Optimization of Pectinase Production from Silkworm Excrement Using Aspergillus niger

Abstract

Silkworm excrement, a byproduct of the sericulture industry, is rich in organic compounds and presents a sustainable substrate for enzyme production. This study investigates the potential of silkworm excrement as a substrate for pectinase production using Aspergillus niger submerged fermentation. Single-factor experiments and a Box–Behnken Design (BBD) were employed to identify and optimize the key medium components and their interactions. Results indicated that the optimal concentrations for maximum pectinase activity were CaCl₂ at 0.437 g/L, orange peel powder at 4.223 g/L, (NH₄)₂SO₄ at 15.761 g/L, and bran at 33.590 g/L. The optimized conditions achieved a pectinase activity of 20.505 U/mL, validating the robustness of the RSM model. This approach not only maximizes enzyme activity but also presents a sustainable and cost-effective method for utilizing a byproduct in the sericulture industry, aligning with principles of circular economy and waste minimization.

1. Introduction

The sericulture industry, an ancient and enduring sector, remains a vital part of the global economy. Currently, nearly 40 countries and regions engage in sericulture, with the majority of production concentrated in Asia, particularly in China, India, Japan, and Brazil. China stands out as the world’s largest producer of cocoons and raw silk, accounting for over 80% of the global output. A byproduct of this industry, silkworm excrement—also known as silkworm castings or frass—presents both opportunities and challenges. Annually, significant quantities of silkworm excrement are generated during the rearing process. For instance, producing one ton of cocoons typically yields 3.3 tons of silkworm excrement [1]. As the largest sericulture producer, China raises approximately 15 million silkworm eggs per year, generating around 4.5 million tons of silkworm excrement. This byproduct is often underutilized and discarded, posing environmental challenges such as soil and water pollution [2].

Silkworm excrement is a rich source of nutrients and active ingredients, including proteins, cellulose, pectin, and essential minerals like calcium, phosphorus, copper, iron, zinc, and manganese. These characteristics make it a valuable resource for agricultural applications. Recent research has explored innovative ways to utilize silkworm excrement effectively, including composting for organic fertilizer production, vermicomposting with earthworms to enhance nutrient availability, and as a substrate for enzyme production through microbial fermentation processes [3]. In agriculture, silkworm excrement is widely used as an organic fertilizer due to its high content of nitrogen, phosphorus, and potassium, which improve soil fertility and promote plant growth [4]. It also serves as a substrate for earthworm cultivation, enhancing the production of vermicompost that enriches soil with beneficial microorganism [3]. Additionally, silkworm excrement is being explored for its potential in ecological restoration, particularly in remediating heavy metal-contaminated soils by immobilizing metals and supporting vegetation growth [5]. Research is also ongoing to optimize the composting process of silkworm excrement by isolating and using cellulose-degrading bacteria, which can improve the efficiency of its decomposition and utilization [6]. In recent years, the production of biological enzymes using silkworm excrement through microbial fermentation has gained attention. For instance, He et al. confirmed the feasibility of microbial fermentation of silkworm excrement to produce pectinase using Aspergillus oryzae and Aspergillus acanthus [7,8].

Pectinase holds significant industrial value, particularly in juice clarification and wine production, where it enhances yield and stability (Zion Market Research, 2023) [9]. However, the high cost of conventional substrates for enzyme production limits its scalability. This study addresses this gap by utilizing silkworm excrement—a nutrient-rich, low-cost byproduct—to produce pectinase via Aspergillus niger, aligning with circular economy principles.

