Optimization of fungal chitosan production from Cunninghamella echinulata using statistical  designs

Abstract

Fungal chitosan (FCH) is superior to crustacean chitosan (CH) sources and is of immense interest to the scientific community while having a high demand at the global market. Industrial scale fermentation technologies of FCH production are associated with considerable challenges that frequently restrict their economic production and feasibility. The production of high quality FCH using an underexplored fungal strain Cunninghamella echinulata NCIM 691 that is hoped to mitigate potential future large-scale production was investigated. The one-factor-at-a-time (OFAT) method was implemented to examine the effect of the medium components (i.e. carbon and nitrogen) on the FCH yield. Among these variables, the optimal condition for increased FCH yield was carbon (glucose) and nitrogen (yeast extract) source. A total of 11 factors affected FCH yield among which, the best factors were screened by Plackett–Burman design (PBD). The optimization process was carried out using the response surface methodology (RSM) via Box-Behnken design (BBD). The three-level Box– Behnken factorial design facilitated optimum values for 3 parameters—glucose (2% w/v), yeast extract (1.5% w/v) and magnesium sulphate (0.1% w/v) at 30˚C and pH of 4.5. The optimization resulted in a 2.2-fold higher FCH yield. The produced FCH was confirmed using XRD, ¹H NMR, TGA and DSC techniques. The degree of deacetylation (DDA) of the extracted FCH was 88.3%. This optimization process provided a significant improvement of FCH yields and product quality for future potential scale-up processes. This research represents the first report on achieving high FCH yield using a reasonably unfamiliar fungus C. echinulata NCIM 691 through optimised submerged fermentation conditions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-03919-6.

Introduction

The increased use of synthetic polymers in everyday life has resulted in greater unintentional release of various toxic chemicals into the environment. Researchers have been emphasizing the utilization of green chemistry approaches towards designing, developing, production and advancement of biopolymers and biomaterials (Sharma et al. 2023; Karamchandani et al. 2022a; Banat et al. 2021). With the incessant consumption and expanding demand for biopolymers production, the global market for biopolymers is expected to reach around 4.9 billion USD by 2024, with an annual growth rate of 15.6% over the analysis period (Report on alternative products and technologies to plastics and their applications 2022 NITI Aayoghttps://www.niti.gov.in).

‘Chitosan’ (CH) is one such biopolymer that has varied applications in the industrial sector. The term was introduced by Felix Hoppe-Seyler in 1894 (Crini 2019) and since then it has been expanded for several aspects. Historical evidence for CH appeared since 1859 when Charles Rouget, a French physiologist demonstrated that the product obtained after deacetylation of chitin (CT) (through boiling in presence of alkali) was soluble in acidic solutions (Crini 2019). CH is the N-deacetylated form of CT, which is the second most abundant biopolymer present on our planet after cellulose. The molecular formula for each monomer of CH is C₆H₁₁O₄N with one primary amine (− NH₂) and two free hydroxyl (-OH) groups. The hydroxyl group at C-2 position in CH has been replaced by acetamido or amino groups which makes it different from its analogue cellulose.

Due to the presence of the nitrogen element (6.89%) and many other functional groups, CH is being investigated expansively. The commercial pertinence of CH is evident as its utility is found in diverse applications ranging from biomarkers to quantum dots due to its porosity and unique structural integrity (Kumar et al. 2020). CH allows its modification into different derivatives that enhances its application at a broader industrial scale such as agriculture and agro-based industry, textile, food and nutrition, human and veterinary medicine, pharmaceuticals, drug delivery, tissue engineering, biotechnology, material science, environmental protection and many more (Karamchandani et al. 2022b; Ke et al. 2021). Various forms of CH such as oligo-CH, irradiated CH, nano-CH and CH nanodots are quite important in agriculture as plant elicitors (Wang et al. 2023; Sun et al. 2023). CH is extensively used due to its degradable abilities into non-toxic residues (Karamchandani et al. 2022c). Remarkably, the market for CH has been mounting and valued at USD 10.88 billion in 2022 and is projected to expand at a compound annual growth rate of 20.1% from 2023 to 2030 (Chitosan market size, share and trends analysis report by application 2023https://www.grandviewresearch.com/industry-analysis/global-chitosan-market).

Commercially CH is extracted from crustaceans’ shells like shrimps, crayfish, crab, molluscs such as oysters, squids and cuttlefish (marine sources), exoskeletons of insects such as cockroaches, brachiopods (terrestrial sources) which has seasonal limitations. The extracted CT/CH from conventional sources has further limitations since the properties of this biopolymer vary between batches. The inability to obtain it in a highly deacetylated form restricts applications in the biomedical sector. The presence of higher levels of calcium carbonate in them requires carrying out a demineralisation process. The generation of toxic residues during the CH extraction process from crustacean has led towards exploring new microbial sources with innovative approaches (Huq et al. 2022).

The presence of CH in certain fungal species has supported the progress of discovering promising alternative routes for its production. The fungi belonging to the ‘Zygomycetes Class’ are a rich and alternative source for CH production; as they lack seasonal limitations (Ghormade et al. 2017). Fungal chitosan (FCH) is found in the cell wall as free CT or CH. The fungal sources are advantageous as a valuable substitute due to their more consistent product characteristics. Furthermore, waste materials are frequently used to regenerate liquid and solid production media for fungal growth (to produce FCH), which allows the application of circular economy principles and enhances the process economics (Crognale et al. 2022). FCH has low polydispersity index which aids its utilization as an efficient scaffolding material in constructing templates for biodegradable tissue regeneration (Huq et al. 2022). Thus, for biomedical and food products, FCH serves as a perfectly suitable candidate. Our present search thus intends to explore and realize the potential of FCH for unique advantages.

