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Article

Statistical Optimization of Tween-80-Assisted Potassium Hydroxide Pretreatment and Enzymatic Hydrolysis for Enhancing Sugar Yields from Corn Cob

by
Hui Zhang
1,* and
Junhui Wu
1,2
1
School of Life Sciences, Liaocheng University, Liaocheng 252000, China
2
Liaocheng Agricultural and Rural Bureau, Liaocheng 252000, China
*
Author to whom correspondence should be addressed.
Fermentation 2023, 9(12), 1009; https://doi.org/10.3390/fermentation9121009
Submission received: 31 October 2023 / Revised: 23 November 2023 / Accepted: 5 December 2023 / Published: 7 December 2023
(This article belongs to the Special Issue Recent Advances in Bioconversion of Biomass to Value-Added Products)
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Abstract

:
With the addition of Tween 80, potassium hydroxide pretreatment and enzymatic hydrolysis were statistically optimized to maximize sugar yields from corn cob (CC). The results indicated that the sugar yields from CC could be influenced significantly by the potassium hydroxide concentration, temperature and time during pretreatment. The optimized pretreatment conditions were as follows: potassium hydroxide, 46 g·L−1; Tween 80, 3.0 g·L−1; solid dose, 200 g·L−1; temperature, 78 °C; and time, 50 min. After optimization, the lignin reduction and recoveries of cellulose and hemicellulose were 89.7%, 97.8% and 68.0%, respectively. In addition, sugar production could also be influenced by the biomass loading, enzyme loading and reaction time. A maximal glucose production (518.48 mg·gds−1, milligrams per gram of dry substrate) and xylose production (351.14 mg·gds−1), 97.2% cellulose conversion and 82.9% hemicellulose conversion from CC could be obtained when the biomass loading was 195 g·L−1 and the enzyme loading was 8.9 FPU·gds−1 (filter paper activity units per gram of dry substrate) and when the Tween 80 concentration was 3.0 g·L−1 at 50 °C for 30.4 h during hydrolysis. This is the first systematic study of combined Tween 80 pretreatment of CC by potassium hydroxide and hydrolysis of CC by cellulase preparation to increase sugar production.

1. Introduction

Bioethanol production from lignocellulosic biomass has received worldwide attention from researchers because it can reduce greenhouse gas emissions and the consumption of fossil fuels [1]. However, a sustainable, low-cost and effective pretreatment of biomass is needed to reduce the lignin content and cellulose crystallinity, which can enhance the utilization efficiency of cellulosic components from lignocellulosic substrates. Alkaline reagents, such as calcium and sodium hydroxides and ammonia solution, are always applied to the pretreatment of biomass due to the effective delignification and reduction in cellulose crystallinity and polymerization degree that can be obtained [2]. However, calcium precipitation, sodium use and discharge and NH3 off-gassing can result in environmental pollution [3]. Potassium hydroxide pretreatment is a prospective method, as the produced black liquor can be used for fertilizer production [4,5]. In fact, some researchers have attempted to pretreat corn stover [6], rice straw [7] and Kash grass [8] using potassium hydroxide. However, degradation of the cellulosic components always occurs after pretreatment. Therefore, pretreatment optimization in order to retain the holocellulose in large amounts and enhance the utilization efficiency of holocellulose is very important. Furthermore, it is noteworthy that the hydrophobic compounds produced during delignification can adhere to the surface of biomass, which also influences the hydrolysis of holocellulose by enzymes [9]. This problem can be resolved by using surfactants, as they combine hydrophobic compounds, form emulsions and hinder hydrophobic compounds from being redeposited on the biomass surface [10,11,12]. This indicates that surfactants can assist pretreatments in intensifying the delignification and utilization efficiency of substrates.
Glucose from cellulose and xylose from xylan (a type of hemicellulose) can be converted to ethanol by yeast. However, the optimal temperature for yeast growth was shown to be different than that for enzyme activities during hydrolysis [13], which means that the two processes of hydrolysis and ethanol fermentation should be performed separately. Therefore, the enzymatic hydrolysis of biomass becomes a crucial step to take before bioethanol production by yeast fermentation. It should be noted that surfactants such as Tween 80 could be supplied for enzymatic hydrolysis, as they can reduce the nonproductive binding of enzymes to lignin, increase enzyme activities, reduce enzyme usage and shorten the reaction time [14,15,16]. In addition to Tween 80, the levels of biomass loading, pH, temperature and time used in hydrolysis can also influence sugar production, which indicates that the optimization of the enzymatic hydrolysis to maximize sugar yields is also crucial to bioethanol production.
Response surface methodology (RSM) is an effective statistical optimization tool, through which significant factors affecting responses can be identified and levels of the factors along with responses can also be optimized [13]. Corn cob (CC) is one of the most abundant agricultural residues in north China, which makes it a prospective biomass for bioethanol production. Therefore, with the addition of Tween 80, the pretreatment of CC using potassium hydroxide and its subsequent hydrolysis by a cellulase preparation were statistically optimized in this research.

