Investigation of fermentable sugar production through microwave-assisted alkali pretreatment and subsequent acid hydrolysis on Prosopis juliflora biomass

1 Introduction

The continuous rise in global energy demand, coupled with the depletion of fossil fuel reserves and increasing fuel prices, has intensified the search for sustainable and renewable energy resources. These challenges have driven significant research efforts toward the development of biomass-based biofuels as environmentally friendly alternatives to conventional fossil fuels (Tang et al., 2022). Based on the source of biomass and its fermentable sugars production, the biofuel can be classified into various generations. There are four generations of biofuels, first-generation biofuels are primarily made from edible biomass. Although first-generation fuels are environmentally friendly and obtained from sugar cane, corn cob, wheat, etc., they are quite expensive due to the competition with food crops. Hence, the problem arises when there is insufficient food to feed people (Satish and Nagesha, 2018). In second-generation biofuel, they valorize non-edible organic wastes, such as lignocellulosic biomass like agricultural residues and forestry wastes. This overcomes the drawbacks of first-generation biofuels by using waste biomass for biofuel production. So, it became more sustainable, environmentally feasible, easily processable and also ideal for bioethanol production (Kang and Tan, 2016). The fuels produced from lignocellulosic biomass (LCB) generate fewer environmental pollutants during the production process and usage, when compared to commercial fuels. The LCB differs in configuration depending on the biomass nature and the environmental factors (climate, soil, and locality). The major components of LCB are cellulose, hemicellulose and lignin, which are the starting materials for fermentable sugar production (Abraham et al., 2020). Third-generation biofuels are being developed primarily from aquatic flora such as algae, which are more expensive and less stable. Fourth-generation biofuels are produced from genetically engineered microorganisms or genetically modified energy crops for bioethanol production (Naik et al., 2010).

Cellulose is a polysaccharide, made up of glucose monomers linked through β-1, 4 D-glycosidic bonds with glucopyranose linear chain bond units, with the formula of (C₆H₁₀O₅)_n, where n is the repeating monomeric β-D-glucopyranose number that varies depending on the other cellulose source (Ramadoss et al., 2016). The cellulose is more stable than lignin and hemicellulose due to its crystallinity and it requires harsh chemicals and processes to break the strong intermolecular bonds to obtain the glucose monomers as fermentable sugars. The cellulose has two regions: amorphous regions, which degrade in strong acid or alkali, and crystalline regions with some weak regions, which are soluble in strong acid and alkali at high temperature and pressure (Gupta et al., 2009). Hemicellulose is a complex heteropolysaccharide composed of various sugar molecules like arabinose, xylose, glucose, mannose, etc. It is highly branched and strongly interacts with cellulose within the cell wall matrix and increases the structural strength of the cell wall (Rao et al., 2023). Hemicellulose is a versatile, renewable resource of various bio-based materials which have various uses (Sivarathnakumar et al., 2019). Lignin is a complex organic polymer that gives structural support to plants and it is one of the major components of plant cell walls (together with cellulose and hemicellulose). Lignin in plants makes them stronger and more rigid, which helps them stay upright and supports the tissues (Naseeruddin et al., 2013). In general, lignin is not composed of sugar molecules like cellulose and hemicellulose, instead it is made up of various phenolic alcohols such as coniferyl, sinapyl and p-coumaryl alcohol. These phenols bind to cellulose fibers, forming a complex three-dimensional network that provides structural support within plant cell walls. The woody biomass is greatly impacted by lignin, which is removed to obtain cellulose for the paper and biofuel industries (Naseeruddin et al., 2017).

Pretreatment is a vital step in the economic transition of LCB into biofuels. This stage is crucial in biomass conversion due to the LCB’s complex hierarchical structure. The structure is changed during biomass pretreatment to improve conversion efficiency and to help enzymatic hydrolysis (Rezania et al., 2020). Pretreatment is a process that helps break various linkages in biomass and release sugars. These sugars are further used as substrates for fermentation to produce bioethanol. An efficient pretreatment should break the rigid structure, delignify and enhance the accessibility of cellulose and hemicellulose for the production of fermentable (Jain and Kumar, 2024).

