Potential of Strawberry Leaves with Biostimulants: Repository of Metabolites and Bioethanol Production

Abstract

To understand the potential uses of strawberry leaves and their application in the industrial sector, it is important to study their metabolic and lignocellulosic profile. The objective of the study was to characterize the metabolic profile of strawberry leaves grown with biostimulants and to verify whether this by-product can be utilized as a matrix for bioethanol production. The eight treatments studied were the absence and presence of biostimulants, as follows: arbuscular mycorrhizal fungi (AMF), Ascophyllum nodosum (AN), Trichoderma harzianum (TH), AMF + AN, AMF + TH, AN + TH, and AMF + AN + TH. Treatments were applied monthly, either manually (AMF) or with a micropipette (AN and TH), from June 2023 to March 2024. Hydrogen nuclear magnetic resonance (¹H NMR) analysis identified 11 metabolites in the leaves, including 5 amino acids, 4 organic acids, and 2 carbohydrates, which may be of industrial interest. The leaves were used for bioethanol production through saccharification with cellulolytic enzymes, followed by fermentation with Saccharomyces cerevisiae. Enzymatic hydrolysis resulted in a total reducing sugar content of 21.12 g·L⁻¹. Alcoholic fermentation yielded 8.97 g·L⁻¹ of bioethanol in 12 h, which corresponds to 45.48 L·t⁻¹ of dry leaves. In conclusion, regardless of biostimulation, strawberry leaves are an important repository of metabolites that can be used as raw material in different processes. Additionally, the leaves are suitable as raw material for bioethanol production in a biorefinery concept.

1. Introduction

The establishment of strawberry plants (Fragaria X ananassa Duch.) in a soilless cultivation system is a strategy to improve working conditions during the management of this horticultural crop [1] and also allows fruit yield and quality enhancement, optimizing the phytosanitary maintenance of plants in the cultivation agroecosystem [2]. Despite these benefits of the soilless system, one of the challenges of growing strawberries in substrate is reducing the use of chemical inputs, such as fertilizers and biocides, in response to the production system sustainability. One strategy to overcome these obstacles, combined with the construction of the cultivation substrate microbiota, is the use of biostimulants [3], such as arbuscular mycorrhizal fungi (AMF), Ascophyllum nodosum (L.) Le Jolis, and Trichoderma harzianum Rifai [4]. The use of biostimulants in strawberry cultivation improves nutrient acquisition and plant resistance to adverse conditions [5] due to increased nutrient absorption capacity from greater solubility, root morphology, and AMF colonization of the roots. This allows for intensified production and fruit quality with less use of chemical inputs during the production cycle [6].

As a way of optimizing cultivation and reducing production costs, strawberry growers maintain the same plants for more than one production cycle through renewal pruning [7]. Among the pruning intensities performed is drastic pruning, which consists of the total removal of leaves, generating a significant amount of unused waste in commercial strawberry crops [2,8]. The leaves can be used for new applications, adding value to this waste, according to circular bioeconomy principles. Compounds such as bioactives and biofuels can be generated, requiring prior knowledge of the chemical composition and processes for using the waste [9,10].

The detailed characterization of strawberry leaves’ metabolic profile makes it possible to identify high value-added compounds, such as flavonoids, terpenes, and hydroxycinnamic acid derivatives. It also provides data on the variability of these metabolites as a function of different treatments during cultivation, which allows processing strategies to be directed towards obtaining possible components that can be used as functional ingredients for use in the food, cosmetic, and pharmaceutical industries [11]. At the same time, the quantification of structural carbohydrates (cellulose, hemicellulose) and lignin present in leaf tissue provides information for assessing the viability of biotechnological routes aimed at bioethanol production. The search for alternatives to fossil fuels is essential to reduce global dependence on non-renewable resources and mitigate the associated environmental impacts [12].