2. Materials and Methods

2.1. Substrate, Chemicals and Enzymes

CC from a local farm in Liaocheng (Liaocheng Jiamei Farm, Liaocheng, China) was milled into a powder (particle size < 5 mm) using high speed multifunctional pulverizer (XT-200, Yongkang Songqing Hardware Factory, Yongkang, China), dried to a constant weight at 85 °C using Electric Heating Constant Temperature Blast Drying box (LC-101-2, Hunan Lichen Instrument Technology Co., Ltd., Changsha, China) and stored at room temperature. Tween 20 (CP), Tween 80 (CP), PEG 4000 (CP), PEG 6000 (CP) and potassium hydroxide (AR) were obtained from Xilong Scientific Co., Ltd., Shantou, China. CTAB (AR) and SDS (AR) were obtained from Sinopharm Chemical Reagent Co., Ltd., Shanghai, China. Based on our previous studies, crude cellulase (6.13 FPU·mL−1, filter paper activity units per milliliter; 889.18 XU·mL−1, xylanase activity units per milliliter) was prepared with the application of Aspergillus niger HQ-1 [17,18].

2.2. Screening for the Optimal Surfactant in Pretreatment

The effects of pretreatment with polysorbate 20 (Tween 20), polysorbate 80 (Tween 80), polyethylene glycol 4000 (PEG 4000), polyethylene glycol 6000 (PEG 6000), cetyltrimethylammonium bromide (CTAB) and sodium dodecyl sulfate (SDS) on the amount of sugar production from CC were determined. Each surfactant was mixed with 100 mL of potassium hydroxide solution (20 g·L−1) at a 2.0 g·L−1 concentration in 250 mL conical flasks. Then, CC was added to the reaction mixture at a solid dose of 100 g·L−1 and pretreated at 40 °C in water bath (HHS-21-8, Shanghai Xiyu Instrument Equipment Co., Ltd., Shanghai, China) for 20 min. After being washed, filtered to neutrality and dried at 80 °C for 24 h, the CC was enzymolized using the cellulase preparation under the conditions described in Section 2.5.1. A pretreatment without the use of a surfactant was used as a control. The level of sugar production was determined and was applied as a reference indicator to screen the optimal surfactant.

2.3. Optimization of Pretreatment of Corn Cob

The Plackett–Burman design (PBD) is an efficient tool to screen and identify variables that could significantly influence responses according to the contribution percentage of variables [19,20,21]. However, PBD does not distinguish between main effects and interaction effects and does not consider interaction effects among variables [19,22,23]. In this research, with the use of a two-level PBD method containing twelve trials and three repetitive center points, the effects of five variables on the level of sugar production were investigated (Table S1). Each trial was performed by adding CC to potassium hydroxide solution (100 mL) in conical flasks (250 mL). The CC obtained after pretreatment was hydrolyzed under initial conditions, and the level of sugar production was used as a response. The suitable ranges of the variables were identified using the method of steepest ascent (Table S2). In the end, through using a Box–Behnken design (BBD) containing three variables and fifteen trials, the levels of the variables and sugar production were optimized (Table S3).