Among diverse pretreatment methods, microwave-assisted alkali pretreatment attracts attention due to its rapid, uniform heating, which is capable to solubilizes lignin and partially removes hemicellulose (Lorenci Woiciechowski et al., 2020). The synergistic effect of microwave irradiation and alkali could greatly improve the cellulose accessibility and reduce crystallinity. Compared with conventional pretreatment, microwave processes improve reaction efficiency by shortening the pretreatment time and energy consumption (Jain and Kumar, 2024). In general, pretreatment is followed by either acid or enzymatic hydrolysis, which converts the polysaccharide cellulose to fermentable sugars such as glucose. Acids break down the complex polysaccharides into simple monomeric sugar molecules, which are essential for bioethanol production. This increases the availability and biodegradability of cellulose (Alexand et al., 2020). Acid pretreatment is less effective in terms of delignification, but it can still disrupt lignin. But extensive hydrolysis may lead to the formation of degradation products such as furfural and 5-hydroxymethylfurfural, which inhibit microbial fermentation. Also, the neutralization of the hydrolysates before fermentation and the use of more expensive equipment are still a challenging process. Otherwise, this pretreatment method offers advantages such as simple pretreatment for LCB, rapid reaction rates, moderate temperatures, and low costs (Amdebrhan et al., 2016).

Although numerous studies have investigated lignocellulosic biomass pretreatment, limited information is available on the integrated optimization of microwave-assisted alkaline pretreatment followed by mild H₂SO₄ hydrolysis for Prosopis juliflora stems. Already reported literature has mainly focused on conventional alkaline or acid pretreatment processes, which generally require longer processing times, higher chemical consumption, or lack systematic optimization of interacting process variables. Furthermore, this work is to develop an efficient, low-severity, statistically optimized, and scalable pretreatment strategy that maximizes fermentable sugar recovery while minimizing energy consumption and chemical usage.

The present study addresses this research gap by developing an integrated pretreatment strategy comprising microwave-assisted KOH pretreatment followed by controlled dilute H₂SO₄ hydrolysis for the efficient deconstruction of P. juliflora stems. Unlike previous studies that primarily investigated individual pretreatment methods, this work systematically optimizes both the pretreatment and hydrolysis stages to maximize fermentable sugar recovery while reducing process severity. The microwave-assisted alkaline pretreatment parameters, including KOH concentration (1%–3%, w/v), biomass loading (one to five g), microwave power (150–450 W), and irradiation time (three to five min), were optimized using Response Surface Methodology (RSM) based on a Box–Behnken Design (BBD). Subsequently, the sulfuric acid concentration for hydrolysis was optimized to enhance cellulose conversion into fermentable sugars. Reducing sugar concentrations before and after the acid hydrolysis were quantified using the DNS assay, while scanning electron microscopy (SEM) was employed to investigate the morphological changes associated with biomass deconstruction. The novelty of this study lies in integrating statistically optimized microwave-assisted alkaline pretreatment with mild acid hydrolysis to establish a rapid, low-severity, and scalable process that improves lignocellulosic deconstruction and fermentable sugar production from P. juliflora, thereby contributing to the development of sustainable and economically viable bioethanol production.

2 Materials and methods

2.1 Selection of lignocellulosic feedstock

Prosopis juliflora stems were collected from the campus surroundings of SASTRA University, Thirumalaisamudram, Thanjavur, Tamil Nadu (10.9631° N, 79.3854° E). Then the biomass was sun-dried for 8–10 days and pulverized into a powdered sample. The powdered sample was sieved using an automatic sieving machine and collected the particles of size ranging from 0.5 to 1.0 mm and stored in an air-tight container for further processing.