Bioethanol derived from corn (Zea mays L.) (starch) and sugarcane (Saccharum officinarum L.) (sucrose) stands out as the most widely used renewable fuel [13]. However, the production of bioethanol from these raw materials raises environmental, ethical, and economic concerns, which includes competition for resources with food production, such as water and arable land, and the potential to impact food prices and availability, compromising food security [14]. Furthermore, in Brazil, the sugarcane harvest is limited to certain periods, and once cut, the cane begins to ferment rapidly, making storage impossible and limiting the availability of raw material throughout the year [15]. In this context, lignocellulosic biomass emerges as a promising renewable source for bioethanol production [16], such as the use of strawberry leaves. Lignocellulosic materials are renewable, low-cost, and widely available in agricultural waste. Among the main residues used for this purpose are rice straw (Oryza sativa L.), wheat straw (Triticum aestivum L.), corn straw, and sugarcane bagasse [17]. However, to our knowledge, no studies have been conducted on the use of strawberry leaves for bioethanol production.

Thus, the objective of the study was to characterize the metabolic profile of strawberry leaves grown with biostimulants in order to identify possible compounds of industrial interest and verify whether this by-product is suitable as a matrix for bioethanol production.

2. Materials and Methods

2.1. Strawberry Cultivation

Bare-root strawberry daughter plants, San Andreas cultivar, constituted the plant material for the study. We chose ‘San Andreas’ because it is the most widely used cultivar by growers in the Brazilian subtropics [1]. The work was carried out in the municipality of Passo Fundo (28°15′41″ S; 52°24′45″ W), Rio Grande do Sul state, Brazil, from June (winter) 2023 to March (autumn) 2024 in a greenhouse (430 m²) with a semicircular roof, covered with low-density polyethylene film with an anti-ultraviolet additive.

The eight treatments studied were the absence (control) and presence of biostimulants, represented by arbuscular mycorrhizal fungi (AMF), Ascophyllum nodosum (AN), Trichoderma harzianum (TH), AMF + AN, AMF + TH, AN + TH, and AMF + AN + TH. The experiment was designed in randomized blocks, with three replicates.

The AMF-based biostimulant was a multispecific on-farm inoculant [18], composed of eight species [19]: Acaulospora koskei Blaszk., Acaulospora rehmii Sieverding & Toro, Claroideoglomus aff. luteum, Claroideoglomus claroideum (N.C. Schenck & G.S. Sm.) C. Walker & A. Schüßler, Claroideoglomus etunicatum (W.N. Becker & Gerd.) C. Walker & A. Schüßler, Funneliformis aff. mosseae, Glomus aff. versiforme, and Glomus sp. (caesaris like). The biostimulant based on A. nodosum extract was the commercial product Acadian^® (Koppert^®, Piracicaba, São Paulo, Brazil). The biostimulant based on T. harzianum was the commercial product Trichodermil^® (Koppert^®, Piracicaba, São Paulo, Brazil), consisting of T. harzianum (CEPA ESALQ 1306).

The daughter plants were transplanted in June 2023 into containers measuring 1 m long × 0.3 m wide, filled with Dallemole^® substrate (Vacaria, Rio Grande do Sul, Brazil). The plants were spaced 0.17 m apart, with one row of plants per container. Treatments were applied monthly, either manually (AMF) or with a micropipette (AN and TH), from June 2023 to March 2024. For the treatments inoculated with AMF, 5 g of on-farm inoculant were added monthly to the planting bed (June) at the time of transplanting and then around the crown of the plants. For the treatments that received A. nodosum and T. harzianum, a solution of 2.5 mL·L⁻¹ (Acadian^®) and 1.5 mL·L⁻¹ (Trichodermil^®), respectively, was prepared monthly. Subsequently, 10 mL of the Acadian^® solution and 10 mL of the Trichodermil^® solution were applied to each plant at its crown base, aiming for it to drain into the substrate, with the aid of a micropipette. The dosages for these three biostimulants were based on previous studies [4].

The irrigation used in the experiment was localized (drip tapes) in an automated system, with a flow rate of 1.41 L·h⁻¹ per dripper. The nutrient solutions supplied to the plants on a weekly basis were made with a 50% reduction in phosphorus supply [20]. The nutrient solution was composed of calcium nitrate, potassium monobasic phosphate, potassium nitrate, magnesium sulfate, urea, and micronutrients (boron, copper, iron, manganese, molybdenum, and zinc).

