence of P8 and V9 fermenting sugars in the hydrolysate to produce organic acids such as lactic acid and acetic acid. Concurrently, the viable probiotic count increased substantially from 1.05 × 10⁵ CFU/mL in the control group to 3.36 × 10⁷ CFU/mL in the fermentation group. These significant changes confirm a strong correlation between the decrease in pH and the increase in viable cell count, indicating that under optimized conditions, the almond hull enzymatic hydrolysate can effectively support the growth and proliferation of the two probiotic strains. Furthermore, the final pH of 4.8 is conducive to maintaining the stability and biological activity of the probiotics. This not only achieves the goal of converting agricultural waste into a high-value functional substrate but also provides a sustainable approach for the food and feed industries to produce high-count probiotic products, aligning with the principles of a circular economy.

3.8. Reducing Sugars and Soluble Protein

The content of reducing sugars in the fermentation broth decreased by 4.23%, while the protein content increased by 3.37% (Figure 9). It is speculated that reducing sugars were primarily utilized as carbon sources for probiotic proliferation, while polysaccharide-hydrolyzing enzymes secreted by probiotics could degrade macromolecular polysaccharides into reducing sugars to replenish the consumed fraction. This dynamic balance between consumption and replenishment accounts for the slight net change in reducing sugar content between the fermentation and control groups. Consistent with this mechanism, Mantzourani et al. reported 3.6% and 5.7% reductions in reducing sugar content after 24 h fermentation of mixed fruit juice (red apple, orange, pomegranate, red grape) with Lactiplantibacillus paracasei SP5 and Pediococcus pentosaceus SP2, respectively [33]. Notably, Lee et al. observed a 51.10% increase in reducing sugar content after 72 h fermentation of isoflavone-enriched soybean leaves with a composite starter of Lactiplantibacillus plantarum P1201 and Levilactobacillus brevis BMK184 [34], further confirming that the net change in reducing sugars depends on the relative rates of microbial consumption and polysaccharide hydrolysis. For the elevated protein content, two synergistic mechanisms are proposed: proteases secreted by probiotics cleave protein-binding linkages to convert bound proteins into detectable soluble proteins, and de novo-synthesized microbial proteins provide an additional protein source. These effects are corroborated by independent studies: Naseem et al. documented an increase in protein content from 2.53% to 3.53% after 120 h fermentation of spinach (Spinacia oleracea) leaves with Lactiplantibacillus plantarum ATCC 8014 [35], and Lee et al. reported a 4.28% rise in soluble protein content under their aforementioned fermentation conditions [34].

3.9. Total Polyphenols and Total Flavonoids

Probiotic fermentation significantly altered the contents of total polyphenols and free flavonoids in almond hull enzymatic hydrolysate (p < 0.05, Figure 10). Compared with the control, the fermentation group exhibited a 6.16% decrease in total polyphenols and a 20.40% significant increase in free flavonoids, indicating the directional biotransformation of bound bioactive components into their free forms.

Almond hull polyphenols and flavonoids predominantly exist as bound macromolecular polymers (e.g., flavonoid glycosides conjugated with polysaccharides or proteins). Glycosidases and esterases secreted by probiotics hydrolyze their glycosidic and ester bonds, degrading macromolecular polyphenols into small molecules and converting bound flavonoids into free forms—the primary driver of elevated free-flavonoid content. Wen et al. reported that fermentation of litchi pericarp proanthocyanidins with Lacticaseibacillus rhamnosus HN001 completely degraded the major polyphenolic component (type-A proanthocyanidins), while syringetin 3-O-glucoside content increased by 71.43% and flavonols including kaempferol 3-O-glucoside were also significantly elevated [36]; Kothari et al. further observed a 6% significant increase in total flavonoids after 24 h fermentation of leek juice with Lactiplantibacillus plantarum [37].