2.2 Materials

Potassium hydroxide–KOH (88%, extra pure), sodium hydroxide (98%), DNS powder and Sodium sulfate were purchased from Sisco Research Laboratories (SRL), Mumbai. Sulfuric acid (98%), hydrochloric acid–HCL (80%), and Phenol crystalline (99%) were purchased from Merck Life Science, Mumbai. The reagents are prepared using analytical-grade distilled water for the following experiments.

2.3 Design of the box behnken method for optimization

The pretreatment optimization of P. juliflora stem biomass for fermentable sugars production was designed using Design Expert (Version 12.0) software. Total of 29 experimental runs were generated and apply a second-order quadratic polynomial regression model for analyzing the operating parameters. Three levels and four factors were considered in this design (A) KOH conc (1, 2% and 3%) (B) biomass loading (1, 3 and 5 g) (C) microwave power (150, 300 and 450 W) and (D) Irradiation time (3, 4 and 5 min). Based on the existing literature, the parameters’ range was established. The fermentable sugars produced during microwave-assisted KOH pretreatment and acid hydrolysis were evaluated as response variables 1 and 2 as before hydrolysis (BH) and after hydrolysis (AH), respectively. The estimated dependent and independent variables are reported in Table 1.

TABLE 1. Design table to estimate the reducing sugars produced before and after hydrolysis of microwave-assisted KOH pretreatment on Prosopis juliflora using Box-Behnken design.

BH−Before Hydrolysis and AH−After Hydrolysis.

2.4 Microwave-assisted KOH pretreatment

Microwave-assisted KOH pretreatment was performed on a microwave oven (Samsung MODEL: MC28A5033CK/TL). Each experimental trial was performed as per the Box Behnken Design. Prosopis juliflora stem biomass was pretreated under various operating conditions mentioned in Table 1. Before operating the microwave oven, the oven was preheated for 5 min at 450W and then each experimental trial was carried out in 1L Erlenmeyer flasks, one after the other with a constant liquid volume of 500 mL under static conditions. After the pretreatment, reaction mixture was cooled to room temperature and undergoes the filtration processes. Each pretreated sample was filtered independently, and solids were dried at 90 °C for 24 h in a hot air oven, while the liquid obtained after the pretreatment were stored separately for DNS analysis. The pretreated liquid exhibited an alkaline pH ranging from ∼13.2 to 13.8, depending on the KOH concentration (1%–4%, w/v). For reducing sugar analysis, the pH of BH samples was neutralized by the gradual addition of 0.5 M sulfuric acid (H₂SO₄) under continuous stirring. The acid was added dropwise until the pH reached ∼7.0, as measured using a calibrated digital pH meter. Then 1 mL of the neutralized pretreated liquid samples were used for DNS analysis, and the absorbance values were recorded to identify the reducing sugars produced before hydrolysis.

2.5 Acid hydrolysis

Initially, the pretreated P. juliflora stem biomass were used to optimize the acid hydrolysis with various concentrations of H₂SO₄ (1, 3, 5, 7, and 9%) and the acid hydrolysis reaction was carried out in an autoclave at 121 °C for 60 min. After hydrolysis, the mixture was centrifuged for 5 min at 10,000 rpm to extract the hydrolyzed liquid and the solid residue. Then, liquid hydrolyzate were collected, and the pH was neutralized to pH seven in order to analyze the production of reducing sugar using DNS assay and the absorbance was recorded. The hydrolysate with the maximum reducing sugar was found and identified as the optimized acid hydrolysis condition (5%) of the pretreated biomass.