At the end of the first growing cycle, in March 2024, after pruning the plants [2], the removed leaves, separated by treatment, were used to characterize their metabolic profile. The free reducing sugars content present in the leaves from each treatment was also evaluated in order to select those with the highest potential for bioethanol production.

2.2. Metabolic Composition of Leaves by Nuclear Magnetic Resonance of Hydrogen

The procedure for extracting metabolites from strawberry leaves in the eight treatments (control, AMF, AN, TH, AMF + AN, AMF + TH, AN + TH, and AMF + AN + TH) followed the method described by Kim et al. (2010) [21], without modifications. Each sample was analyzed in triplicate. Briefly, a total of 50 mg of dried and ground leaves was extracted with a combination of 0.75 mL of a 90 mmol·L⁻¹ phosphate buffer (KH₂PO₄, pH 6.00) in deuterated water (D₂O) containing 0.1 mg·mL⁻¹ of sodium 3-trimethylsilyl-2,2,3,3-propionate-d4 (TSP-d₄) and 0.75 mL of methanol-d₄. After combining the solvents, the samples were vortexed for 1 min, placed in an ultrasonic bath for 20 min, and centrifuged at 17,000× g. Next, 800 µL of the supernatant was transferred to 5 mm diameter nuclear magnetic resonance (NMR) tubes.

Hydrogen nuclear magnetic resonance (¹H NMR) spectra were recorded on a 600 MHz spectrometer (Avance Neo 600 MHz, Fällanden, Switzerland). The spectra were acquired at 300 K, with a spectral window of 16 ppm, 32k points, HDO signal presaturation, 128 promediation, and acquisition (AQ) and recovery (d1) times of 3.2 s and 5 s, respectively. All spectra were obtained using the zgcppr pulse sequence and calibrated by the TSP-d₄ signal at 0.00 ppm. The phases and baselines were automatically corrected using Chenomx NMR Suite 10.0 software (Chenomx Inc., Edmonton, AB, Canada). In this same software, the compounds were identified by comparison with the spectra of pure substances present in the database. In addition, the TSP-d₄ signal area was used as a reference for metabolite quantification.

Metabolite concentrations were exported as a CSV file and imported into MetaboAnalyst 6.0. Data normalization was performed by summation and auto-scaling. Next, a one-way parametric analysis of variance (ANOVA) test was applied. To control the expected proportion of erroneously rejected null hypotheses, the false discovery rate (FDR) was used, considering metabolites with a p-value < 0.05 as significant. In addition, Fisher’s LSD post-test (p < 0.05) was performed. Hierarchical cluster analysis (HCA) associated with the heat map was obtained using Ward’s method, with normalized Euclidean distances as a measure of similarity.

2.3. Bioethanol Production from Strawberry Leaves

The leaves used as biomass for bioethanol production were dried in an air circulation oven at 60 °C for 24 h. After drying, the leaves were ground in a Willey mill with a 1 mm sieve and stored at −20 °C until use. Each sample was analyzed in triplicate.

2.3.1. Composition of Strawberry Leaves

Bioethanol production depends on the presence of free reducing sugars or material that can be hydrolyzed for its production. To determine the amount of free reducing sugar (RS) already present in the leaves, 10% (w/v) of the leaves from each treatment were added to a citrate-phosphate buffer medium (200 mmol·L⁻¹, pH 4.5). The samples were shaken manually for 1 min at room temperature and filtered with a qualitative filter. The proteins present in the supernatant were precipitated using the Carrez method [22]. Subsequently, the RS was analyzed using the 3,5-dinitrosalicylic acid (DNS) method [23]. This procedure was performed to select leaves from the treatment with the highest RS content to proceed to the next steps.