Over the past decades, statistical experimental methods have been widely applied to optimize industrial processes. Response Surface Methodology (RSM) has been particularly effective in optimizing the production of enzymes, such as pectinases, cellulolytic enzymes, and xylanase [10,11,12]. Box–Behnken Design (BBD) is an experimental design method within RSM that optimizes response variables by exploring the interactions among three or more continuous factors. Aspergillus niger, an important industrial microorganism known for its strong enzyme secretion capacity, is capable of producing a variety of enzymes, including amylase, acid protease, cellulase, pectinase, and glucose oxidase. It has been recognized as food-grade due to its safety and efficacy [13,14]. To date, no study has optimized silkworm excrement-based fermentation using RSM, nor has any work reported the isolation of Aspergillus niger strains directly adapted to this substrate. Herein, we present the first systematic investigation of pectinase production from silkworm excrement using submerged fermentation with Aspergillus niger. A native strain (JA026) isolated from silkworm excrement was employed to leverage substrate-specific adaptability. Single-factor experiments identified critical parameters (e.g., CaCl₂, orange peel powder), followed by RSM-BBD optimization to model interactions and maximize enzyme activity. This dual approach not only addresses a critical gap in waste valorization but also establishes a novel framework for sustainable enzyme production aligned with circular economy principles.

2. Materials and Methods

2.1. Microorganism and Substrate

Aspergillus niger JA026 (Zenodo database: 15325017) was isolated from silkworm excrement collected from Hechi, Guangxi Province, China.

Silkworm excrement comes from a sericulture farm of Hechi, Guangxi Province, China. The raw silkworm excrement was subjected to a thorough cleaning process to eliminate impurities, primarily residual mulberry leaves and other foreign materials that could potentially interfere with the fermentation process or provide a substrate for the growth of unwanted microorganisms. Following the removal of these impurities, the material was dried in a controlled environment at 60 °C for a duration of 3 h. Once dried, the silkworm excrement was mechanically crushed using a specialized crusher to achieve a uniform particle size. The crushed material was then passed through a 40-mesh sieve to obtain a consistent texture. In addition to the physical pre-treatment, the silkworm excrement was comprehensively characterized to determine its nutritional composition. Through a series of laboratory analyses conducted as part of this study, it was found to contain 15.7% crude protein, 3.4% crude fat, 19.3% crude fiber, and 7.6% pectin. These analyses were performed using well-established methodologies, including Kjeldahl nitrogen analysis for the determination of protein content, Soxhlet extraction for fat content, and gravimetric procedures for fiber and pectin content.

2.2. Medium and Fermentation Conditions

The basal fermentation medium consisted of the following (g/L): silkworm excrement 60, bran 35, orange peel powder 5, (NH₄)₂SO₄ 1, K₂HPO₄ 1. The initial pH of the fermentation medium was adjusted to 5.0.

Submerged fermentation was conducted in 250 mL Erlenmeyer flasks containing 100 mL of medium, inoculated with 2 mL spore suspension (1 × 10⁸ spores/mL) and incubated at 30 °C for 3 days at 150 rpm.

2.3. Experimental Design

2.3.1. Single-Factor Experiments

To evaluate the impact of various medium components on pectinase production during fermentation, single-factor experiments were conducted. The specific parameters and their levels were designed as outlined in

Table 1. The setting of single-factor experiments.

Factor	Additive Amount (g/L)
	1	2	3	4	5	6
Calcium chloride	0	0.2	0.4	0.6	0.8	-
Orange peel powder	0	1	3	5	7	9
Ammonium sulfate	0	2.5	5	10	15	20
Bran	25	27.5	30	32.5	35	-

. Each experiment was performed in triplicate to ensure reproducibility and statistical significance. Statistical analyses, including one-way ANOVA followed by Tukey’s HSD post hoc tests, were performed to determine the significance of inter-group differences and identify specific variations among dosage levels.

2.3.2. Box–Behnken Design

Following the single-factor experiments, a BBD was employed to further optimize the fermentation conditions. This design utilizes RSM to evaluate the effects of multiple variables and their interactions on the response. The concentrations of the medium components were set at three levels: low (−1), center (0), and high (+1). The software for BBD was Design-Expert 12.0.3. The experimental design, including the symbolic codes, names, and actual levels of the variables, is detailed in

Table 2. Coded values of independent variables used in the Box–Behnken design.

Code	Variables	Level
		−1	0	1
A	Calcium chloride	0.2	0.4	0.6
B	Orange peel powder	3	5	7
C	Ammonium sulfate	10	15	20
D	Bran	30	32.5	35

Unit: g/L.

.

A total of 29 experimental runs were conducted, with each run performed in triplicate to ensure reproducibility. The average pectinase activity was recorded as the response variable. The BBD was implemented using Design-Expert 12 to analyze the data and determine the optimal conditions for pectinase production.