2.4. Cellulosic Components Determination

The cellulosic components of the CC were determined based on the National Renewable Energy Laboratory (NREL) technique [24]. Calculations for the recovery of solids (SDR), cellulose (CER) and hemicellulose (HCER) and the lignin reduction (LGR) were performed according to the following equations:
SDR = (W1/W0) × 100%
CER = (CE1 × SDR/CE0) × 100%
HCER = [HCE1 × SDR/HCE0] × 100%
LGR = 1 − [(LG1 × SDR)/LG0] × 100%
where W0 and W1 are the weight of the raw CC (g) and pretreated CC (g), respectively; CE0 and CE1 are the cellulose concentration (g/g) in the raw CC and that in the CC obtained after the pretreatment, respectively; HCE0 and HCE1 are the hemicellulose concentration (g/g) in the raw CC and that in the CC after the pretreatment, respectively; and LG0 and LG1 are the lignin concentration (g/g) in the raw CC and that in the CC after the pretreatment, respectively.

2.5. Optimization of Enzymolysis

2.5.1. Initial Enzymolysis Conditions

A mixture of 100 mL of 50 mM sodium acetate buffer (pH 4.4), CC (130 g·L−1), Tween 80 (1.5 g·L−1), cellulase preparation (2.5 FPU·gds−1, filter paper activity units per gram of dry substrate), cycloheximide (30 µg·mL−1) and tetracycline antibiotic (40 µg·mL−1) were prepared in conical flasks (250 mL). After hydrolysis at 45 °C and mild shaking for 17.0 h, the reactant was centrifugated at 10,000 rpm for 10 min. the level of sugar production was measured by HPLC as described by Zhang and Wu (2021) [18] and was reported as milligrams per gram of dry substrate (mg·gds−1). In the control samples, the enzyme solution was replaced with an equivalent enzyme solution that was inactivated at 100 °C for 10 min.

2.5.2. Screening of the Optimal Surfactant in Enzymatic Hydrolysis

CC pretreated under the optimized conditions was used as a substrate for enzymatic hydrolysis under initial conditions during screening of the optimal surfactant with a concentration of 1.5 g·L−1 among the surfactants mentioned in Section 2.2. Enzymatic hydrolysis without any surfactant was used as a control. The sugar yields from the CC were determined and were used as reference indicators to screen for the optimal surfactant in the hydrolysis.

2.5.3. Enzymatic Hydrolysis Optimization of CC

The effects of different factors during the enzymatic hydrolysis on the sugar yields were investigated using PBD, which included fifteen trials, six factors and two levels (−1, +1), including three replicates at the center point (0). The design and the tested factors are illustrated in Table S4. The maximal level of sugar production was preliminarily evaluated using the method of steepest ascent (Table S5). Finally, with the use of a central composite design (CCD) containing three variables and twenty trials, the levels of significant variables and sugar production were optimized (Table S6).

2.6. Calculations for Conversions of Cellulosic Components

Cellulose can be expressed as (C6H10O5)n, and the molar mass of each monomer (C6H10O5) is 162 g/mol. Theoretically, 1 mol of monomer can be converted into 1 mol of glucose with a molar mass of 180 g/mol, which indicates that 1 g of cellulose can be converted to 1.11 g of glucose. In addition, xylan can be expressed as (C5H8O4)n, and the molar mass of each monomer (C5H8O4) is 132 g/mol. During hydrolysis, 1 mol of xylan can theoretically be converted to 1 mol of xylose (C5H10O5) with a molar mass of 150 g/mol. Therefore, 1 g of xylan can be converted to 1.136 g of xylose. Therefore, calculations of the conversions of cellulose (CEC) and hemicellulose (HCEC) were carried out according to the following equations:
CEC = [MGL/(MCE × 1.11)] × 100%
HCEC = [MXY/(MHCE × 1.136)] × 100%
where MGL and MXY are the glucose and xylose production from CC (mg·gds−1), respectively, and MCE and MHCE are the concentrations of cellulose and hemicellulose in the CC after the pretreatment (mg·gds−1), respectively.