The industrial level FCH production requisite employment of strategic approaches to achieve high yield of microbial bioactive compounds. One-factor-at-a-time (OFAT) is being used conventionally to identify the critical factors involved in optimizing the fermentation parameters. However, certain challenges like availability of the resources, time involved in fermentation processes through OFAT approach hinders its wider application in microbial technology. Nevertheless, currently available statistical approaches reasonably supports designing media, optimizing fermentation parameters in limited periods (Singh et al. 2017). Efforts towards transforming the fermentation process into a well-organized scheme is therefore mandatory in order to reduce the number of obligatory experiments. This is being executed using the Design of Experiment (DoE) application that permits identification of the most important factors from a list of many potential ones (Bezerra et al. 2008; Politis et al. 2017). The objective of DoE includes the screening process using Plackett–Burman design (PBD) or Taguchi design in determining the most crucial factor/s. PBD identifies significant factors in two-level multi-variate experiments. Followed by screening, the fermentation parameters are optimized using Box-Behnken design (BBD), Central Composite Design (CCD), Full factorial design, Fractional factorial design of RSM.

The present work investigates FCH extraction from Zygomycetes fungus Cunninghamella echinulata. Typically, C. echinulata has been isolated from soil samples under laboratory conditions (Silva et al. 2014). The carbon, nitrogen sources, inorganic salts along with optimum growth parameters are crucial for fermentative production of FCH at lab-scale. We have performed optimization studies to achieve high biomass with a substantial amount of FCH from C. echinulata using the OFAT approach for the screening and identification of the best suitable carbon and nitrogen sources. This was followed by statistical analysis through the RSM approach based on the BBD. Minitab® 19 Statistical Software (Minitab, LLC, Pennsylvania, USA) was employed for RSM designing and evaluating factor/s influencing FCH production. Furthermore, the extracted FCH was validated using analytical techniques such as X-Ray Diffraction (XRD), Nuclear Magnetic Resonance Spectroscopy (NMR), Thermogravimetric Analysis (TGA) and Differential Scanning Calorimetry (DSC).

Materials and methods

C. echinulata NCIM 691 was procured from National Collection of Industrial Microorganism (NCIM) Pune, Maharashtra, India. The culture was grown, maintained on PDA at 29 ± 1 °C and stored at 4 °C. The pure quality and dehydrated media components like malt extract glucose yeast extract peptone (MGYP), potato dextrose broth/agar (PDB/PDA), Mueller–Hinton broth/agar (MHB/MHA) (Himedia, Mumbai, India) were used. Components namely glucose, galactose, sucrose, fructose, peptone, yeast extract, soya meal, tryptone, ammonium sulphate, dihydrogen potassium phosphate, sodium chloride, calcium chloride, magnesium sulphate (HiMedia, Mumbai, India) were used. Whatman™ no. 1 paper was procured from GE Healthcare Life Sciences, UK. Other chemicals like sodium hydroxide, acetic acid, hydrochloric acid, ethanol and acetone were purchased from Sisco Research Laboratories Pvt. Ltd. India. Low molecular weight CH (LMWCH) (50–190 kDa, DDA 75–85%), Deuterium chloride (DCl) and Deuterium oxide (D₂O) were procured from Sigma-Aldrich, USA.

Media components and culture conditions for C. echinulata NCIM 691

C. echinulata NCIM 691 was revived by scraping growth from PDA plate and grown in PDB for up to six days. The spore suspension (1.0 × 10⁸ spores/ml) was made by gently scraping the culture in Phosphate Buffer Saline (PBS – pH 7.0). The spore suspension was inoculated into 250 ml Erlenmeyer flasks with 100 ml of culture media at 29 ± 1 °C for 16 h on a rotary shaker (100 rpm). Further, 7.5% (v/v) of the inoculum was aseptically transferred to the 500 ml Erlenmeyer flasks containing fermentation medium (100 ml) supplemented with various carbon sources of 2% (w/v) (glucose, galactose, sucrose, fructose); nitrogen source of 2% (w/v) (peptone, yeast extract, soya-meal, tryptone) along with 0.5% (NH₄)₂SO₄; 0.1% K₂HPO₄; 0.1% NaCl; 0.01% CaCl₂.H₂O and 0.05% MgSO₄·7H₂O at pH 4.5. Flasks were incubated at 29 ± 1 °C under 100 rpm agitation condition for 6 days.

Optimizing fermentation parameters through one-factor-at-a-time approach

The fermentation conditions required for FCH production were carried out to determine the optimum batch time (6–12 days), pH range (4.5–6.0) and temperature (25 to 30 °C) to produce high biomass and attain substantial yield of biopolymer. Biomass was harvested, filtered through filter paper (Whatman™ no. 1), washed using sterile distilled water and dried (60 °C) until the constant weight was achieved to determine the dry cell weight (DCW).

Determining the most suitable carbon source/s and concentration/s to achieve highest fungal biomass and FCH yields

Four different carbon sources viz., glucose, galactose, sucrose and fructose individually at concentrations of 1, 1.5 and 2% (w/v) were tested to identify the most suitable and optimal concentration required for production of FCH. While varying concentration and type of carbon source, concentrations of all the other media components were kept constant. All the physicochemical parameters were also kept constant during the conduct of the experiment. Fungal culture was inoculated in fermentation broth and incubated for 6 days at 29 ± 1 °C. Further FCH extraction was carried out using an alkali insoluble method (Pochanavanich and Suntornsuk 2002). DCW and FCH yield were determined for all carbon sources at different concentrations.

Determining the most suitable nitrogen source/s and concentration/s to achieve highest fungal biomass and FCH yields

Like carbon sources, we also screened suitable nitrogen sources for enhancing the biomass as well as yield of FCH. Different nitrogen sources like peptone, tryptone, yeast extract and soya meal at concentrations 1, 1.5 and 2% (w/v) were used individually or in combinations. While varying concentrations and type of nitrogen sources; all the other media components and their concentrations were kept constant. Fungal culture was inoculated in customized fermentation broth and incubated for 6 days at 29 ± 1 °C. Further FCH extraction was carried out using an alkali insoluble method. DCW and FCH yield were determined for all given conditions.