2.4. Pectinase Assay

Following fermentation, the culture medium was centrifuged at 12,000 rpm for 15 min at 4 °C to separate the biomass from the supernatant. The resulting filtrate was utilized for the enzyme assay.

The pectinase activity was determined using the DNS method as described in [15]. The reaction mixture was prepared by adding 1.5 mL of 0.5% (w/v) pectin, dissolved in 0.2 mol/L sodium phosphate–citric acid buffer (pH 5.0), to 1.0 mL of appropriately diluted crude enzyme solution. The mixture was incubated at 50 °C for 30 min to allow the enzymatic reaction to proceed. After the incubation period, 3.0 mL of DNS reagent (1% 3,5-dinitrosalicylic acid, 20% potassium sodium tartrate, 1% NaOH, 0.2% phenol, and 0.05% Na₂SO₃) was added to the reaction mixture to stop the reaction. The tubes were then boiled for 10 min to stabilize the color development. Once cooled to room temperature, the volume of the mixture was adjusted to 25 mL with distilled water, and the optical density was measured at 540 nm. A standard curve of D-galacturonic acid was prepared to quantify the amount of galacturonic acid released. One unit of pectinase activity is defined as the amount of enzyme required to release 1 µmol of galacturonic acid per minute under the specified assay conditions. All enzyme activity values are presented as the mean of triplicate measurements.

3. Results

3.1. Single-Factor Analysis of Pectinase Production

To investigate the influence of various medium components on pectinase production during fermentation, a series of single-factor experiments were conducted. The results are summarized in Figure 1.

Figure 1a shows the effect of Calcium chloride (CaCl₂) concentration on pectinase activity. Pectinase activity increased with increasing CaCl₂ concentration, reaching a maximum of 16.25 U/mL at 0.4 g/L. Further increases in CaCl₂ concentration led to a decline in enzyme activity, indicating an optimal concentration for pectinase production. Figure 1b illustrates the impact of orange peel powder concentration on pectinase activity. Enzyme activity peaked at 9.48 U/mL with the addition of 5 g/L of orange peel powder. Higher concentrations of orange peel powder resulted in decreased enzyme activity, suggesting that excessive amounts may inhibit pectinase production. Figure 1c presents the effect of Ammonium sulfate ((NH₄)₂SO₄) concentration on pectinase activity. Enzyme activity increased with the addition of (NH₄)₂SO₄, reaching a maximum of 14.87 U/mL at 15 g/L. Beyond this concentration, enzyme activity declined, indicating that optimal pectinase production is sensitive to (NH₄)₂SO₄ levels. Figure 1d illustrates the effect of bran concentration on pectinase production. The highest enzyme activity (8.2 U/mL) was achieved at a bran concentration of 32.5 g/L. This finding underscores the importance of maintaining an optimal balance between carbon and nitrogen sources for maximizing pectinase yield.

Statistical analysis of the results of the single-factor experiments showed that all single-factor experiments demonstrated statistically significant inter-group differences (p < 0.01) (Table S1).

3.2. Optimization of Medium Components for Pectinase Production by Box–Behnken Design

The BBD was employed to optimize the concentrations of key medium components (CaCl₂, orange peel powder, (NH₄)₂SO₄, and bran) and to evaluate their interactions on pectinase activity. The experimental design and responses for the 29 experimental runs are detailed in

Table 3. Box–Behnken Design (BBD) of factors in coded levels with pectinase activity as a response.