2.7. Data Analysis

The Minitab software (Minitab 14.12) and Statistical Analysis System (SAS, 8.0) were applied to the experimental design and data analysis in this research. Variables or terms with p values less than 0.05 were regarded as having significant effects on the responses.

3. Results and Discussions

3.1. Screening of the Optimal Surfactant in Pretreatment

As shown in Figure 1, compared with the control (without surfactant), the addition of each surfactant in the pretreatment could effectively enhance the sugar yields from the CC. Among the surfactants adopted in this work, Tween 80 could most effectively enhance the yields of glucose and xylose from the CC. In fact, Tween 80 was also added during the pretreatment of sugarcane bagasse [10,25], corn cob [26] and corn stover [27] to enhance their enzymatic digestibility. In previous studies, it was reported that the use of PEG 6000 in a sodium hydroxide pretreatment can promote sugar yields from pine foliage [9] and sugarcane bagasse [28] more effectively than Tween 80. In the report described by Nasirpour et al. (2018) [29], PEG 6000 was also added during the pretreatment of sugarcane bagasse to enhance its enzymatic digestibility. Goshadroua and Lefsrud (2017) [30] also reported that the addition of PEG 4000 during pretreatment can enhance sugar yields from beech wood waste more effectively than Tween 80 and Tween 20. The different effects of the surfactants used in the pretreatment on the sugar yields were perhaps dependent on the methods and substrates used. Based on the results in this work, Tween 80 was adopted as a variable during the subsequent pretreatment optimization.