Experimental design and data analysis

Plackett–Burman Design (PBD)

The selection of an appropriate carbon and nitrogen source along with their concentrations were carried out using PBD (Plackett and Burman 1946). The PBD facilitates the identification of the crucial variables or parameters influencing the FCH production. PBD emphasizes the conduct of the total number of trials as N + 1 where ‘N’ denotes the number of factors to be considered for the experiments. All the factors were given a two-level (high and low) definition and further assigned as + 1 or—1 value (El-Far et al. 2021). 11 factors (Table 1) with around 28 randomized experimental runs were investigated considering the high- and low-level value of each factor (Supplementary Table S1). These were further designed to select the most important factors responsible for maximum FCH production.

Table 1.

Experimental range, levels of independent nutritional and physical variables included for production of chitosan from C. echinulata NCIM 691

No of factor	Factor (Variable)	Levels of factor		Unit
		Low level (–1)	High level (+ 1)
1	Carbon source: Glucose	10	20	g/l
2	Nitrogen source: Peptone	5	10	g/l
3	Nitrogen source: Yeast extract	10	20	g/l
4	Ammonium sulphate	5	10	g/l
5	Dipotassium hydrogen phosphate	1	5	g/l
6	Sodium chloride	1	5	g/l
7	Calcium chloride	0.5	1	g/l
8	Magnesium sulphate	0.5	1	g/l
9	pH	4.5	6	-
10	Agitation	100	120	rpm
11	Incubation time	6	12	days

Box-Behnken Design

The Box-Behnken Design experiment (Box and Wilson 1951) consisted of 15 trials and 3 independent screened variables at 3 different levels i.e. low (− 1), high (+ 1) and medium (0). All the experiments were carried out in triplicate and the average FCH yield was considered as response (Y) (Abdel-Gawad 2017). The mutual interaction among the variables and their corresponding optimum levels were expressed by a second order polynomial equation. A generalized form of this equation used was as follows,

$$\text{Y}={\beta }_o+\sum {\beta }_i{X}_i+\sum {\beta }_{\mathit{ii}}{X}_i^2+\sum {\beta }_{\mathit{ij}}{X}_i{X}_j$$

where ‘Y’ denotes the response; X_i and X_j represents the independent variables; β_o denotes the intercept (regression coefficient of the model); β_i, β_ii, and β_ij are the linear, quadratic and interaction coefficients, respectively.

Statistical analysis

The experimental data obtained were statistically analysed using BBD through Minitab® 19 Statistical Software (Minitab, LLC, Pennsylvania, USA) to determine the significant differences (p ≤ 0.05) in response under different conditions. The contour and 3-dimensional surface plots were constructed to visualize the interaction between significant variables and their optimal values. The goodness of fit of the model was evaluated through the determination of coefficient (R²) and the analysis of variance (ANOVA). The validation of statistical significance was performed through F—test.

Extraction of  fungal chitosan

The biomass of C. echinulata NCIM 691 was dried continuously to achieve a constant weight and homogenized subsequently. After complete drying of the fungal biomass, alkali treatment was carried out to extract the materials like glucan and protein. The dried mycelia were treated with 1 N NaOH solution (1:30 w/v ratio) followed by autoclaving at 121 °C for 20 min. The alkali insoluble materials (AIM) were then centrifuged at 10,000 rpm for 15 min to obtain a pellet containing FCH. This was washed further with distilled water, until the pellet was completely neutralized. The washed pellet was then treated with 1% acetic acid (1:40, w/v) at 95 °C for 6 h. The FCH released into the supernatant was extracted from this acid insoluble fraction. The FCH was precipitated subsequently from the supernatant by adjusting pH to 10.0 with 1 N NaOH. The precipitate was centrifuged further at 10,000 rpm for 15 min. Afterward, the extracted FCH was washed with distilled water to attain a neutral pH (7.0), followed by treatment with 95% (v/v) ethanol (1:20 v/v) and acetone (1:20 v/v). After completing the extraction procedure, FCH was dried at 60 °C for 24 h to get rid of water. The dry weight was measured to calculate FCH yield. The alkali-insoluble precipitate obtained after suitable alkali and acid extraction processes contained FCH (Muzzarelli et al. 1994) which was utilized further for analytical characterization.

Physico-chemical characterization of fungal chitosan

The FCH extracted through submerged fermentation (SmF) was characterized through analytical techniques using X-Ray Diffraction (XRD), Nuclear Magnetic Resonance (NMR) Spectroscopy, Thermogravimetric Analysis (TGA), and Differential Scanning Calorimetry (DSC) as described below:

X-Ray diffraction

The extracted FCH was subjected to XRD analysis using X-ray diffractometer (Kβ filter 1D for Cu; voltage = 45 kV, intensity = 40 mA) and diffraction pattern was measured (Divya et al. 2017). The X-ray diffractometer includes a 2θ goniometer (Bruker, Germany, D8 QUEST SCXRD system). The diffraction pattern was used to identify the sample’s crystalline phases, the structural properties (with great precision), size and the orientation of crystallites.

Nuclear magnetic resonance spectroscopy

The ¹H NMR was carried out to determine the DDA of FCH sample. The samples were prepared using 10 mg of FCH in a solution containing 1.96 mL of D₂O and 0.04 mL of deuterated HCl with continuous stirring for half an hour to confirm the complete dissolution. FCH samples were lyophilized and dissolved in 1 mL of D₂O. Approximately 600 μL of the sample was used for the ¹H NMR spectra. All the ¹H NMR spectra were taken on a Bruker AVANCE III HD 500 MHz spectrophotometer. The ¹H NMR spectra were obtained at 70 °C without any interference from the solvent (HOD) with any of the CH peaks (Mane et al. 2022). The DDA of FCH was calculated using following equation:

$$\text{DDA}(\%)=\left(\frac{H1D}{H1D+HAc/3}\right)\times 100$$

where, H1D is the peak of proton H1 of the deacetylated monomer. HAc is the peak of the 3 protons of the acetyl group.