Run	A	B	C	D	Pectinase (U/mL)
					Experimental	Predicted
1	1	0	0	1	17.94	17.92
2	0	1	−1	0	16.74	16.86
3	−1	0	0	1	17.17	17.11
4	0	1	1	0	16.15	16.00
5	0	0	0	0	20.86	20.48
6	1	0	1	0	16.15	16.32
7	0	0	1	−1	15.64	15.61
8	1	0	0	−1	17.43	17.47
9	−1	0	1	0	16.41	16.54
10	0	0	0	0	20.25	20.48
11	0	−1	0	−1	16.4	16.39
12	0	−1	−1	0	16.91	17.04
13	0	0	−1	1	16.15	16.24
14	0	−1	1	0	17.68	17.54
15	1	1	0	0	15.63	15.64
16	0	−1	0	1	19.74	19.73
17	−1	−1	0	0	15.64	15.69
18	1	0	−1	0	17.69	17.53
19	0	0	0	0	20.22	20.48
20	1	−1	0	0	18.71	18.68
21	−1	0	0	−1	16.66	16.66
22	0	0	−1	−1	18.71	18.74
23	−1	0	−1	0	15.89	15.69
24	0	0	0	0	20.78	20.48
25	0	0	0	0	20.30	20.48
26	0	1	0	−1	18.45	18.42
27	0	1	0	1	16.01	15.98
28	0	0	1	1	18.97	19.00
29	−1	1	0	0	16.92	17.01

. The highest pectinase activity, ranging from 20.22 to 20.86 U/mL, was achieved at the central levels of all four components (Table 3: Runs 5, 10, 19, 24, and 25).

A second-order polynomial equation was fitted to the data to describe the relationship between pectinase activity and the four variables:

Y = 20.48 + 0.4050A − 0.4317B − 0.0908C + 0.2242D − 1.09AB − 0.5150AC + 0.0000AD − 0.3400BC − 1.44BD + 1.47CD − 2.04A² − 1.69B² − 1.93C² − 1.16D²

where Y represents pectinase activity (U/mL), and A, B, C, and D are the coded variables for CaCl₂, orange peel powder, (NH₄)₂SO₄, and bran, respectively.

The Analysis of Variance (ANOVA) for the BBD is presented in

Table 4. Analysis of Variance (ANOVA) for response surface quadratic model of Box–Behnken Design (BBD) for the production of pectinase.

Source	Sum of Squares	Df	Mean Square	F-Value	p-Value
Model	79.93	14	5.71	132.90	<0.0001	significant
A	1.97	1	1.97	45.82	<0.0001
B	2.24	1	2.24	52.05	<0.0001
C	0.0990	1	0.0990	2.30	0.1512
D	0.6030	1	0.6030	14.04	0.0022
AB	4.75	1	4.75	110.62	<0.0001
AC	1.06	1	1.06	24.70	0.0002
AD	0.0000	1	0.0000	0.0000	1.0000
BC	0.4624	1	0.4624	10.76	0.0055
BD	8.35	1	8.35	194.42	<0.0001
CD	8.67	1	8.67	201.89	<0.0001
A2	26.88	1	26.88	625.64	<0.0001
B2	18.59	1	18.59	432.82	<0.0001
C2	24.14	1	24.14	562.03	<0.0001
D2	8.68	1	8.68	202.06	<0.0001
Residual	0.6014	14	0.0430
Lack of Fit	0.2142	10	0.0214	0.2212	0.9758	not significant
Pure Error	0.3873	4	0.0968
Corr. Total	80.53	28

R² = 0.9925, R² (Pred) = 0.9772; df = degree of freedom; significant, p ≤ 0.05; non-significant, p > 0.05.

. The model’s Predicted R² value of 0.9772 closely aligns with the observed R² value of 0.9925, indicating a strong agreement between the predicted and observed values. This high correlation confirms the model’s accuracy in describing the relationship between the independent variables and pectinase production. The model’s F-value of 132.90 and a p-value of <0.0001 indicate that the model is highly significant. The terms A, B, AB, BD, CD, A², B², C², and D² were highly significant (p < 0.0001), while D, AC, and BC were significant (p < 0.05). The terms C and AD were not significant.

The normal probability plot of the residuals (Figure 2) shows that the residuals are approximately normally distributed, supporting the assumption of normality. The straight-line relationship in the plot indicates that the error terms are normally distributed, validating the model’s assumptions.

The optimal concentrations of medium components for achieving maximal pectinase activity were determined using response surface and contour plots, as depicted in Figure 3a–f. These plots elucidate the interactions among the experimental variables—CaCl₂, orange peel powder, (NH₄)₂SO₄, and bran—and their collective influence on pectinase activity. The three-dimensional (3D) surface plots exhibit a distinct peak, signifying the optimal conditions for pectinase production.