3.2. Optimization of Tween-80-Assisted Potassium Hydroxide Pretreatment of Corn Cob

In this work, sugar production from CC was used as a response for the optimization, as the objective of the optimization was to improve the utilization efficiency of the substrates and enhance the sugar yields. In fact, the level of sugar production was also used as a response for the pretreatment optimization of corn stalk [31] and corn cob [32]. The results in Table 1 show that the sugar yields could be influenced significantly by the potassium hydroxide concentration, temperature and time during the pretreatment. Similar results were observed during the pretreatment of deoiled Jatropha curcas seeds [33] and switchgrass [34]. Furthermore, the sugar yields from corn cob [35] and sugarcane bagasse [36] can be influenced significantly by the time and temperature during pretreatment. The sugar yields were positively influenced by the potassium hydroxide concentration in this work, as the level of delignification could be enhanced by a high concentration of potassium hydroxide. Similar results have also been observed during the pretreatment of wheat straw [4] and kallar grass and cotton stalks [37]. The level of delignification could also be enhanced by increasing the temperature, as the bonds between the lignin and carbohydrate could be disrupted more effectively at higher temperatures, and the reaction rate constant could also be enhanced at relatively high temperatures [38]. The solid dose amount during the pretreatment was related to the heat and mass transfer efficiency and the adjustment of the chemical concentration [4]. In this research, the sugar yields were positively influenced by the solid dose amount, as a lower level could trigger relatively higher levels of potassium hydroxide and higher levels of holocellulose degradation. In addition, the sugar yields could be positively influenced by the amount of Tween 80, which was perhaps related to the higher level of delignification caused by higher Tween 80 concentrations. Similar results were also observed in the delignification of corn cob [26] and bamboo [39] with the addition of Tween 80. Based on the above results, 3.0 g·L−1 Tween 80 and a solid dose of 200 g·L−1 were adopted in the next experimental steps.
The results shown in Table S8 indicate that the level of sugar production reached the maximum while adopting a concentration of 48 g·L−1 of potassium hydroxide, a temperature of 80 °C and a pretreatment time of 50 min. However, too severe pretreatment conditions after the plateau were not beneficial to the level of sugar production. As shown in Table 2, the level of sugar production could be influenced significantly by the primary terms (x1, x2 and x3) and secondary terms (x12, x22, and x32). The level of glucose production was influenced insignificantly by all the interaction terms (x1x2, x1x3 and x2x3). The level of xylose production was influenced insignificantly by the interaction of x1 and x2 and that of x2 and x3, whereas it was influenced significantly by the interaction of x1 and x3. For the model for predicting the level of glucose production, the p values of the lack of fit (0.118) and model (0.000) along with an R2 value of 99.6% and an adjusted R2 value of 98.8%showed the high accuracy of the model. For the model predicting the level of xylose production, the p values of the lack of fit (0.120) and model (0.000), along with the R2 value of 99.6% and the adjusted R2 value of 98.8% showed that the model could also predict the optimal levels of the variables and response.
The effects of the interaction terms on the level of sugar production are shown in contour plots in Figure 2. As shown in Figure 2a,b, the optimal potassium hydroxide concentration for sugar production ranged from 44 g·L−1 to 48 g·L−1. The suitable temperature range for glucose production was from 78 °C to 81 °C, and that for xylose production was from 75 °C to 78 °C. Based on Figure 2c,d, the suitable potassium hydroxide concentration range for sugar production was 44 g·L−1–48 g·L−1. The suitable time range for glucose production was from 50 min to 52.5 min, and that for xylose production was from 47.5 min to 50 min. Figure 2e,f indicate that the optimal temperature region for glucose production was 78 °C–81 °C, and that for xylose production was from 75 °C to 78 °C. The optimal time range for glucose production was from 50 min to 52.5 min, and that for xylose production was from 47.5 min to 50 min.
After the canonical analysis, the optimal pretreatment conditions for attaining the maximal glucose yield (230.89 mg·gds−1) were as follows: potassium hydroxide concentration, 46.4 g·L−1 (x1 = 0.13424); temperature, 78.2 °C (x2 = −0.17903) and time, 51.2 min (x3 = 0.14878). The optimal pretreatment conditions for attaining the maximal xylose yield (125.89 mg·gds−1) were as follows: potassium hydroxide concentration, 45.7 g·L−1 (x1 = −0.18947); temperature, 77.3 °C (x2 = −0.27117) and time, 47.9 min (x3 = −0.26113). The corresponding models for the optimization were as follows:
Y1 = 229.500 − 6.749x1 − 6.710x2 + 4.554x3 − 25.358x12 − 19.090x22 − 15.797x32 + 0.177x1x2 − 0.185x1x3 − 0.682x2x3
Y2 = 123.143 − 8.164x1 − 7.548x2 − 7.276x3 − 20.058x12 − 14.330x22 − 12.278x32 + 1.750x1x2 − 3.973x1x3 − 0.410x2x3
where Y1 is the glucose yield, Y2 is the xylose yield, x1 is the potassium hydroxide concentration, x2 is the temperature and x3 is the time.
After modification, the optimal conditions were set as follows: potassium hydroxide concentration, 46 g·L−1; temperature, 78 °C; time, 50 min; Tween 80 concentration, 3.0 g·L−1 and solid dose, 200 g·L−1. The average yields of glucose and xylose from three replicates were 230.36 mg·gds−1 and 124.30 mg·gds−1, respectively, which were similar to the predicted values (230.89 mg·gds−1 and 125.89 mg·gds−1) of the models. To investigate the changes in the contents of cellulosic components and lignin before and after the pretreatment, raw CC (20.0 g) was pretreated under the optimal conditions, and 14.65 g of CC was obtained. The results showed that cellulose content of 35.98% and 48.03%, hemicellulose contents of 40.15% and 37.25% and lignin contents of 14.98% and 2.11% for the raw CC and the pretreated CC were obtained, respectively. After the calculation, the SDR, CER, HCER and LGR values were 73.25%, 97.8%, 68.0% and 89.7%, respectively. Comparisons of related results from different studies are illustrated in Table 3. The levels of lignin reduction (89.7%) and cellulose recovery (97.8%) in this work were the highest among the studies shown in Table 3, which made it possible to enhance the glucose yield from the CC. However, the hemicellulose recovery value of 68.0% obtained in this research was less than 81.47% [38] and 81.1% [40]. The relatively low hemicellulose recovery obtained in this work was related to the higher level of delignification during the pretreatment. However, the hemicellulose recovery value of 68.0% obtained in this work was also higher than the previously reported values of 47.77% [41], 9.9% [42] and 61.3% [43].
As shown in Table 3, compared with other reports, the relatively higher solid dose of 200 g·L−1 used in this work indicated that the optimal conditions in this research could pretreat more CC at once, enhancing the utilization efficiency of vessels. Among the different reports, the temperature and time ranged from 50 °C to 170 °C and from 50 min to 360 min, respectively. The relatively short time (50 min) used during the pretreatment in this research could lead to an enhancement in the pretreatment efficiency. Although the temperature (50 °C) [38,43] was below 78 °C in this research, the 360 min of time used in the two previous reports indicate a lower pretreatment efficiency. Compared with the temperatures of 170 °C [40], 121 °C [41] and 80 °C [42,44], the temperature of 78 °C used in this research could lead to a reduction in the energy expenditure of the pretreatment.