All the pretreated solids from the 29 trials were hydrolyzed with 5% H₂SO₄, 1:100 g/mL of solid to liquid ratio at 121 °C for 60 min in an autoclave. After the hydrolysis, reaction mixture was centrifuged at 10,000 rpm for 5 min, and liquid was collected. Like BH, the hydrolysate obtained after the hydrolysis (AH) was adjusted to pH ∼7.0 using 0.5 M KOH before DNS analysis. Neutralization to near-neutral pH was performed to prevent interference from highly acidic or alkaline conditions during the reducing sugar determination. After the pH neutralization, the DNS assay was performed and the absorbance was recorded. The workflow of the present study is illustrated as Figure 1 Using Super Pro Designer software (Version 11).

2.6 Characterization

2.6.1 Compositional and chemical analysis

The lignocellulosic composition (cellulose, hemicellulose, lignin, extractives and ash) for pulverized P. juliflora stem biomass was analyzed using Van Soest detergent extraction method (Godin et al., 2011). The presence of reducing sugars before and after acid hydrolysis was quantified by the 3,5-dinitrosalicylic acid (DNS) method with an absorbance at 540 nm (Miller, 2002).

2.6.2 SEM analysis

Scanning Electron Microscopy (SEM) is a potent analytical instrument that uses high magnification to study a wide range of materials and generate high-resolution images. The breakdown of lignocellulosic components in the pretreated sample was identified using scanning electron microscopy analysis. It is used to characterize the morphological changes of native and pretreated solids using a VEGA 3TESCAN scanning electron microscope. Samples were deposited on a carbon tape, vacuum-dried, with an accelerated voltage of 10 kV, using a secondary electron detector.

3 Result and discussion

3.1 Compositional analysis of Prosopis juliflora stem

The composition of native P. juliflora stem biomass reveals that 42% ± 1.0% cellulose, 23% ± 0.5% hemicellulose, 27% ± 1.2% lignin, 5% ± 0.3% ash, and 3% ± 0.5% extractives were present. The composition of cellulose and hemicellulose in the biomass determines the ability to produce fermentable sugar. The biomass composition values were found to be in reasonable agreement with the previously reported composition studied by Naseeruddin et al. (Naseeruddin et al., 2013) where the composition of P. juliflora was reported as 49.40% ± 0.4% cellulose, 18.00% ± 0.4% hemicellulose, 28.30% ± 0.9% lignin, 4.30% ± 0.1% ash and extractives. Like the present findings, Thanarasu et al. (Thanarasu et al., 2019) reported 45% ± 1.5% cellulose, 25% ± 1.0% hemicellulose and 23% ± 1.2% lignin, especially carbohydrate fractions.

The differences in the composition of P. juliflora was observed due to geographical origin, plant maturity, environmental conditions and analytical methods. The relatively high cellulose content (42%) further suggests that P. juliflora can be used as a promising lignocellulosic feedstock for bioethanol production. However, the higher lignin content (27%) necessitates elaborate pretreatment techniques required for efficient delignification. The presence of hemicellulose (23%) further confirms the ability to produce fermentable sugars before and after hydrolysis. Moreover, the ash content (5%) and extractives (3%) are within the normal ranges reported in literature, indicating that there is little interference in downstream processing. Overall, the composition analysis in the present study agrees with the reported literature and highlights the suitability of P. juliflora stem biomass as a potential feedstock for biofuel production. The moderate lignin content and appreciable carbohydrate fractions suggest that the high sugar yields are due to the importance of pretreatment optimizations and mild acid hydrolysis.