Thermogravimetric analysis

This method provides thermal analysis of a sample under the influence of altered temperature conditions. The information about physical phenomena including absorption, adsorption, phase transitions and desorption can be acquired through TGA analysis. Additionally, information about chemical phenomena comprising chemi-sorptions, solid–gas reactions and thermal decomposition was obtained. The TGA was carried out using aTGA-60H (Shimadzu, Japan) at 40–600 °C in an inert environment of nitrogen at a heating rate of 20 °C/min with an empty reference pan (Claverie et al. 2023). Thus, the TGA curves were observed to govern the thermal stability along with decomposition temperature of LMWCH and FCH samples.

Differential scanning calorimetry

This thermo-analytical method measures the difference in the amount of heat needed to raise the temperature of a sample and standard or reference compound. In this technique, both the test sample (FCH) and standard (commercial—LMWCH) were upheld at almost similar temperatures throughout the experiment. Temperature conditions maintained during the DSC analysis were gradually increased for the sample holder. The DSC (DSC Q10 V9.9 Build 303, Shimadzu Corporation, Japan) was carried out at 30–200 °C in an inert nitrogen environment under heating rate of 10 °C/min along with an empty reference pan (Ramasamy et al. 2014).

Results

Optimization of different parameters using one-factor-at-a-time approach

Determining the utmost factor influencing the production of FCH from C. echinulata was indispensable for scale-up purposes. The most significant carbon and nitrogen sources and their concentrations were identified well with the help of statistical tools and software. Various physico-chemical parameters (incubation period, pH, temperature) were also determined successfully and are described in the following sections.

Effect of different carbon sources on maximising fungal biomass with high chitosan content

Carbon, nitrogen sources and their respective concentrations influencing production of FCH from C. echinulata were acknowledged. Figure 1 depicts the effect of carbon sources on fungal biomass and FCH production. Among all the carbon sources at 1% (w/v) of glucose showed a maximum of 282 mg/l FCH production with DCW of 9.8 g/l, followed by galactose (124 mg/l, DCW of 8.5 g/l), sucrose (86 mg/l, DCW of 7.6 g/l) and fructose (217 mg/l, DCW of 8.4 g/l). It may be due to the fact that glucose is one of the easily assimilated carbon sources in the metabolic pathway during the growth of an organism as well as FCH biosynthesis. A gradual increase in the concentration of glucose from 1 to 2% (w/v) showed a positive effect on biomass and also the FCH yield. Therefore, the highest biomass with high FCH content was achieved at 2% (w/v) glucose (DCW 24.9 g/l; FCH- 386 mg/l), whereas, the other sources—galactose (128 mg/l, DCW of 20.6 g/l), sucrose (96 mg/l, DCW of 17.6 g/l) and fructose (254 mg/l, DCW of 20.3 g/l) resulted in lower yields.

Fig. 1.

Effect of different carbon sources at A: 1%, B: 1.5%, C: 2% and D: Merge plot depicting comparison between all carbon sources at varied concentrations (%) on chitosan production (mg/l) by C. echinulata. The fungal suspension—1.0 × 10⁸ spores/ml was made in phosphate buffer saline (pH 7.0) and inoculated into fermentation medium supplemented with four carbon sources individually at three different concentrations which were selected through One-factor-at-a-time (OFAT) approach. Culture was incubated at 29 ± 1 °C/100 rpm/6 days. Highest biomass and chitosan production were observed in glucose followed by galactose, sucrose and fructose. Comparison between all four carbon sources depicted a positive effect on biomass and chitosan with gradual increase in the concentration of glucose

Effect of different nitrogen sources and concentrations in achieving maximum biomass and high chitosan yield

CH is basically a nitrogen containing biopolymer and is also a deacetylated form of CT. Therefore, the C/N ratio needs to be in an appropriate proportion to produce optimum FCH from the microbial fermentation processes. Fungi need organic or inorganic nitrogen sources as sole nutrient components to synthesize CT and CH in their cell wall. From the organic and inorganic nitrogen sources tested, the former one exhibited a prominent effect on FCH production. Supplementary Fig. S1 depicts the prominent effect of yeast extract on FCH production as compared to other nitrogen sources. Among all the nitrogen sources at 1.5% (w/v), yeast extract showed a maximum of 549.06 mg/l of FCH production with DCW of 12.7 g/l followed by tryptone (457.3 mg/l, DCW of 19.8 g/l), peptone (331.6 mg/l, DCW of 10.1 g/l) and soya meal (256.3 mg/l, DCW of 11.23 g/l). Gradual increase in yeast extract from 1 to 2% (w/v) showed positive effect on the FCH yield only. In conclusion, the highest FCH content (607 mg/l) was achieved at 2% (w/v) of yeast extract with DCW of 12.71 g/l. Also, among the different combinations of P (Peptone)/Y (Yeast extract) ratios, the maximal FCH production (650.2 mg/l) and DCW (18.98 g/l) was observed for the ratio P:Y at 1:2. Furthermore, with varied ratios of P:Y i.e., 0.5:0.5 and 1:1, a gradual decline in both FCH production and DCW was observed.

Experimental design and statistical data analysis

Screening the factors influencing FCH production using Plackett–Burman Design

The Plackett–Burman Design was used to identify the most crucial conditions or factors influencing the growth of fungus and yield of FCH. The results of FCH yield (response) of the different trials (28 experimental runs) in coded values together with the actual response are shown in Supplementary Table S2. Based on the outcome of the PBD, the Pareto chart illustrated the order of significance of the variables affecting the FCH yield (Fig. 2). Among the 11 parameters included; glucose, yeast extract and magnesium sulphate unveiled positive effect on FCH yield (p < 0.05). ANOVA test depicting the screened factors responsible for FCH production are shown in the Supplementary Table S3.

Fig. 2.