3.3. Screening of Surfactant in Enzymatic Hydrolysis

As shown in Figure 3, the yields of glucose and xylose from the CC after the enzymatic hydrolysis containing each surfactant were higher than those in the control, which indicated that the surfactants could enhance the sugar yields. Compared with the other surfactants, Tween 80 could also enhance the sugar yields more effectively. In some previous reports, Tween 80 was also used in the enzymatic hydrolysis of corn stover [6], sugarcane bagasse [25,45], corn cob [26], oil palm fruit bunch [46], green coconut fiber [47] and jabon alkaline pulp [48] to enhance sugar yields. However, PEG 4000 can enhance sugar yields from poplar pulp more effectively than Tween 80 [49]. PEG 6000 was added to the enzymatic hydrolysis of cotton microdust [50] to enhance sugar yields. The different effects of the surfactants in enhancing the sugar production were probably associated with the composition of the substrates and enzyme sources. Based on the results shown in Figure 3, Tween 80 was determined to be suitable for the pretreatment.

3.4. Optimization of Enzymatic Hydrolysis of Corn Cob

As shown in Table 4, the sugar yields were influenced significantly by the biomass loading, enzyme loading and reaction time. In some previous reports, sugar yields from sweet sorghum bagasse [13] and rice straw [51] were also influenced significantly by these three factors. In addition, the sugar yields could be enhanced by relatively high levels of these three factors in this work. An insufficiently low biomass loading could reduce the pretreatment efficiency, and an excessively high a biomass loading could influence the stirring and result in enzymatic feedback inhibition. Enzyme loading was also related to the enzymolysis input costs and enzymolysis efficiency. An insufficient enzyme loading was not enough to produce sugar on a large scale. However, an excess level could result in instability of the fluid kinetics and an unsuitable suspension of slurry, which could influence the sugar yields. The reaction time was also related to the efficiency and input costs for enzymolysis. It is noteworthy that the sugar yield efficiency could be influenced by the recrystallization of cellulose and adhesion on amorphous regions by cellulases in the later period of hydrolysis [52].
The level of sugar production from the CC was not influenced significantly by the pH, temperature and Tween 80 concentration in this research, whereas the level of sugar production from cotton stalk was influenced significantly by the temperature and pH [53], and the level of sugar production from pine foliage was influenced significantly by the Tween 80 concentration [9]. The different effects of the variables on the level of sugar production were probably related to the source diversity of the enzymes and substrates.
Table S5 illustrates that the level of sugar production could be maximized under the following conditions: biomass loading, 200 g·L−1; Tween 80 concentration, 3.0 g·L−1; enzyme loading, 8.5 FPU·gds−1; pH, 4.8; temperature, 50 °C and time, 30 h. Table 5 illustrates that the level of sugar production was influenced significantly by the primary terms (X1, X2 and X3) and secondary terms (X12, X22 and X32). The glucose yield was influenced insignificantly by three interaction terms (X1X2, X1X3 and X2X3). The xylose yield was influenced insignificantly by two interaction terms, X1X2 and X2X3; however, it was significantly influenced by X1X3. For the model of optimizing the glucose yield, the p values of the lack of fit (0.105) and the model (0.000) along with the R2 value of 99.4% and te adjusted R2 value of 98.9% evidenced that the glucose yield could be predicted and optimized using the model. For the model for predicting the xylose yield, the p values of the lack of fit (0.103) and the model (0.000) along with the R2 value of 99.5% and the adjusted R2 value of 99.1% also illustrated the accuracy of the model.