3.2 Optimization of H₂SO₄ concentration for acid hydrolysis

At first, the pretreated biomass samples were taken and optimized using various H₂SO₄ concentrations (1, 3, 5, 7, and 9%) to determine the release of maximum reducing sugars. Acid hydrolysis was performed in an autoclave at 121 °C for 60 min. Following this step, a DNS assay was performed for each acid concentration for the determination of reducing sugars after adjusting to neutral pH. Based on the results, it was found that 5% H₂SO₄ concentration showed the highest conversion of reducing sugar (2.149 mg/mL) as shown in Figure 2. With the optimized pretreatment conditions (300W, 4 min, and H₂SO₄ of 1:100 g/mL). The effect of H₂SO₄ concentration on reducing sugar production was evaluated under fixed hydrolysis conditions (121 °C, 60 min) following microwave-assisted KOH pretreatment of P. juliflora. Figure 2. Shows that the reducing sugar concentration increased from 1% to 5% as the H₂SO₄ concentration increases and reached the maximum, which implied the enhanced cleavage of glycosidic bonds and effective depolymerization of cellulose. Beyond this optimum level, a reduction in reducing sugar was observed at higher acid concentration (from 7 to 9%), and this was clearly attributed to secondary degradation of the released sugars to inhibitory compounds such as furfural and HMF. The hydrolysis process under the optimized conditions (300 W, 4 min pretreatment; 1:100 ratio) resulted in a maximum reducing sugar concentration of 2.149 mg/mL, confirming that moderate acid concentration (5%) is sufficient for efficient conversion of biomass with minimum sugar degradation. These results emphasize the significance of optimizing the acid strength to achieve the highest recovery of fermentable sugars and to prevent the over-degradation, thus improving the overall efficiency of the biomass-to-bioethanol conversion process.

3.3 Optimization of microwave-assisted KOH pretreatment for fermentable sugars production

The optimization of BBD using the microwave-assisted KOH pretreatment on P. juliflora stem biomass provided various insights which deals with the interaction of different parameters on the yield of reducing sugars. Using the data from the experimental trials, study assessed four different variables: KOH concentration (1%, 2% and 3%), biomass loading (1g, 3g and 5g), microwave power (150 W, 300 W and 450 W), and irradiation time (3, 4 and 5 min). Based on the experiments, factors affecting the reducing sugars during the microwave pretreatment process as well as after the acid hydrolysis was determined. A broad range of reducing sugars was obtained before and after the hydrolysis process. The concentration of H₂SO₄ for acid hydrolysis was optimized to 5% for the maximum conversion of reducing sugar and found the optimized of 5% H₂SO₄. The concentration of fermentable sugars produced before and after hydrolysis was tabulated as response variables in Table 1.

A systematic examination of the 29 BBD experiments revealed that higher biomass loading (5 g) consistently resulted in higher fermentable sugar production irrespective of KOH concentration, provided sufficient microwave power and irradiation time were applied. Runs performed at low biomass loading (1 g) generally produced lower fermentable sugar yields, indicating limited availability of cellulose and hemicellulose for hydrolysis. Intermediate microwave power (300 W) combined with moderate irradiation time (4 min) produced consistently higher responses than either lower or higher operating levels, suggesting that excessive microwave intensity or prolonged exposure may not proportionally improve biomass deconstruction. The optimized condition (2% KOH, 5 g biomass, 150 W, 4 min) yielded the highest reducing sugar concentration after hydrolysis (2.25 mg/mL), whereas combinations involving low biomass loading and insufficient microwave energy resulted in the lowest sugar recovery. These observations demonstrate the strong interaction among the process variables and justify the statistically determined optimum conditions.

The fitted quadratic models effectively describe the relationship between the independent variables (A–D) and the responses, namely, fermentable sugars produced before and after hydrolysis. The coefficients of variance (R ²) for before and after hydrolysis were found to be 0.9249 and 0.8854, respectively. Based on these R ² values, the model denotes the strong statistical significance and fit. Adequate Precision (signal-to-noise ratio) exceeded the recommended threshold of 4, with values of 14.728 for before hydrolysis and 10.202 for after hydrolysis, which indicates an adequate model and is also used to navigate the model space. Furthermore, the Predicted R ² values for fermentable sugars produced before hydrolysis and after hydrolysis are 0.6153 and 0.4068, respectively. Adjusted R ² values for fermentable sugars produced before hydrolysis and after hydrolysis are 0.8480 and 0.7707, respectively, in comparison with the Predicted R ² values, showing differences around 0.2 to 0.3, which suggests reasonable improvement in differences. These results show better predictive capability and confirm that the developed models are reliable and suitable for driving the experimental design space.