The leaves from the treatment with the highest RS content were analyzed for moisture, protein, lipids, ash, crude fiber, neutral detergent fiber (NDF), acid detergent fiber (ADF), and lignin, according to Caputi (2017) [24]. The carbohydrate content (%) was determined by the difference method according to Equation (1). Based on the NDF, ADF, and lignin analyses, the cellulose and hemicellulose contents were determined using the Van Soest and Wine (1967) [25] method, with the cellulose content being the difference between ADF and lignin content, and the hemicellulose content being the difference between NDF and ADF.

$$\mathrm{CC}=100-(\mathrm{M}+\mathrm{P}+\mathrm{L}+\mathrm{A})$$

(1)

where CC = carbohydrate content (%); M = moisture content (%); P = protein content (%): L = lipid content (%); A = ash content (%).

2.3.2. Evaluation of Enzymes for the Saccharification of Strawberry Leaves

Based on the selection of leaves with the highest free RS content, different enzymes were evaluated for the saccharification of strawberry leaves. The enzymes tested consisted of an enzyme mixture containing: (1) cellulases, β-glucosidases, and hemicellulase (SAE 0020, Novonesis); (2) cellulase (produced by Trichoderma reesei, C2730, Sigma-Aldrich); (3) hemicellulase (produced by Aspergillus niger, H2125, Sigma-Aldrich); (4) mixtures of enzymes SAE 0020 + C2730, and C2730 + H2125; (5) control treatment (without enzymes). Enzymatic hydrolysis was evaluated for 2 h in a shaker at 50 °C and 150 rpm, using 10% (w/v) leaves in citrate–phosphate buffer (200 mmol·L⁻¹, pH 4.5) and 0.5% (v/v) enzyme. After hydrolysis, the solids were separated by filtration. The proteins present in the supernatant were precipitated using the Carrez method [22] and RS released was analyzed using the DNS method [23].

2.3.3. Enzymatic Hydrolysis

After identifying the most efficient enzyme (SAE 0020), enzymatic hydrolysis was performed at 50 °C and 150 rpm for 48 h in a jacketed reactor with mechanical agitation and a useful volume of 1.5 L. Ten percent (w/v) of leaves in citrate–phosphate buffer (200 mmol·L⁻¹, pH 4.5) and 0.5% (v/v) of enzymes were used. Periodic sampling was performed at 0, 6, 12, 24, and 48 h to determine the RS released by the DNS method [23]. After hydrolysis, the supernatant was separated by centrifugation for 10 min at 3000 rpm (Eppendorf 5810 Centrifuge), followed by filtration. The supernatant was autoclaved at 121 °C and 1 atm for 20 min for sterilization and proceeded to the alcoholic fermentation stage.

Equation (2) was used to calculate the efficiency of enzymatic hydrolysis:

$$\mathrm{HE} (\%) = {\displaystyle \frac{{\mathrm{RS}}_{\mathrm{f}\mathrm{t}} -{ \mathrm{RS}}_{\mathrm{t}0}}{\mathrm{CC}}}$$

(2)

where HE = hydrolysis efficiency; RS_ft = reducing sugars at final time (g RS·100 g⁻¹); RS_t0 = reducing sugars at initial time (g RS·100 g⁻¹); CC = total carbohydrates per 100 g of leaves.

2.3.4. Fermentation and Bioethanol Production

Bioethanol production from strawberry leaves was carried out by static alcoholic fermentation in Erlenmeyer flasks (1 L) containing 600 mL of the supernatant resulting from centrifugation of the raw material hydrolysate. Ten percent (v/v) of Saccharomyces cerevisiae inoculum previously cultured in yeast extract peptone dextrose (YPD) medium for 24 h at 30 °C and 150 rpm was added.

Samples were periodically obtained (0, 6, 12, and 24 h) from the fermentation medium to evaluate the RS content by the DNS method [23] and bioethanol by the spectrophotometric method proposed by Salik and Povh (1993) [26]. This method is based on the absorbance of the samples (600 nm) subjected to distillation and reaction with potassium dichromate at a temperature of 60 °C for 30 min. The samples were taken in triplicate.