As shown in Figure 4a,b, the maximal level of sugar production could be obtained when the biomass loading region was from 190 g·L−1 to 200 g·L−1, and the enzyme loading region was from 8.0 FPU·gds−1 to 10.0 FPU·gds−1 after 30 h of hydrolysis. Figure 4c,d illustrate that the maximal level of sugar production could be obtained in the biomass loading region of 190 g·L−1–200 g·L−1 with the use of 8.5 FPU·gds−1. In addition, the maximal glucose yield and xylose yield could be obtained when the reaction time ranged from 30 h to 33 h and from 27 h to 30 h, respectively. Figure 4e,f illustrate that the suitable enzyme loading range for sugar production was 8.0 FPU·gds−1–10.0 FPU·gds−1 with the use of 200 g·L−1 biomass loading. Furthermore, the suitable range of the reaction time for the glucose yield was from 30 h to 33 h, and that for the xylose yield was from 27 h to 30 h.
After the canonical analysis, the optimal conditions for obtaining the maximal glucose yield (518.76 mg·gds−1) were as follows: biomass loading, 195.1 g·L−1 (X1 = −0.24656); enzyme loading, 8.93 FPU·gds−1 (X2 = 0.14154) and reaction time, 31.7 h (X3 = 0.28508). The optimal conditions for obtaining the maximal xylose yield (351.08 mg·gds−1) were as follows: biomass loading, 195.6 g·L−1 (X1 = −0.21828); enzyme loading, 8.9 FPU·gds−1 (X2 = 0.13858) and reaction time, 29.1 h (X3 = 0.15778). The corresponding regression models were obtained as follows:
Y3 = 515.824 − 8.651X1 + 7.871X2 + 9.232X3 − 18.894X12 − 30.526X22 − 16.813X32 − 2.469X1X2 − 1.111X1X3 + 0.566X2X3
Y4 = 347.833 − 15.640X1 + 9.549X2 − 11.097X3 − 32.468X12 − 33.709X22 − 30.603X32 + 2.004X1X2 − 7.526X1X3 − 1.466X2X3
where Y3, Y4, X1, X2 and X3 are the glucose yield, xylose yield, biomass loading, enzyme loading and reaction time, respectively.
After adjustment, verification of the models was performed three times, where the biomass loading was 195 g·L−1, the enzyme loading was 8.9 FPU·gds−1 and the reaction time was 30.4 h. The level of glucose production was 518.48 mg·gds−1, and that of xylose production was 351.14 mg·gds−1. The experimental data were similar to the predicted data (518.76 mg·gds−1 and 351.08 mg·gds−1).
To explore the competitiveness of the results in this research, comparisons of sugar production in various studies were performed (Table 6). This illustrated that the levels of sugar production in this study (518.48 mg·gds−1 351.14 mg·gds−1) were the highest among the values reported in Table 6, which indicated that the method of sugar production from CC conducted in this research has established a foundation for enhancing bioethanol production. Based on the 97.2% cellulose conversion and 82.9% hemicellulose conversion, the utilization efficiency of holocellulose in the CC was more competitive. Table 6 also illustrates that the expression levels of enzyme loading were FPU·gds−1 [41,44,54,55,56,57], CBU·gds−1 [44] and EU·gds−1 [58]. The levels of enzyme loading among the various studies could not be compared directly due to differences in the determination of the enzyme activities and enzyme loading expression. However, the 8.9 FPU·gds−1 enzyme loading in this research was less than 10.0 FPU·gds−1 [41,55], 61.27 FPU·gds−1 [44], 75.15 FPU·gds−1 [54] and 31.1 FPU·gds−1 [56,57], which indicated that the cellulase input costs could be reduced in this research.
As shown in Table 6, the 200 g·L−1 biomass loading in [54] was the highest. However, the simultaneous adoption of 72 h and 75.15 FPU·gds−1 could result in a lower hydrolysis efficiency and higher cellulase input cost. Although the biomass loading (195 g·L−1) used in this research was slightly below 200 g·L−1, the application of a 30.4 h duration and an 8.9 FPU·gds−1 enzyme loading could enhance the hydrolysis efficiency and reduce the cellulase input costs. In addition, the reaction time range was 24.0 h–96.0 h among the various reports (Table 6). Although the shorter time (24.0 h) used in [56] was more advantageous than the 30.4 h used in this research, an insufficient biomass loading (50 g·L−1) and excess enzyme loading (31.1 FPU·gds−1) were also used, which could improve the reaction vessel requirements and reduce the hydrolysis efficiency. Compared with six other previous reports [41,44,54,55,57,58], the 30.4 h reaction time in this work was shorter, which could improve the sugar yield efficiency. On the other hand, compared with the six reports [41,44,54,55,56,57], using an in-house cellulase preparation in this research could also reduce the enzyme input costs. In brief, a higher level of sugar production could be obtained by using a smaller amount of enzyme and a shorter time to hydrolyze more substrates in this research.