The slight reduction in total polyphenols arises from two synergistic effects: partial polyphenols undergo oxidative degradation into quinones catalyzed by polyphenol oxidase, and probiotics selectively utilize simple-structured polyphenols as carbon sources, whereas complex-structured flavonoids are recalcitrant to microbial utilization and thus become enriched. Consistent with this mechanism, Yan et al. fermented a mixed herbal extract with five different Lactobacillus strains for 72 h, and recorded total polyphenol reductions ranging from 10.06% to 20.44% across all treatments [38]. Notably, the magnitude of changes in polyphenol and flavonoid profiles varies across plant-based fermentation systems. These discrepancies are not contradictory but stem from the combined effects of raw material structure, substrate composition, strain-specific metabolic traits, and fermentation parameters.

3.10. Total Antioxidant Capacity

The total antioxidant capacity of almond hull enzymatic hydrolysates before and after fermentation was evaluated via DPPH radical scavenging and FRAP reducing power assays (Figure 11). The fermented group exhibited significantly higher overall antioxidant capacity than the unfermented control: the DPPH radical scavenging rate increased by 14.43% (p < 0.05), while the FRAP reducing power showed a non-significant increase of 5.93%. This enhancement arises from the synergistic action of multiple bioactive components generated during fermentation: the significant elevation in DPPH scavenging activity is primarily driven by the 24.72% increase in free flavonoids (whose phenolic hydroxyl groups effectively donate electrons to neutralize free radicals), supplemented by antioxidant small-molecular peptides from protein hydrolysis and highly bioavailable small-molecular polyphenol metabolites; the mild FRAP improvement indicates fermentation exerts weak effects on reducing-type antioxidants (e.g., reducing sugars) in almond hull, and its limited enhancement is also mainly attributed to the increased phenolic hydroxyl groups in the system. These findings are consistent with independent studies: Guo et al. reported a 5.6–8.6% increase in DPPH scavenging activity but a 7.0–13.2% decrease in FRAP value after 48 h fermentation of tomato pulp with Lactiplantibacillus plantarum [39], while Wang et al. observed simultaneous increases of 16.4% in DPPH activity and 19.4% in FRAP value following 48 h fermentation of Aronia melanocarpa juice with Lacticaseibacillus rhamnosus JYLR-005 [38]. The inter-study variability in both DPPH and FRAP responses, which stems from differences in substrate composition, strain-specific metabolic traits and fermentation parameters, fully explains the differential magnitude of changes observed in this study [40].

Note: For the intergroup difference analysis results of all detected indicators between the control group and the fermentation group, the Benjamini–Hochberg method was used for false discovery rate (FDR) multiple-testing correction. The results showed that the adjusted q-values of all indicators were < 0.05, indicating that the differences between the fermentation group and the control group in all detected indicators were statistically significant.

3.11. Results of Liquid Chromatography–Mass Spectrometry Analysis of Polyphenols in Fermentation Broth of Almond Hull

Liquid chromatography–tandem mass spectrometry (LCMS/MS) was used to systematically compare and analyze the chemical constituents of the fermentation broth and the unfermented broth of almond hull. The results of qualitative and semi-quantitative analysis showed that microbial fermentation had a significant regulatory effect on the chemical composition of the extract of almond hull. The four polyphenols, 4-Vinylphenol, Benzenepentol, 4-Hydroxy-3-methylbenzoic, Salicylaldehyde and Isofraxidin, which were stable in the unfermented samples, were not detected after microbial fermentation. It was speculated that these substances were degraded, transformed or metabolized by functional microorganisms during the fermentation process. The above results showed that microbial fermentation could reshape the phenolic composition of the extract of almond hull through complex metabolic reactions, so that some endogenous components disappeared and new characteristic components were generated. In addition, the results of

Table 5. Identified polyphenolic compounds in almond hull.