Equation (3) was used to calculate the efficiency of bioethanol production in relation to the initial amount of RS in fermentation.

$$\eta (\%)={\displaystyle \frac{\Delta \mathrm{E}}{0.511 \times ({\mathrm{s}}_0- \mathrm{s})}} \times 100$$

(3)

where η = bioethanol production efficiency; ΔE = variation in bioethanol concentration (g·L⁻¹); S₀ = RS concentration at the start of fermentation (g·L⁻¹); S = RS concentration at the end of fermentation (g·L⁻¹).

Equation (4) was used to calculate bioethanol productivity (g/L⁻¹·h⁻¹):

$$\mathrm{Bioethanol} = {\displaystyle \frac{\Delta \mathrm{E}}{\Delta \mathrm{t}}}$$

(4)

where ΔE = variation in bioethanol concentration (g·L⁻¹); Δt = variation in fermentation time (h).

3. Results

3.1. Metabolic Composition of Leaves by Nuclear Magnetic Resonance of Hydrogen

The representative spectra of the strawberry leaf samples from each treatment studied are shown in Figure 1. Using Chenomx software, eleven metabolites were identified, including five amino acids (alanine, asparagine, isoleucine, valine, and tyrosine), four organic acids (4-aminobutyric acid, citric acid, malic acid, and succinic acid), and two carbohydrates (glucose and sucrose). When qualitatively evaluating the ¹H NMR spectra (Figure 1), no new signals directly related to the treatments were found. Thus, except for variations in signal intensity, no apparent differences were observed between the samples.

The comparison between the eight treatments, in relation to metabolite concentrations, was performed using ANOVA, which indicated eight metabolites with significant differences (p < 0.05) (Figure 2). Leaves from plants grown with AMF had the highest concentrations of alanine (Figure 2A) and glucose (Figure 2D). However, co-inoculation of AMF and T. harzianum improved the levels of asparagine (Figure 2B) and valine (Figure 2G). Furthermore, the combination of AMF + A. nodosum increased the concentration of succinic acid (Figure 2E) and sucrose (Figure 2F). Finally, plants grown with A. nodosum and T. harzianum, either alone or in combination, produced leaves with higher levels of citric acid (Figure 2C) and tyrosine (Figure 2H).

To facilitate the visualization of these differences, an HCA dendrogram associated with a heat map was generated using the concentrations of all quantified metabolites (Figure 3). Distinct patterns in metabolite concentration were observed in strawberry leaves subjected to biostimulant treatments. In the first group (AMF + AN + TH and AN + TH), there was an increase in the concentration of metabolites such as citric acid, valine, and asparagine (Figure 3). The isolated treatments with TH and AN form another group, presenting similar metabolic profiles (Figure 3). The AMF + AN treatment, in the third group, revealed a specific metabolic profile, with significant variations in metabolites such as tyrosine, succinic acid, and sucrose (Figure 3). The fourth group, formed by AMF + TH and control, suggested that this combination causes a more subtle metabolic impact, close to that of untreated plants (Figure 3). Finally, the treatment with AMF, isolated in the fifth group, presented a distinct pattern, with an increase in malic acid and alanine levels (Figure 3).

3.2. Bioethanol Production from Strawberry Leaves

The leaves presented different RS contents depending on the biostimulants applied during strawberry cultivation (Figure 4). Plants cultivated with A. nodosum produced leaves with RS contents 21% higher than those of plants cultivated only with AMF (Figure 4). Therefore, leaf samples from the A. nodosum treatment were used for the subsequent steps (bromatological analysis, enzymatic hydrolysis, fermentation, and bioethanol production).

The bromatological analysis indicated that the leaves of strawberry plants cultivated with A. nodosum were mainly composed of fibers and carbohydrates (

Table 1. Proximate composition (wet basis) of lignocellulosic biomass from strawberry leaves.

Bromatological Analyses 1
Humidity	Protein	Lipids	Ash	Crude Fiber	NDF	ADF	Total Carbohydrates
%
4.14	8.99	3.86	12.87	24.79	26.4	25.91	70.14

¹ NDF: neutral detergent fiber; ADF: Acid detergent fiber.