Pareto chart depicting the factors responsible for optimum yield of chitosan (g/l) from C. echinulata. The Plackett–Burman Design (PBD) was used to identify the most significant factors affecting the growth of the fungus and chitosan yields (response) through 28 experimental runs in coded values together with the actual response. The order of significance of the variables affecting the chitosan yield is depicted from PBD, illustrated in Pareto chart (extracted from Minitab software). Among 11 factors—glucose, yeast extract and magnesium sulphate unveiled positive effect on chitosan yield form the fungus

Response surface methodology using Box-Behnken design matrix

About 15 experiments (45 runs in triplicate) were designed using BBD matrix with different combinations of 3 independent parameters (glucose, yeast extract and magnesium sulphate). The design with 3 levels demonstrated the combined effects of those factors responsible for optimum yield of FCH. The variables and their coded levels are illustrated in Supplementary Table S4. The BBD matrix of independent variables in coded units along with experimental values for FCH production is shown in Supplementary Table S5. The significance of each coefficient is checked by p-values. This is essential to know the mutual interactions pattern between the tested variables as significance and effectiveness of process parameters is determined by lower p-values. This value of each independent variable is < 0.05. Model terms with p values < 0.05 were considered significant, whereas those with p values > 0.05 were regarded as insignificant. ANOVA analysis indicated that magnesium sulphate was a highly significant factor that influenced FCH yield (p = 0.000) (Supplementary Table S6). The model’s fitting validity was examined by the coefficient of determination (R²) and found to be 67.01%, and the model adjusted R² (R²adj) was 56.01%. The statistical analysis of all coded coefficients of the main effect of each tested variable on FCH yield was obtained. The T-value in Table 2 denote the T-test statistics to test the significance of the coefficient in the model. If T-test is applied, then the null hypothesis is that the coefficient has value zero. The T-test statistics denotes the difference between the observed values of the coefficient minus the value of coefficient under null hypothesis divided by the standard error of the estimate. In this case, the value of the coefficient under null hypothesis is zero. Hence, the T-value here is just the ratio of the observed coefficient value divided by its standard error. Among the independent models Variance Inflation Factor (VIF) measured multicollinearity. The present data signifies that the VIF value was equal to 1 indicating that the factors are not correlated (Table 2). The data obtained from the response optimizer indicated approximately 2.2 folds enhanced FCH yield when concentrations of glucose, yeast extract and magnesium sulphate were 20, 15 and 1.0 g/l respectively. The three-dimensional response surface plot and the corresponding 2 D contour plot were plotted to determine the optimum level of each variable and the effect of their interactions on FCH yield (mg/g). The hold value obtained from 2 D contour plot for magnesium sulphate was 0.75 (g/l); for glucose is 15 (g/l) and for yeast extract is 15 (g/l) (Fig. 3). Figure 4 illustrates the similar interaction in the form of the surface plots.

Table 2.

Statistical analysis of Coded Coefficients of the main effect of each variables on FCH yield using BBD

Term	Coef	SE Coef	T-value	P-value	VIF
Constant blocks	89.53	3.08	29.06	0.000
Glucose	3.84	1.89	2.04	0.050	1.00
Yeast extract	3.85	1.89	2.04	0.049	1.00
Magnesium sulphate	9.24	1.89	4.90	0.000	1.00
Glucose*Glucose	5.02	2.78	1.81	0.080	1.01
Yeast extract*Yeast extract	− 4.40	2.78	− 1.58	0.123	1.01
Magnesium sulphate*Magnesium sulphate	7.63	2.78	2.75	0.010	1.01
Glucose*Yeast extract	5.48	2.67	2.05	0.048	1.00
Glucose*Magnesium sulphate	9.46	2.67	3.54	0.001	1.00
Yeast extract*Magnesium sulphate	5.39	2.67	2.02	0.051	1.00

Fig. 3.

Contour plots depicting the relative effect of A: yeast extract and glucose, B: magnesium sulphate and glucose (g/l) on chitosan production from C. echinulata. The contour plots illustrating the interaction between significant variables and their optimal values for fungus growth and chitosan production. The hold value obtained from 2 D contour plot for magnesium sulphate was 0.75 (g/l); for glucose is 15 (g/l) and for yeast extract is 15 (g/l). The plots were extracted by Box-Behnken design using Minitab software. Variation in colours—light to dark green/blue in surface plots depicts the region of highest chitosan production by the fungus. Plots aid in determining the impact of two factors on chitosan production

Fig. 4.

The 3-dimensional surface plots depicting the relative effect of A: yeast extract and glucose, B: magnesium sulphate and glucose (g/l) on chitosan (mg/g) production from C. echinulata. The plots were extracted by Box-Behnken design (BBD) using Minitab software. The upper edge of the plot depicts the area of maximum chitosan production by the fungus

The model summary and regression equation in Uncoded Units is described below:

Model summary
S	R-sq	R-sq(adj)
9.24286	67.01%	56.01%	39.62%

Regression equation:
Fungal Chitosan (FCH) (mg/g)	= 295.9—14.21 Glucose—0.47 Yeast extract—324.5 Magnesium sulphate
	+ 0.201 Glucose*Glucose—0.176 Yeast extract*Yeast extract
	+ 122.1 Magnesium sulphate*Magnesium sulphate
	+ 0.219 Glucose*Yeast extract + 7.56 Glucose*Magnesium sulphate
	+ 4.31 Yeast extract*Magnesium sulphate

Characterization of  fungal chitosan

X-Ray diffraction

The diffraction pattern of LMWCH (which was used as a reference) showed two peaks at 2θ = 19.86 and 2θ = 10.34 indicating the crystalline nature of this biopolymer. Similarly, the diffraction pattern of FCH was also observed at 2θ = 13 and 2θ = 9.5. This confirmed the structural similarity between the LMWCH (Fig. 5A) and FCH (Fig. 5B).

Fig. 5.