Common Polyphenols	Specific Compounds
Simple phenols	2,6-Dimethoxyphenol, 2-Methoxy-phloroglucinol, 4-aminocatechol, Apocynin, Catechol, diacetylphloroglucinol, Hydroquinone, p-Cresol, Phenol, p-Hydroxybenzalacetone, Pyrogallol, salicyl alcohol, Tyrosol, Vanillin,
Phenolic acids	(2E)-3-(3,4-Dimethoxyphenyl)acrylic acid, (E)-Ferulic acid, (E)-Isoferulic acid, (E)-p-coumaric acid, 3,4-dihydroxyphenylacetic acid, 3,5-Dimethoxybenzoic acid, 3-Phenyllactic acid, 4-Amino-3-hydroxybenzoic acid, 4-Methoxysalicylic acid, 4-O-feruloyl-D-quinic acid, 5-Methoxysalicylic acid, Benzoic acid, Benzoic acid, Caffeic acid, Chlorogenic acid, DL-4-Hydroxyphenyllactic acid, Ferulic acid, Gallic acid, Gentisic acid, Homovanillic acid, Isovanillic acid, Methyl caffeate, Neochlorogenic acid, Protocatechuic acid, Salicylic acid, Sinapinic acid, Syringic acid, Vanillyl mandelic acid, β-Resorcylic acid
Flavonoids	2-(3,4-Dihydroxyphenyl)-5H-chromene-3,5,7-triol, Catechin, Cianidanol, isoquercetin, Quercetin, Quercetin-3β-D-glucoside, Rhamnetin, Rutin, Tangeritin, Taxifolin, Trifolin, 2-Hydroxy-2,3-dihydrogenistein, Daidzin,
Phenylpropanoids	3,6-Dihydroxy-2H-chromen-2-one, 4-Methoxycinnamaldehyde, 7,8-Dihydroxy-4-methylcoumarin, 7-Hydroxycoumarine, Asaronaldehyde, Cinnamaldehyde, Cinnamic acid, Coumarin, Fraxetin, Imperatorin, Melilotoside, Safrole, Scopoletin, Scopolin, Sinapyl alcohol, Eugenol,
Lignin class	Balanophonin, Lariciresinol 4-O-glucoside, Coniferyl ferulate,
Other polyphenols	1-O-(2-Hydroxybenzoyl)-β-D-glucopyranose, 1-O-(4-Hydroxybenzoyl)-β-D-glucopyranose, 1-O-[(2E)-3-(3,4-Dihydroxyphenyl)-2-propenoyl]-β-D-glucopyranuronic acid, 2-O-caffeoylglucaric acid, 3-[2-(β-D-Glucopyranosyloxy)-4-methoxyphenyl]propanoic acid, D-(-)-Salicin, Glucogallin, 1,3,5-trihydroxy-4-{[(2E)-3-(3-hydroxy-4-methoxyphenyl)prop-2-enoyl]oxy}cyclohexane-1-carboxylic acid, 3,4-dihydroxyphenylpyruvic acid, (R)-(−)-6-methoxymellein, 6-methoxymellein, Cinnamoyl glycine, p-Cresol glucuronide, vanilloloside, 4-Methoxybenzaldehyde, Acetovanillin

showed that the fermentation group and the micro-fermentation group contained a variety of polyphenols.

Principal component analysis (PCA) was performed to assess the overall metabolic differences and analytical stability among the control, fermentation, and quality control (QC) groups. The PCA score plot (Figure 12A) shows that the first principal component (PC1) and the second principal component (PC2) accounted for 39.4% and 14% of the total variance, respectively. The samples from the control group (red) and fermentation group (green) were clearly separated along the PC1 axis, indicating distinct metabolic profiles between the two groups. The QC samples (blue) clustered tightly together, confirming the high stability and reproducibility of the LC-MS/MS analytical system throughout the experiment.

To further explore the effect of fermentation treatment on the metabolite profile, partial least squares-discriminant analysis (PLS-DA) was performed on the three groups of samples. As shown in Figure 12B, the control and fermentation samples exhibited a significant separation trend in the PLS score space with no overlapping regions, indicating that fermentation treatment markedly altered the metabolic profile of almond hull homogenate. Meanwhile, the quality control (QC) samples were tightly clustered in the score plot, confirming the stability of the LC-MS/MS detection technique and the reliability of the data in this study.