). Based on the NDF, ADF, and lignin (0.15%) values, the cellulose (ADF–lignin) and hemicellulose (NDF–ADF) contents were estimated at 25.76% and 0.49%, respectively. In relation to hemicellulose and lignin, cellulose was predominant in the composition of the lignocellulosic material of strawberry leaves.

The enzymes SAE 0020 and SAE0020 + C2730 showed the highest RS values after enzymatic hydrolysis (Figure 5). As the values of these treatments (SAE 0020 and SAE0020 + C2730) were equivalent, the effect of hydrolysis was attributed to the SAE 0020 enzyme, which was selected for larger-scale enzymatic hydrolysis.

In 24 h, there was maximum RS release by enzymatic hydrolysis (Figure 6). The efficiency of enzymatic hydrolysis was 17.15% in 24 h, considering 70% TOC in strawberry leaves.

After enzymatic hydrolysis, alcoholic fermentation by S. cerevisiae was performed. After 12 h of fermentation, an ethanol concentration of 8.97 g·L⁻¹ was reached, or a productivity of 0.74 g/L⁻¹·h⁻¹, with 17.17 g·L⁻¹ of RS consumed (Figure 7). After 24 h of fermentation, ethanol production increased by only 0.27 g·L⁻¹ (reaching 9.24 g·L⁻¹), with productivity falling to 0.38 g/L⁻¹·h⁻¹. Thus, the fermentation time of 12 h was sufficient for ethanol production efficiency to reach 83% of the theoretical yield when calculated from the initial RS concentration (21.12 g·L⁻¹), or 100% conversion when based on the amount of reducing sugar actually consumed (17.17 g·L⁻¹).

4. Discussion

Here, it was shown that strawberry leaves grown with biostimulants in a soilless system presented metabolites of phytopharmaceutical interest (Figure 2) and that this by-product can be used for bioethanol production, mainly from the enrichment of the culture substrate with A. nodosum (Figure 4). A. nodosum was the source of the first liquid extract marketed from seaweed [27], and its use in agriculture is related to its biodegradable, non-toxic, and ecologically sustainable properties [28]. Its use as a biostimulant in plant cultivation activates several genes involved in the biosynthesis of soluble carbohydrates (GOLS2 and GOLS3), polysaccharide degradation (SEX1, SEX4/DSP4, and MUR4), and inhibition of sucrose degradation (At1g12240) [29]. Thus, plants grown with this macroalgae produced leaves with higher reducing sugar contents (Figure 4), which represents a greater potential for bioethanol production, independent of biomass hydrolysis, which can be performed to increase free sugar contents.

Using a mixture of cellulases, β-glucosidases, and hemicellulases (SAE 0020), an enzymatic hydrolysis efficiency of ~17% was achieved for strawberry leaves (Figure 6). Although other methods exist for hydrolysis, enzymatic hydrolysis is a suitable process because it requires less energy and mild environmental conditions, with fewer fermentation inhibitors generated during the process [30]. Different enzymes can be used to optimize hydrolysis efficiency, as the complete transformation of lignocellulosic biomass into reducing sugars is complex [31]. The absence of pretreatment to break down or modify this matrix may have contributed to maintaining physical and chemical barriers to enzymatic action, resulting in low hydrolysis efficiency. In addition, the presence of secondary compounds abundant in leaves, such as phenolics and hydroxycinnamic acid derivatives [32,33], may have partially or competitively inhibited enzyme action, affecting the efficiency of the process.

Optimization of pretreatment and hydrolysis processes is necessary to leverage second-generation bioethanol production. Strawberry leaves have an advantage over other lignocellulosic materials due to their low lignin content, which facilitates the conversion of cellulose and hemicellulose into fermentable sugars [34]. Considering circular bioeconomy, the prospecting of this study in its continuity should consider the possibility of performing solid-state fermentation of the leaves in order to carry out the homemade production of enzymes in the material itself for hydrolysis. The product of solid-state fermentation can be used directly for subsequent stages of saccharification and fermentation in a consolidated bioprocess [35]. The commercial enzymes used are not specific for the hydrolysis of materials but rather developed for analytical hydrolysis processes.