Table 2 is the representation of response surface models obtained from statistical data for the fermentable sugars produced before hydrolysis. It shows the model F value of 12.16, so there is a 0.01% chance that an F value this large could occur due to noise, and it indicates the model is significant. The P value is less than 0.0500, indicating model terms are significant. Here, the interactions of A, B, C, D, BC and BD are the significant model terms. The lack of fit is 0.2626 with an insignificant pointing the model fit, so there is a 26.26% chance that a Lack of Fit F-value this large could occur due to noise. Similarly, Table 3 shows the response of fermentable sugars produced before hydrolysis. The model F value is 7.72, which indicates the significance of the model, and there is 0.02% chance for the F value to be large due to noise. The P value is less than 0.0500, indicating model terms are significant. The model terms of A, B, C, D, CD, A², B² and C² are significant in this case. The lack of fit is 0.1990; it is not significant, and there is a 19.90% chance that a lack of fit occurs due to noise.

R 1 B H = + 0.463 − 0.047 A + 0.229 B + 0.059 C + 0.044 D − 0.018 A B + 0.023 A C + 0.019 A D − 0.127 B C + 0.078 B D − 0.014 C D − 0.012 A 2 − 0.027 B 2 + 0.05 C 2 + 0.032 D 2 (1)

R 1 A H = + 0.625 + 0.175 A + 0.455 B − 0.154 C + 0.296 D + 0.183 A B + 0.008 A C − 0.04 A D − 0.221 B C + 0.067 B D − 0.387 C D + 0.257 A 2 + 0.314 B 2 + 0.21 C 2 − 0.008 D 2 (2)

TABLE 2. Analysis of variance for the quadratic model describing the reducing sugars produced before hydrolysis of Prosopis juliflora.

TABLE 3. Analysis of variance for the quadratic model describing the reducing sugars produced after hydrolysis of Prosopis juliflora.

Equations 1, 2, expressed as coded variables, were used to predict the responses at specific factor levels. In the coded system, the high and low levels of each factor are represented by +1 and −1, respectively. The coded equations allow the comparison of the factor coefficients, providing a means of evaluating the relative effect of individual process variables on the reduction of sugar yields before and after hydrolysis. In these equations, A, B, C, and D denote the independent variables corresponding to KOH concentration, biomass loading, microwave power and time, respectively. Interaction effects between the variables are represented by the terms AB, BC, CD and DA, and the quadratic effects of the variables are expressed by the terms A², B², C² and D². 3D response surface plots were generated based on the fitted quadratic models to visualize the relationships between the independent variables and the responses. The surface plots were represented in Figures 3, 4. The relationship between several input variables and one or more response variables is analyzed using this quadratic method. The surface plot is a key feature of RSM that visually illustrates these relationships. In a surface plot, the x and y-axes are the two inputs, and the z-axis is the response. The plot is a three-dimensional (3D) surface of response change vs. input variables. Figure 5 shows a parity plot of (a) BH and (b) AH samples, where the X-axis represents the actual values obtained from experimental data whereas the Y-axis (Predicted Value) shows values predicted by the model. If the predicted values are similar to the actual values, then the points lie on the 45-degree diagonal line (line of parity). The clustering near the diagonal indicates that the RSM model is performing well in predicting reducing sugar yield both before and after hydrolysis. From Figures 5a,b, it was identified that the data points before acid hydrolysis (BH) are dispersed around the diagonal line, suggesting that while the RSM model can estimate sugar yield, its accuracy is only moderate. This reflects the lower and less consistent sugar release from untreated biomass, which makes predictions less dependable. In contrast, after acid hydrolysis (AH), the points align much more closely with the diagonal, demonstrating strong agreement between predicted and actual values. This indicates that hydrolysis markedly enhances sugar release and that the RSM model captures the process behavior with high accuracy.