Figure 3a demonstrates that pectinase activity is significantly influenced by the concentrations of CaCl₂ and orange peel powder. The peak enzyme activity was observed within an intermediate range for both components: CaCl₂ concentrations between 0.4 and 0.5 g/L and orange peel powder concentrations between 4 and 6 g/L. This finding suggests that an optimal balance of these medium components is essential for maximizing pectinase yield.

Figure 3b illustrates the interaction between CaCl₂ (factor A) and (NH₄)₂SO₄ (factor C) and its effect on pectinase activity. The 3D surface plot delineates the complex relationship between these two variables and their combined impact on enzyme production. The plot indicates that pectinase activity is modulated by the interaction between CaCl₂ and (NH₄)₂SO₄, with optimal production occurring within specific concentration ranges for both factors. Notably, the highest enzyme activity was observed when CaCl₂ was between 0.4 g/L and 0.5 g/L and (NH₄)₂SO₄ was between 12.5 g/L and 17.5 g/L, suggesting a synergistic effect between these two components that significantly enhances pectinase activity.

Figure 3c presents the impact of CaCl₂ (factor A) and bran (factor D) on pectinase activity. The circular shape of the 3D response surface in Figure 3c indicates significant decreases in pectinase activity at both lower and higher concentrations of CaCl₂ and bran. The plot reveals that optimal pectinase production occurs within a specific concentration range for both components, specifically when CaCl₂ concentration is between 0.4 g/L and 0.5 g/L and bran concentration ranges from 31.25 g/L to 33.75 g/L, highlighting a narrow optimal range for these components that significantly influences pectinase production.

Figure 3d illustrates the significant influence of orange peel powder (factor B) and (NH₄)₂SO₄ (factor C) on pectinase production. The plot demonstrates that pectinase production is notably affected when both orange peel powder and (NH₄)₂SO₄ are present at their lower concentrations. The maximum pectinase activity was recorded within an intermediate concentration range for both components, orange peel powder between 4 and 6 g/L and (NH₄)₂SO₄ between 12.5 and 17.5 g/L, indicating that an optimal balance of these two components is essential for achieving peak enzyme yields.

Figure 3e provides a detailed examination of the interaction between orange peel powder (factor B) and bran (factor D) on pectinase production. The plot indicates that pectinase activity significantly diminishes at both lower concentrations of orange peel powder and bran but peaks when both components are present at intermediate concentrations. Specifically, the maximum pectinase activity was observed at orange peel powder concentrations ranging from 4 to 6 g/L and bran concentrations ranging from 32.5 to 33.75 g/L, suggesting that an optimal balance of these two components is crucial for achieving the highest enzyme yields.

Figure 3f presents the impact of (NH₄)₂SO₄ and bran concentrations on pectinase production. The plot reveals a pronounced effect on pectinase production when both (NH₄)₂SO₄ and bran are present at lower levels. The maximum pectinase activity was recorded at intermediate concentrations of (NH₄)₂SO₄ (12.5–17.5 g/L) and bran (31.25–33.75 g/L), suggesting that an optimal balance between these two substrates is critical for achieving peak enzyme yields.

3.3. Validation of the RSM Model

The RSM model was validated to ensure the reliability of the optimized conditions for pectinase production. The optimized conditions suggested by the RSM model for pectinase production were CaCl₂ at 0.437 g/L, orange peel powder at 4.223 g/L, (NH4)₂SO₄ at 15.761 g/L, and bran at 33.590 g/L. To verify these predictions, a validation experiment was conducted under the optimized conditions. The experimentally observed pectinase activity was 20.505 U/mL, which closely approximated the predicted response of 20.645 U/mL. This close agreement between the predicted and actual values confirms the robustness of the RSM model. The optimized conditions demonstrated that the response values were indeed close to the model predictions, effectively validating the strategy used for enzyme production optimization. The R-squared (R²) values, which are commonly accepted metrics for assessing the fit of statistical models, indicated a high degree of correlation between the observed and predicted responses. These reasonable R² values describe the actual behavior of the statistical system, affirming its applicability for interpolation within the experimental domain.