To further characterize the specific metabolic changes induced by fermentation, orthogonal projections to latent structures discriminant analysis (OPLS-DA, C) was applied. The OPLS-DA score plot (Figure 12C) revealed a clear separation between the control (red) and fermentation (green) groups, with the T score [1] explaining 47.9% of the total variance and the orthogonal T score [1] accounting for 9.3% of the variance. This distinct separation confirms that microbial fermentation significantly reshaped the metabolome of the almond hull, leading to profound changes in the chemical composition of the extracts. To assess the overfitting risk of the OPLS-DA model (Figure 12D), a permutation test with 200 iterations was performed. The results showed that all R²Y and Q²Y values of the randomly permuted models were lower than those of the original model, and the intercept of the Q²Y regression line was −0.063 (< 0), indicating that the model was free of overfitting and the intergroup differences were statistically significant.

The PCA, PLS and OPLS-DA score plots collectively demonstrate that microbial fermentation induces significant and reproducible changes in the metabolite profiles of almond hull. The clear separation between the control and fermentation groups, coupled with the tight clustering of QC samples, validates the robustness of the analytical approach and the reliability of the subsequent differential metabolite analysis.

Figure 13 displays a volcano plot of differential metabolites between fermented and unfermented almond hull; a total of 307 metabolites were identified, where red dots represent significantly upregulated metabolites (Sig_Up, n = 14), blue dots represent significantly downregulated metabolites (Sig_Down, n = 34), and gray dots represent non-differential metabolites (NoDiff, n = 259), with the screening criteria that metabolites were considered significantly differential if they met the following three conditions simultaneously: fold change (ratio) ≥ 2 or ≤1/2 (|log₂(FC)| ≥ 1), two-tailed Student’s t-test p-value < 0.05, and variable importance in projection (VIP) ≥ 1.0 from PLS-DA analysis, and after applying these strict filtering criteria, a total of 48 differential metabolites (14 upregulated and 34 downregulated) were finally identified for subsequent functional enrichment and pathway analysis.

Microbial fermentation exerted a profound impact on the metabolomic profile of almond hull extracts. Notably, 34 metabolites were significantly downregulated after fermentation, including polyphenols (4-vinylphenol, benzenepentol, 4-hydroxy-3-methylbenzoic acid, salicylaldehyde, and isofraxidin) and primary metabolites (uridine, uracil, and valylproline), suggesting their degradation or transformation by functional microorganisms during fermentation. Conversely, 15 metabolites were significantly upregulated, such as tyrosol and indole-3-lactic acid, which may serve as key contributors to the enhanced bioactivity of the fermented extract. These results demonstrate that microbial fermentation effectively remodels the phenolic composition and metabolic pathways of almond hull.

4. Discussion

This study systematically validated the feasibility of almond hull, an underutilized agricultural by-product, as a fermentation substrate for the co-cultivation of P8 and V9, and optimized the fermentation parameters via single-factor experiments and Box–Behnken response surface methodology. The obtained data not only clarified the growth adaptability of the two probiotic strains in the almond hull matrix but also revealed the dynamic transformation of bioactive components and the enhancement of antioxidant capacity during fermentation, providing a solid experimental basis for the high-value utilization of almond processing by-products and the development of plant-based synbiotic/symbiotic products. The in-depth analysis of the experimental data and exploration of practical application prospects are presented as follows, combined with the current research results and the development trend of the food and agricultural industries.