Figure 6 illustrates the perturbation plots, showing the influence of individual process variables on reducing sugar production for Figure 6a BH and (b) AH, while maintaining the remaining variables at their center levels. A steeper slope indicates a greater sensitivity of the response to changes in the corresponding process variable. In the case of BH, biomass loading (B) showed the greatest positive influence on reducing sugar release, followed by microwave power (C) and irradiation time (D). In contrast, higher KOH concentration (A) exhibited a slight negative effect. Similarly in the case of AH, biomass loading (B) remained the most significant factor, while KOH concentration (A) and irradiation time (D) positively enhanced fermentable sugar production by improving cellulose accessibility. Overall, the perturbation analysis confirms that biomass loading is the dominant parameter governing sugar production, with the combined effects of microwave power, alkali concentration, and irradiation time contributing to efficient biomass deconstruction and enhanced hydrolysis.

The process was tested at a small scale using a household microwave, hence, various operating issues such as heating consistency, temperature control was a problem. Also, this biomass availability is plenty and to scaling up to industrial level microwave systems need more research and cost. Future work should explore more about the continuous-flow microwave reactors, recycling reagents, optimizing at higher solid concentrations, integrating processes, and validating on a pilot scale would be a solution for commercial potential.

3.4 SEM analysis

SEM was employed to study the morphological and structural changes of P. juliflora stem biomass before and after treatment. The representative micrographs are shown in Figure 7, clearly showing the progressive disruption in the lignocellulosic matrix. The native biomass (Figure 7a) has a compact, smooth and well-ordered surface with an intact and rigid structure, as expected for a natural lignin and hemicellulose sheath around cellulose fibres (Alexand et al., 2020). This dense and organized morphology hinders the access of enzymes and acid. The structure (Figure 7b) is much rougher, fragmented and disordered after microwave-assisted KOH pretreatment, which indicates the destruction of lignin and hemicellulose network. The formation of cracks, pores and irregular surfaces indicates efficient delignification and partial removal of hemicellulose, which increases surface area and leads to better accessibility of cellulose. Such structural disruption is consistent with the high efficiency of delignification (up to 73.43%) observed, which aids in improving the recovery of cellulose (Lin and Tanaka, 2006). Further acid hydrolysis (Figure 7c) resulted in a more pronounced morphological transformation into highly porous, fibrillated and hollow structures. The disappearance of amorphous components endorses the extensive depolymerization and structural disintegration, exposing cellulose microfibrils. In general, the SEM analysis reveals the efficiency of the combined pretreatment and hydrolysis in deconstructing the biomass and improving its amenability to further conversion into fermentable sugars (Ghazanfar et al., 2022).