4. Discussion

The present study successfully utilized silkworm excrement as a substrate for pectinase production using Aspergillus niger through submerged fermentation. The optimization of the fermentation medium components and conditions was achieved using single-factor experiments combined with RSM. This approach not only maximized pectinase activity but also demonstrated the potential of this byproduct for sustainable enzyme production and waste management in the sericulture industry.

4.1. Optimization of Fermentation Medium Components

In this study, silkworm excrement served as the primary substrate for pectinase production via fermentation. Previous experiments investigated how varying concentrations of silkworm excrement influenced pectinase yield. Enzyme activity reached its maximum at 60 g/L. Lower concentrations provided inadequate nutrients, limiting enzyme production, while higher concentrations caused clumping, hindering microbial access and maintaining low enzyme activity. Based on these prior findings, we set the silkworm excrement concentration at 60 g/L for subsequent experiments. The optimization study then concentrated on refining other medium components to enhance enzyme output, while keeping silkworm manure as the main substrate. The single-factor experiments indicated that the supplementation of CaCl₂, orange peel powder, and (NH₄)₂SO₄ had positive effects on pectinase production, while an excessive amount of these components led to a decline in enzyme activity. These findings are consistent with previous studies showing that certain mineral elements and organic acids can enhance microbial enzyme production. Calcium ions are crucial for maintaining cell membrane integrity and stabilizing enzyme structures, thereby improving their activity. This is supported by a study by Ketipally et al. (2018) [16], which demonstrated that calcium ions can enhance enzyme activity by interacting with specific amino acid residues in the enzyme structure. Similarly, organic acids, such as those found in orange peel powder, act as carbon sources and chelate metal ions, promoting enzyme synthesis. AbdRahman NH et al. (2024) reported that organic acids can provide essential carbon sources for microbial growth and enzyme production, and they can also chelate metal ions, which are often required as cofactors for enzyme activity [17]. Additionally, (NH₄)₂SO₄ has been found to improve enzyme production by providing a nitrogen source and stabilizing the enzyme structure [16,18].

4.2. Interaction Analysis and Model Validation

The RSM analysis further confirmed the significant impact of the interaction between different medium components on pectinase production. The regression model generated from the BBD provided a clear relationship between the four variables (CaCl₂, orange peel powder, (NH₄)₂SO₄, and bran) and pectinase activity. While Factor C [(NH₄)₂SO₄] and the AD interaction exhibited non-significant main effects, their retention aligns with the hierarchy principle of RSM, ensuring model completeness and interpretability. The significant quadratic term for (NH₄)₂SO₄ (C², p < 0.0001) underscores its nonlinear impact on enzyme activity, reflecting substrate inhibition at elevated concentrations. Non-significant terms (e.g., AD) were retained to maintain model structure, as their exclusion did not enhance predictive power but risked omitting potential mechanistic insights.

The optimal concentrations identified by the model were CaCl₂ at 0.437 g/L, orange peel powder at 4.223 g/L, (NH₄)₂SO₄ at 15.761 g/L, and bran at 33.590 g/L. These results highlight the importance of balancing the nutrient composition of the medium to achieve maximum enzyme production. The optimization of medium components is crucial for enhancing enzyme production, as it ensures that all necessary nutrients are available in optimal quantities to support microbial growth and enzyme synthesis. These findings are consistent with previous studies that have demonstrated the importance of optimizing medium components for enzyme production. For instance, a study by Ketipally and Ram (2018) showed that the optimization of medium components, including CaCl₂ and (NH₄)₂SO₄, significantly improved the production of pectinase [16]. Similarly, de Alencar Guimarães et al. (2023) reported that the addition of organic acids, such as those found in orange peel powder, enhanced the production of pectinolytic enzymes [19]. Additionally, Abd-Elhalem et al. (2015) highlighted the role of bran as a carbon source in optimizing enzyme production [20]. The RSM analysis and BBD model provided valuable insights into the optimal concentrations of medium components for maximum pectinase production. These results underscore the necessity of a balanced nutrient composition in the medium to achieve high enzyme yields, which is essential for industrial applications.