In alcoholic fermentation with S. cerevisiae, glucose is converted into pyruvate via glycolysis and then transformed into ethanol via alcoholic fermentation [36]. Here, S. cerevisiae converted most of the reducing sugars into bioethanol, with 100% efficiency in 12 h (Figure 7). During this time, the highest productivity (0.74 g/L⁻¹·h⁻¹) was observed, with a production of 8.97 g·L⁻¹ of bioethanol, which corresponds to 45.48 L of ethanol per ton of dry leaves.

Comparison with previous studies shows that the yield obtained is competitive, even considering differences in raw material and substrate concentration. For example, Bender et al. (2025) [37], when evaluating a medium with 5% (w/v) of potato (Solanum tuberosum L.) and sweet potato [Ipomoea batatas (L.) Lam.], with and without supplementation of Spirulina platensis microalgae, obtained bioethanol production of 10.13 g·L⁻¹ and efficiency of 71.37% (without supplementation of S. platensis) and 13.47 g·L⁻¹ and efficiency of 85.54% (with supplementation of S. platensis). Simon et al. (2025) [38] obtained bioethanol production of 7.29 g·L⁻¹ and 79.21% efficiency using a medium with 10% (w/v) of S. platensis. No studies were found in the literature on the use of strawberry leaves for ethanol production. Therefore, comparison with other raw materials already evaluated demonstrates the potential of this residue as a source of carbohydrates for alcoholic fermentation.

Strawberry leaves can be targeted for biorefinery applications, especially for the extraction of high-value bioproducts. After extraction, the process residue, which is rich in sugars, can be subjected to pretreatments to further increase the concentration of sugars available for fermentation processes. This treatment is favored by the low concentration of lignin present in the leaves and reduces the challenges associated with processing [39]. Adeyemi et al. (2019) [40] produced bioethanol with other feedstock sources, achieving 36.26 g·L⁻¹ with rice straw and 37.27 g·L⁻¹ with wheat straw using alkaline pretreatments.

The volume of strawberry leaves produced in a greenhouse is directly related to the size and efficiency of space utilization. In a 500 m² greenhouse, with 70% utilization of useful space, after plant pruning [2], it is possible to obtain an average production of 917.2 kg of fresh leaves, of which 22.7% corresponds to dry mass, resulting in 208.5 kg of dry leaves ready for use. Considering a production of 8.97 g·L⁻¹ of bioethanol—45.48 L of bioethanol per ton of dry leaves (Figure 7)—this production space would generate 9.48 L of bioethanol in a 500 m² greenhouse. Although this value is low when compared to other biomasses such as corn stover (362–456 L·t⁻¹), wheat straw (406 L·t⁻¹), and rice straw (416 L·t⁻¹) [41,42], improvements in enzymatic hydrolysis process and pretreatments could increase the yield of bioethanol from strawberry leaves.

In addition to their value for bioethanol production, strawberry leaves can be used for the extraction of biomolecules, which can be used in the pharmaceutical and agribusiness industries [8]. The substances already detected in strawberry leaves, such as quercetin, kaempferol, p-coumaric acid, and p-tyrosol, have anti-inflammatory, antioxidant, and anti-hyperglycemic actions [43]. Biostimulants play a crucial role in modulating and optimizing the lignocellulosic and metabolic profiles of plants. They can, for example, enhance the concentration of reducing sugars for greater efficiency in bioethanol production or to enrich the feedstock with specific amino acids or organic acids for other industrial applications [44]. For example, the use of AMF alone stimulates the production of alanine and glucose in leaves, while the co-inoculation of AMF + T. harzianum improves asparagine and valine contents, and the combination of AMF + A. nodosum increases the concentration of succinic acid and sucrose (Figure 2). The beneficial effects of biostimulants on the metabolic profile of strawberries has already been reported in another research using AMF [4], T. harzianum [45], and A. nodosum [46]. The synthesis of metabolites in plants grown with biostimulants can occur through increases in the acquisition of water and nutrients, in photosynthetic capacity, and through the enhancement of intermediate compounds and precursors [47]. In addition, the production of metabolites can be affected by phytohormones [48] and by signaling molecules [49].