A X-ray powder diffraction patterns of A Commercial—Low molecular weight chitosan (LMWCH), B chitosan extracted from C. echinulata. C 1H NMR spectra of fungal chitosan (FCH). The diffraction pattern of the chitosan samples was noted over a 2θ range of 5°–40° to identify the crystallinity. The diffraction pattern of LMWCH showing two peaks at 2θ = 19.86 and 2θ = 10.34. A similar diffraction pattern of FCH observed at 2θ = 13 and 2θ = 9.5. For 1H NMR analysis FCH was prepared in D₂O and deuterated. The peak observed at 1.990 ppm for a reference-acetyl group (H-Ac) and the peak from 3.113 to 4.803 ppm for protons and at 4.803 indicated H1D. The H1 of the deacetylated monomer and H-Ac indicate the peak of the 3 protons of the acetyl group

Nuclear magnetic resonance spectroscopy

The ¹H NMR was found to be supportive in indicating the DDA of FCH. Figure 5C shows the ¹H NMR spectra of FCH where the peak at 1.990 ppm was referenced as acetyl group (H-Ac). Whereas, the peak from 3.113 to 4.803 ppm was the signal from protons H2, H3, H4, H5 and H6. The peak at 4.803 was found for H1D. Here, H1 of the deacetylated monomer and HAc is the peak of the 3 protons of the acetyl group. The DDA of FCH found was 88.3%.

Thermogravimetric analysis

The CH or FCH sample is a free base, small-molecule crystalline powder. Due to the strong affinity of polysaccharides for water, the first decomposition took place due to loss of water molecules. In LMWCH, first stage loss was 2.382% at 50–180 °C; whereas, for FCH the weight loss was 4.151% which remained stable up to 240 °C. The second decomposition resulted in weight loss of 53.875% (for LMWCH) and 53.854% (for FCH) which can be correlated with the pyrolytic decomposition of saccharide rings of the CH molecule which occurred at the temperature between 200 and 450 °C (Supplementary Figure S2).

Differential scanning calorimetry

The DSC curves of both LMWCH and FCH displayed a typical polysaccharide with two noticeable degradation profiles. The first endothermic peak obtained for LMWCH at 28.19 °C was 348.9 J/g; whereas, for FCH it was 450.5 J/g at temperature of 40.59 °C. The second endothermic peak initiated at 77.81 °C (for LMWCH) and 87.94 °C (for FCH) till 126.30 °C. The elevation in the baseline, corresponds to the degradation of CH due to its combustion (Supplementary Figure S3).

Discussion

A wide array of industrially important natural biopolymers originates from a myriad of microorganisms. The commercial utility of any metabolite is based on its yield and stability among many other characteristics (Shu et al. 2010). Nevertheless, these products are usually synthesized in small amounts by microbes and thus are unable to meet industrial needs (Demain 2000). Sustainable and commercial grade production in a cost-effective manner has numerous challenges which can be dealt with strain improvement, genetic manipulation and process optimization (Parekh et al. 2000). Therefore, commercial scale process optimization plays a vital role (Singh et al. 2017). Concurrently, components of the production media can be optimized through OFAT and DoE methods such as, Taguchi, PBD, BBD, CCD, Fractional factorial design to enhance the yield of microbial products. Considering the opportunities and inevitability of optimizing fermentation parameters, we investigated relatively underexplored C. echinulata, for FCH production through SmF. The media components for obtaining FCH from fungi facilitated in getting more consistent and desired physico-chemical properties as compared to crustacean origin CH.

Cunninghamella spp. are filamentous fungi that grows in soil and on plant material. The genus ‘Cunninghamella’ was established by Matruchot (1903) due to its pedicellate and uni-spored sporangia on the surface of the entire vesicle (Nguyen et al. 2017; Baijal and Mehrotra 1980). The fungal colonies exhibit rapid growth on solid media, attaining a diameter of 82–84 mm after 4 days at 25 °C (Baijal and Mehrotra 1980). Sporangiola and chlamydospores are oval shaped without any zygospores. Among 14 species, C. elegans, C. echinulata and C. bertholletiae contain high FCH concentrations. In a report by Junior et al. (2022) 9 isolates of C. elegans were examined for higher quantity of FCH by growing in Yeast extract peptone dextrose (YPD) medium through SmF at flask level. It is important to highlight that till today no optimization study has been reported for FCH production from C. echinulata. Recently, we reported FCH production from C. echinulata PDB (Karamchandani et al. 2022c) which encouraged us further to identify the independent significant media parameters positively influencing the growth of this fungus. Optimization of various media components and physiological conditions were necessary for substantial growth, fungal biomass and high yield of FCH. We found that out of 11; three factors—glucose (2% w/v), yeast extract (1.5% w/v) and MgSO₄ (0.1% w/v) enhanced FCH yield up to 2.2 folds (Fig. 3). The other media components like (NH₄)₂SO₄ 0.5%; K₂HPO₄ 0.1%; NaCl 0.1%; CaCl₂.H₂O 0.01% also enhanced FCH production when culture was grown at pH 4.5 and 30˚C for 6 days. We report around 220.45 ± 4.72 (mg/g of dry biomass) of FCH from C. echinulata under optimized fermentation processes. The C:N ratio (4:3) and other components indicated that the choice of media and nitrogen sources are critical factors in achieving a high yield of FCH. Earlier Shajahan et al. (2017) had extracted FCH from C. echinulata and was subsequently converted to CHNPs and reported efficient sorption of various commercial dyes. Tan et al. (1996) used nutrient broth for FCH production from C. echinulata, Gongronella butleri USDB 0428 and G. butleri USDB 0201. Among 3 fungi, G. butleri USDB 0201 produced the highest extractable FCH (93.4 mg/200 ml substrate) followed by C. echinulata (79.3 mg/200 ml substrate) and G. butleri USDB 0489 (76.6 mg/200 ml substrate). However, in terms of FCH yield per unit mycelia mass C. echinulata was reported as the best strain due to the ratio of extractable  CH to the mycelia mass yield was highest (7.14). Nevertheless, no optimization studies have been reported for C. echinulata so far.