3.5 Comparative assessment of fermentable sugars production from Prosopis juliflora and other biomass

A comparative assessment of fermentable sugars production from P. juliflora stem biomass based on assorted pretreatment strategies is provided in Table 4. The results clearly shows that pretreatment methodology and operation conditions impart a large effect on sugar yield and process efficacy. Based on the literature, conventional dilute acid pretreatment followed by the moderate hydrolysis efficiency was approximately 200.50 mg/g biomass, and also reported that sugar degradation and inhibitor formation was also found to be high under elevated temperature conditions (Gupta et al., 2009). Similarly, acid hydrolysis system produced 184.71 mg/g P. juliflora biomass (Amdebrhan et al., 2016), further confirming that acid hydrolysis approach did not delignify before saccharification of cellulose. This emphasizes the need for delignification for cellulose accessibility improvement, in contrast to alkaline pretreatment methods (particularly sodium hydroxide treatment), which achieved higher sugar yields (583.9 mg/g biomass) (Palled et al., 2022). This approach, however, often involve longer reaction times and larger amounts of chemicals, which decreases the economic feasibility of process. Literature report using the pretreatment of P. juliflora achieved the reducing sugar yield of 96.75 mg/g biomass, which showed loss of fermentable components and lack of structural disruption of the biomass during processing. Similarly, autohydrolysis with nitric acid and ultrasonication produced 35.5 g/L of reducing sugars (Sivarathnakumar et al., 2019), which showed better performance but was still limited by process complexity and energy input. Chemical pretreatment with various reagents (NaOH/KOH, NaClO₂, Na₂SO₃, Na₂S₂O₄, NH₃) and biphasic acid hydrolysis resulted in only ∼2.5–3.5 g/L (Naseeruddin et al., 2013). The study on sugarcane leaf for fractionation lignin using microwave assisted chemical pretreatment (0.5 M Na₂CO₃, NaOH, 650 W at a frequency of 2,450 MHz in 3 min with enzymatic saccharification) produces 601 mg/g of reducing sugars and 231 mg/g of glucose with significant delignification (David and Krishnamurthi, 2024). Another study on corn cob achieved 73.36% lignin removal and 537.4 mg/g reducing sugars under Microwave-assisted alkali pretreatment (691.73 W, 3.4% NaOH, and 15.5 min), and dilute sulfuric acid hydrolysis (0.5%–2.5%, 110,130 °C, 3,060 min) (Liknaw et al., 2025). Thus, the severity of the process and multi-step treatments may lead to considerable sugar loss and inhibitor production, which decreases the overall efficiency. In comparison, the present study using microwave-assisted KOH pretreatment followed by controlled acid hydrolysis achieved a maximum sugar yield of 225 mg/g, showing a significant improvement in process intensification.

TABLE 4. Comparison of fermentable sugar yields on Prosopis juliflora with the existing literature.

The performance enhancement observed in this work is attributed to the synergy of microwave irradiation and alkali treatment, which rapidly removes lignin and increases surface area and cellulose accessibility. Moreover, the optimized conditions (2% KOH, 300 W, 4 min and 5% H₂SO₄ hydrolysis for 60 min at 120 °C) allowed a good conversion of carbohydrates with low losses of degradation. In summary, this comparative study shows that classical and chemical-intensive methods can reach moderate to high sugar yields but are often constrained by long processing times, high chemical consumption and inhibitor formation. The proposed microwave-assisted KOH method provides a good compromise between shorter reaction times, lower chemical consumption and higher sugar recovery, making it a promising and scalable strategy for lignocellulosic bioconversion.

4 Conclusion

The response surface methodology on the optimization of microwave-assisted KOH pretreatment revealed that the reducing sugar production is highly controlled by the interaction of biomass loading, microwave power and irradiation time during microwave-assisted KOH pretreatment. The increase in biomass loading (5 g) with suitable microwave power and irradiation time improved the availability and accessibility of cellulose and hemicellulose, which considerably increased the reducing sugar yield. Maximum reducing sugar yield (2.25 mg/mL) after hydrolysis confirmed the effectiveness of optimized pretreatment and acid hydrolysis conditions for effective biomass deconstruction. On the other hand, lower sugar yields were obtained when the microwave power was not sufficient, or the combination of parameters was not optimal, even at higher biomass loading, confirming the importance of the synergistic effects among the process variables. In conclusion, this work shows that the optimization of pretreatment parameters is crucial for the maximal production of fermentable sugars and overall efficiency of lignocellulosic biomass conversion.

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Footnotes

Edited by: Malinee Sriariyanun, Kmutnb, Thailand

Reviewed by: Nida Arshad, King Mongkut’s University of Technology North Bangkok, Thailand

Muhammad Arif Darmawan, National Research and Innovation Agency (BRIN), Indonesia

Footnotes

AH, after hydrolysis, BH, before hydrolysis, BBD, Box-Behnken Design, DNS, 3,5-dinitrosalicylic acid, LCB, lignocellulosic biomass, RSM, response surface methodology, SEM, scanning electron microscope, 3−D, and three-dimensional.