In commercial strawberry production, millions of tons of leaves are discarded annually. Considering the concept of circular bioeconomy, there is the possibility of using this cultivation by-product in biorefineries for the processing of lignocellulosic material [50]. The economic viability of a lignocellulosic biorefinery depends on the efficient use of lignocellulosic biomass components and their conversion into bioethanol and other bioproducts [51]. The ability to modulate the metabolic profile of leaves using biostimulants represents an ecological approach to the biorefinery concept. An ideal biorefinery seeks not only to process waste but also to optimize the feedstock for the desired products. In this context, strawberry leaves can be efficiently processed in a cascade model to maximize value generation. Initially, leaf biomass can undergo processes to extract high-value-added metabolites [52], such as amino acids, organic acids, and carbohydrates, which can be modulated by biostimulants (Figure 2). Amino acids can be used in supplements for muscle recovery [53] and in the treatment of neurological disorders [54]. Among identified organic acids, 4-aminobutyric acid is widely used in the treatment of anxiety, stress, and hypertension [55], malic acid has applications in the treatment of fibromyalgia [56], and succinic acid is used in the production of biodegradable plastics [57]. Regarding carbohydrates detected in leaves, glucose and sucrose play crucial roles in biotechnology, the pharmaceutical industry, and agribusiness [58].

After extracting these higher-value compounds, the remaining lignocellulosic residue can be used for bioethanol production. To convert the structural polysaccharides into fermentable sugars, this residue must undergo pretreatment and enzymatic hydrolysis, aiming to increase the availability of RS in the medium [59]. In addition to bioethanol production, soluble sugars can be used to obtain other bioproducts, such as the production of cellulolytic enzymes and in lactic acid production processes to produce polylactic acid; this also contextualizes biorefineries. Thus, a crucial and prominent advantage of using strawberry leaves for this purpose is the low presence of lignin in their lignocellulosic matrix. Here, we demonstrate that lignin was estimated at only 0.15% (Table 1). This characteristic facilitates pretreatment and hydrolysis steps, as lignin is one of the main inhibitors of enzymatic action in sugar release [60]. Its low concentration in strawberry leaves simplifies the process, making the conversion of cellulose and hemicellulose into fermentable sugars more efficient and potentially less costly compared to other lignocellulosic biomasses [39]. This multi-product approach aligns with the principles of circular bioeconomy, transforming a single waste product into multiple valuable resources.

The integration of the circular bioeconomy concept into commercial strawberry production offers a promising prospect for optimizing cultivation and increasing the sustainability of the soilless system. The reuse of discarded leaves, an abundant by-product of cultivation, in lignocellulosic biorefineries allows the transformation of this waste into bioethanol and high-value-added bioproducts, which contributes to reducing waste and generating new sources of revenue. This green approach not only contributes to the economic and environmental efficiency of the production chain but also strengthens more sustainable agricultural practices by aligning waste management with renewable and low-impact solutions for the environment [61].

5. Conclusions

This is the first study to highlight the potential use of strawberry leaves for bioethanol production. The hydrolysis of strawberry leaves with cellulases, β-glucosidases, and hemicellulases results in an efficiency of only 17%. However, even with low hydrolysis efficiency, using strawberry leaves cultivated with Ascophyllum nodosum, it is possible to produce 8.97 g·L⁻¹ (45.48 L·t⁻¹ dry leaf) of bioethanol in 12 h, with high efficiency in converting reducing sugars into bioethanol. In addition, regardless of biostimulation, strawberry leaves are an important reservoir of metabolites, especially amino acids, organic acids, and carbohydrates. Future studies should focus on evaluating pretreatments and optimizing hydrolysis to increase the RS content available for bioethanol production. These findings contribute to the valorization of strawberry leaves as an important by-product of soilless cultivation systems.