Previously, several approaches such as OFAT and Full factorial/BBD/CCD have been used for FCH production from Mucor, Absidia, Rhizopus and Aspergillus species. Araki and Ito (1975), confirmed production of FCH from M. rouxii where media parameters (incubation period, agitation speed, pH and temperature) were optimized and reported high yield (39%) of DCW. Another Zygomycetes fungus—A. coerulea has been reported for FCH with a yield of 6.12 g/kg through OFAT and orthogonal array approach (Wang et al. 2008). Ahead of these conventional methods, currently the modelling and optimization software are being used to determine the critical, decisive factors influencing FCH production. Statistical models such as PBD, Taguchi design, full factorial/BBD/CCD has been variedly used by several researchers (Abo Elsoud et al. 2023; Bagy et al. 2022; Habibi et al. 2020; Zhang et al. 2014). In the other report, instead of media components; physico-chemical parameters such as alkali concentration, temperature and time were optimized using BBD. Authors used PDB with incubation of 7 days at 28 °C/150 rpm. The FCH yield, DDA and molecular weight (Mw) were 7.0% (w/w), 83.64% and Mw 2.70 × 10⁴ Da respectively (Abdel-Gawad et al. 2017). Applications of different combinations of optimization techniques have enabled the efficiency in fermentation processes. Industrial level productivity can be triggered by reducing the processing time, usage of cheap or renewable substrates and accurate quality control processes with low level of energy consumption and higher yield with low losses.

The prime purpose of the screening process was to identify the most vital factors or key variables that critically affected the response, while eliminating the insignificant ones. The selection of important factors in a screening design is critical to ensure optimal accuracy in subsequent experimentation. To accomplish this objective, Pareto charts have been used, as they allow variables to be sorted in the descending order of their importance. Thus, PBD was used as the screening design for identifying significant factors in two-level multi-variate experiments and detect the economical/main effects by neglecting the higher order interactions. Similarly, two-level full factorial designs, fractional factorial designs and Taguchi approach can aid in screening of vital factors. After the screening experiments, the favourable factors having significant effect on the fermentation process were identified and further optimization experiments were designed to determine the optimal levels of these key factors.

For optimisation studies, the BBD was utilized to determine the true optimal levels of the 3 independent variables viz. glucose, yeast extract and MgSO₄ concentrations. It is specifically formulated to accommodate a second-order regression model (quadratic model) by employing a 2² full factorial design, to generate experiments by systematically inserting a mid-level between the low and high levels of each factor ( – 1, 0, 1) unlike 5 levels associated with a CCD of RSM. BBDs represent an efficient RSM for evaluating the mean effects of experimental variables and overall experimental error using a minimum number of required runs besides maximal information delivery. Another commonly used CCD could be helpful in conducting experiments for relatively unknown processes or to find something novel. Nevertheless, for more refinement and optimization; BBD are well suited to explore variable response surfaces and identify the optimum conditions with higher precision (Vaingankar and Juvekar 2014). Thus, response surface designs are efficacious for fitting first-order models which detect linear effects. These designs can also provide indications regarding the existence of second-order effects, or curvature, when centre points are incorporated into the design.

Several researchers have used food waste substrates like apple pomace extract, corn steep liquor (CSL) and cassava wastewater (CW), beet molasses and sugarcane juice for FCH production from varied ‘Zygomycetes fungi’ (Habibi et al. 2020; Berger et al. 2014a; Amorim et al. 2006). Recently, Chatterjee et al. (2023) utilized fleshing waste of leather processing supplemented with yeast extract (0.3%), protein hydrolysate (1%) and glucose (2%) for FCH production from 3 fungi. Authors reported around 335, 239 and 212 mg/l of FCH from Mucor sp., R. oryzae and A. coerulea respectively. Namboodiri et al. (2022) employed SmF at lab-scale tray fermenter and reported FCH of 9 g/kg of rice straw. de Souza et al. (2020) checked the impact of CSL and CW water on the morphology and production of FCH from M. subtilissimus UCP 1262. Around 32.471 mg/g of FCH was achieved using 2² factorial design. The highest FCH production by Lichtheimia hyalospora UCP 1266 was 44.91 mg/g obtained at the central point. The authors confirmed the DDA of 80.29 and 83.61% of the FCH produced by M. subtilissimus (UCP 1262) and L. hyalospora (UCP 1266), respectively using FTIR.

Our previous publication (Karamchandani et al. 2022c) on characterization of C. echinulata originated FCH through UV–visible spectroscopy, DLS, zeta potential, FTIR and SEM was satisfactory. Thus, the present work can be considered as an extensive characterization of FCH through XRD, NMR, TGA and DSC from the same fungus. Analysis through XRD of FCH efficiently showcased the crystallinity of native and cross-linked pristine compound through visibility of two crystalline peaks ~ at 10° and 20° (2θ) (Fig. 5A and B). The endorsement of the amorphous nature of FCH without sharp diffraction in peaks was reasonable. Recently, Azeez et al. (2023) characterized FCH of C. echinulata through FTIR, molecular mass distribution, DDA and crystallinity. With respect to FCH characterization through XRD technique; our findings agree with Azeez et al. (2023) results.