In this study, temperature was identified as a key factor regulating the co-proliferation of the two probiotic strains. The proliferation multiple reached the maximum at 28 °C, which was significantly higher than that in other temperature groups. This result was consistent with the microaerophilic growth characteristics of the strains, as this temperature maintained the activities of key metabolic enzymes such as cellulase and glycosidase, facilitating the utilization of complex nutrients in the almond hull. The highest proliferation multiple ((251.5 ± 5.99) times) was observed at a strain ratio of 1:1, reflecting the synergistic metabolic balance between the two strains. The inoculation amount showed a significantly negative correlation with the proliferation multiple, and the optimal proliferation was achieved at 0.1% inoculation, which helps reduce industrial production costs. A highly reliable quadratic polynomial regression model was obtained by Box–Behnken response surface optimization (R² = 0.9776, adjusted R² = 0.9487, p < 0.0001). The optimal fermentation conditions were P8:V9 = 1:1, an inoculation amount of 0.1%, 28 °C, and a fermentation time of 66 h, under which the actual proliferation multiple was 320 times, close to the predicted value. Fermentation caused significant changes in the physicochemical properties of the almond hull hydrolysate: the pH decreased from 6.0 to 4.8, and the viable probiotic count increased from 5.01 × 10⁵ CFU/mL to 3.36 × 10⁷ CFU/mL. All indexes met the relevant standards for probiotic foods, laying a foundation for product development. The content of free flavonoids increased by 24.72%, while the total polyphenols slightly decreased by 6.16%. The mechanism was that glycosidases and esterases secreted by probiotics converted bound flavonoids into free flavonoids with high bioavailability; simple polyphenols were degraded or utilized, whereas complex flavonoids were enriched. Soluble protein content increased significantly, while reducing sugar content showed no significant change, indicating a balanced nutrient metabolism in the fermentation system that could continuously supply nutrients for probiotic growth. The DPPH radical scavenging capacity and FRAP value of the fermented product were significantly improved, mainly due to the synergistic effect of free flavonoids and small-molecular peptides (flavonoids scavenge free radicals, while peptides chelate metal ions and inhibit lipid peroxidation).

In conclusion, this study confirmed that almond hull is a suitable plant-based probiotic fermentation substrate with rich nutrients and natural prebiotic activity. The optimized fermentation process can realize the efficient proliferation of probiotics and the directional transformation of bioactive components, and the fermented product has excellent antioxidant capacity and commercial development potential. The research results not only provide a new technical path for the high-value utilization of almond hull but also open up a new direction for the development of plant-based synbiotic products and have important theoretical significance and practical application value for promoting the sustainable development of the agricultural by-product utilization industry and the functional food industry. With the in-depth development of follow-up research and the optimization of industrial technology, the fermented almond hull product is expected to be industrialized and marketed in the near future and create significant economic, social and environmental benefits.

5. Conclusions

In this study, a compound fermentation system with P8 and V9 was constructed with almond hull as a raw material. Through a single-factor experiment and response surface optimization, the optimal process parameters for probiotic proliferation was determined to be P8:V9 = 1, an inoculation amount of 0.1%, and fermentation at 28 °C for 66 h. It was found that natural prebiotics such as dietary fiber and polyphenols in almond hull cooperated with probiotics, which not only realized the efficient proliferation of probiotics, but also drove the degradation of matrix-bound polyphenols into free flavonoids, proteolysis into small-molecular peptides and free amino acids, and realized the dual value of nutritional morphology optimization and active ingredient enrichment. This study provides a path for the high-value utilization of agricultural by-products such as almond hull, which is in line with the concept of circular agriculture. The natural compatibility between the plant-based matrix and probiotics was confirmed, which provided a new paradigm for the development of non-dairy-based functional foods suitable for special populations such as lactose intolerance. The mechanism of probiotic metabolism–matrix component transformation revealed by also provides theoretical support for the precise innovation of functional foods.

6. Limitations

This study has several limitations. Firstly, the fermentation substrate was limited to almond hulls from a specific producing area, harvest stage, and pretreatment condition; thus, the results cannot be directly extended to other sources of almond hulls, as their nutrient contents vary with variety, origin, and storage conditions. Secondly, only specific probiotic strains were used, and the conclusions are not fully applicable to other probiotics due to differences in their carbon source utilization and stress resistance. Thirdly, all experiments were conducted under controlled in vitro laboratory conditions without verification in animal models or human clinical trials, failing to fully reflect the actual colonization, metabolism, and probiotic effects in the human intestinal tract. Fourthly, this study only focused on probiotic growth and adaptability, without in-depth analysis of the transformation pathway of functional components (e.g., polyphenols, polysaccharides) in almond hulls during fermentation or the regulatory effect of fermented products on the overall intestinal flora.