The validation experiment showed that the predicted pectinase activity of 20.645 U/mL closely matched the experimentally observed value of 20.505 U/mL, confirming the robustness and accuracy of the RSM model. This validation step is crucial as it ensures that the optimized conditions can be reliably applied in industrial-scale processes [21]. The validation of the RSM model is particularly important in industrial applications because it ensures that the optimized conditions are reproducible and reliable. This step helps in minimizing the risk of process failure and ensures consistent enzyme production, which is critical for commercial applications. The successful validation of the model in this study suggests that the optimized conditions can be effectively translated to larger-scale production, potentially leading to significant cost savings and improved process efficiency.

4.3. Practical Applications and Environmental Sustainability

In terms of practical applications, the use of silkworm excrement as a substrate offers several advantages. It provides a cost-effective and readily available source of nutrients for microbial growth, reducing the need for synthetic media components. Additionally, the utilization of this waste product minimizes environmental pollution associated with its disposal, aligning with the principles of circular economy and sustainable development. In the broader context of pectinase research, a comparison of our study with previous work using Aspergillus niger via submerged fermentation reveals both similarities and unique aspects. Li et al. (2020) induced fermentation with soybean shells, achieving pectinase activity of 6.36 U/mL [22], while Wagh et al. (2022) reported 99.21 U/mL after optimization using agricultural waste orange peel [23]. Esawy et al. (2022) achieved 40 U/mL under optimized conditions using a citrus pectin-based medium [24]. These studies, like ours, highlight the significant potential of Aspergillus niger for pectinase production. However, our study differs in several key ways. First, we utilized silkworm excrement as the substrate, a novel and underutilized resource. Second, our research not only focused on enzyme production optimization but also emphasized its potential application in waste management systems. These unique aspects provide new insights into the sustainable production of pectinase and its practical applications. By converting a waste material into a valuable resource, this approach not only reduces production costs but also promotes environmental sustainability, making it an attractive option for industrial-scale enzyme production [25].

The global pectinase market, valued at $41.2 billion in 2022, is driven by demand in food processing and biofuels [9]. Our method not only reduces reliance on expensive synthetic media but also offers a sustainable alternative for industries seeking cost-effective enzyme production. The use of agricultural and industrial byproducts as substrates for microbial fermentation has gained significant attention in recent years. These byproducts are often rich in organic matter and can serve as excellent carbon and nitrogen sources for microbial growth, thereby reducing the reliance on expensive synthetic media [26,27].

For instance, studies have shown that various agricultural residues, such as orange peel, banana peel, and wheat bran, can be effectively used for the production of enzymes like cellulase, pectinase, and amylase [28,29,30]. Similarly, silkworm excrement, which is rich in organic compounds, can provide essential nutrients for microbial fermentation, enhancing the production of target enzymes. Moreover, the utilization of silkworm excrement addresses environmental concerns related to its disposal. The accumulation of untreated waste can lead to soil and water pollution, contributing to environmental degradation [31]. By repurposing this waste as a substrate for enzyme production, we can significantly reduce the environmental impact associated with its disposal. This approach aligns with the principles of the circular economy, which emphasizes the importance of minimizing waste and maximizing resource efficiency. Sustainable development goals, particularly those related to environmental protection and economic growth, can be achieved through such innovative practices.

5. Conclusions

This study validates silkworm excrement as a sustainable substrate for pectinase production via Aspergillus niger submerged fermentation, achieving 20.5 U/mL activity under optimized conditions—a 26% enhancement over baseline performance. Key innovations include the isolation of the silkworm-adapted strain JA026 and pioneering RSM-BBD modeling of critical component interactions (e.g., CaCl₂/orange peel synergy). However, challenges remain regarding industrial translation: scale-up from 250 mL flasks to industrial bioreactors requires validation of oxygen transfer dynamics; seasonal variability in excrement composition may impact process reproducibility; and economic viability hinges on cost-effective sourcing of orange peel powder (4.223 g/L). Further, downstream processing requirements and integrated optimization of physical parameters were not explored. Nevertheless, by addressing sericulture waste valorization and industrial enzyme demand, this work establishes a scalable circular bioeconomy framework with transformative potential for food processing and bioremediation sectors, aligning economic viability with environmental sustainability pending resolution of these constraints.