In addition to XRD analysis; NMR is a formally validated technique that determines % of DDA. This analytical technique facilitates quality control and determines the sample content along with its purity during investigations. Information about molecular structure can be shown through the quantitative analysis of a test sample. Importance of FCH is also because of its high % of DDA as compared to CH of crustacean origin. Simplicity, speed and precision inspire commissioning ¹H NMR spectroscopy over other conventional techniques (e.g. IR or titration) for determining the DDA %. Other features like robustness, stability, specificity, and accuracy of NMR makes it as a highly appreciable technique for measuring the DDA % of biopolymers (Lavertu et al. 2003). CH possess a wide-range of DDA with numerous conformations in an aqueous system. Consequently, FCH having greater DDA bears elongated conformation with additional flexible chains where a strong repulsion of charges occurs in the biopolymer. With a low DDA, CH may show a spiral shape due to the low charge density in the chain of polymers (Budishevska et al. 2020). Generally, 60 to 97% DDA has been reported for FCH extracted from varied fungi. FCH having 94% DDA from Benjaminiella. poitrasii has been reported (Mane et al. 2017). Recently, a similar kind of DDA (94%) of FCH from Penicillium chrysogenum MZ723110 was reported by El-Far et al. (2021). These researchers had used PBD and CCD with increased yields (3.1-fold). FCH extracted by us from C. echinulata has 88.3% of DDA recommending its multifunctionality for diverse applications.

Like XRD, NMR techniques; TGA also proves to be quite supportive for analytical characterization of FCH (Claverie et al. 2023). The TGA curve of FCH showed first decomposition at 51.38 °C (Berger et al. 2014b). Similarly, our extracted FCH exhibited the first degradation at around 50 °C. Habibi et al. (2020) also reported the first TGA curve for A. terreus originated FCH with 7.5% weight loss (at 30–127 °C). Whereas, the second curve observed was at 210–350 °C with weight loss of 41.7%. The TGA spectrum of FCH extracted by us was thermally stable and thus agrees with the prevailing findings.

Characterization via DSC assured thermal stability of FCH that was evaluated previously through TGA technique. DSC allowed measurements of peculiar features of FCH including fusion and crystallization events along with glass transition temperatures (Tg). Researchers come across several challenges while determining Tg of FCH (Dong et al. 2004). Ahn et al. (2001) reported Tg of CH as 161 °C which is comparable and consistent with our DSC analysis of FCH. Higher value of Tg of the samples can be correlated to an upsurge in the number of OH- groups allowing formation of intermolecular hydrogen bonds. This observation is parallel with our findings. Finally, the foremost goal of our investigation related to physical, chemical and the Tg peak of FCH was observed at 87.94 °C. This work has enabled the optimization of FCH production process from the unexplored fungus i.e. C. echinulata NCIM 691 in accomplishing higher yield, quality product with lower investment of time as well as the energy consumption.

Conclusion

Extraction of FCH from fungal mycelia provides an extremely controllable route in achieving a pure and more consistent product. FCH production is a worthy alternative to diminish environmental pollution caused by strong alkali treatments used in traditional crustacean origin CH extraction processes. Additionally, amendment of FCH is obligatory in directing high quality FCH with anticipated properties (such as polydispersity, DDA and M_W) for multifunctional applications. The growth of fungi by deliberately varying process conditions significantly contribute to extraction of desirable FCH. The reproducible values of these parameters are crucial for the acceptance of FCH in  various sectors such as the medical and pharmaceutical sectors. However, the conventional approach due to the heterogeneous and non-uniformity of the raw material and the relative complexity of the extraction scheme becomes unreliable. Employment of amalgamated/integrated bioprocesses that produce FCH as a co-product from waste mycelia along with the prime product may be supportive to increase and boost the dispersion of this technology. The OFAT approach employed by us has been proved to be supportive to screen and identify the best suitable carbon and nitrogen sources required for optimum growth with substantial yield of FCH from C. echinulata. The RSM based optimization process via BBD assisted in designing and evaluation of the factors that enhanced the yield of FCH satisfactorily. Major limitations with BBD is that it allows one to consider fewer design points as compared to CCD with the same number of parameters. Additionally, it is important to highlight that BBD is not suitable if researcher is willing to conduct the experiments in sequential order. We believe this is the first report on media optimization and the third report on extraction of FCH from C. echinulata. Sequential two-step methodology accomplished through PBD tailed with BBD proved to be a suitable tool in optimizing fermentation parameters to produce FCH with high DDA which could be further up-scaled at commercial level.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

AIM

Alkali insoluble materials

ANOVA

Analysis of variance

BBD

Box-Behnken design

CCD

Central composite design

CH

Chitosan

CHNPs

Chitosan nanoparticles

CSL

Corn steep liquor

CT

Chitin

CW

Cassava waste water

D₂O

Deuterium oxide

DCl

Deuterium chloride

DCW

Dry cell weight

DDA

Degree of deacetylation

DLS

Dynamic light scattering

DoE

Design of experiment

DSC

Differential scanning calorimetry

FCH

Fungal chitosan

FT-IR

Fourier transform infrared spectroscopy

LMWCH

Low molecular weight chitosan

MGYP

Malt extract glucose yeast extract peptone

M_W

Molecular weight

NCIM

National collection of industrial microorganism

NMR

Nuclear magnetic resonance spectroscopy

OFAT

One-factor-at-a-time

PBD

Plackett–Burman Design

PBS

Phosphate Buffer Saline

PDB/PDA

Potato dextrose broth/agar

RSM

Response surface methodology

SEM

Scanning electron microscopy

SmF

Submerged fermentation

Tg

Glass transition temperature

TGA

Thermogravimetric analysis

VIF

Variance Inflation Factor

XRD

X-Ray diffraction

YPD

Yeast extract peptone dextrose

ZP

Zeta potential

Author contribution statement

BMK performed all the experiments in the laboratory and wrote the paper- preliminary draft including entire data analysis. PAM assisted BMK to conduct the experiments and write the preliminary draft. MA analysed the statistical data and was also involved in editing the manuscript. SD and SKS contributed towards conceptualization, designing the methodology and editing the manuscript. IMB and SKS contributed in designing, studying, reviewing, analysing the entire data as well as editing the complete manuscript. All authors have carefully read and approved the final version of the manuscript.

Data availability

The raw data supporting the conclusion of this article will be made available by the authors upon reasonable request.

Declarations

Conflict of interest

Authors have declared that they have no conflict of interest.

Ethical standards

This research does not involve any human participants and/or animals.