4. Conclusions

With the addition of Tween 80, pretreatment of corn cob using potassium hydroxide and hydrolysis of corn cob using a cellulase preparation were statistically optimized for the first time in this research. Using a solid dose of 200 g·L−1 for the pretreatment and a 195 g·L−1 biomass loading for the hydrolysis in this work, could increase the utilization efficiency of a limited volume of reactors and reduce the vessel requirements. The recovery of cellulose (97.8%) and the lignin reduction (89.7%) demonstrated in this research could lay the foundation for increasing sugar production. The glucose yield (518.48 mg·gds−1), xylose yield (351.14 mg·gds−1), cellulose conversion (97.2%) and hemicellulose conversion (82.9%) implied a higher utilization efficiency of holocellulose from the CC. The utilization of a reduced pretreatment time (50 min) and hydrolysis time (30.4 h) in this research could increase the total sugar production efficiency. Although the Tween 80 concentration in the pretreatment and enzymatic hydrolysis had an insignificant effect on the sugar yields from the CC, the addition of Tween 80 could effectively enhance the sugar yields. The process of ethanol fermentation by using sugar from CC will be optimized in future research.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/fermentation9121009/s1. Table S1: Variables and matrix of Plackett–Burman design (PBD) for pretreatment optimization; Table S2: Design of the steepest ascent method for pretreatment optimization; Table S3: Variables and matrix of BBD for pretreatment optimization; Table S4: Variables and matrix of Plackett–Burman design (PBD) for hydrolysis optimization; Table S5: Design of the steepest ascent method for hydrolysis optimization; Table S6: Variables and matrix of CCD for hydrolysis optimization.

Author Contributions

H.Z. performed the experiments of the pretreatment optimization and analyzed the data. J.W. performed the experiments of the enzymatic hydrolysis optimization and analyzed the data. H.Z. wrote, reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by projects from the Natural Science Foundation of Shandong Province, China (ZR2010CQ042).

Institutional Review Board Statement

This article does not contain any studies with animals performed by any of the authors.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data are incorporated into the article and its online Supplementary Materials.

Conflicts of Interest

The authors declare that they have no conflict of interest. It is not under consideration for publication elsewhere, and if accepted, the article will not be published elsewhere in the same form, in any language, without the written consent of the publisher.

Abbreviations

BBDBox–Behnken design
BLBiomass loading
CBUCellubioase activity unit
CCCorn cob
CCDCentral composite design
CECellulose
CECCellulose conversion
CERCellulose recovery
ELEnzyme loading
EUEndoglucanase activity unit
FPU·gds−1Filter paper activity units per gram of dry substrate
FPU·mL−1Filter paper activity units per milliliter
GLGlucose
HCEHemicellulose
HCECHemicellulose conversion
HCERHemicellulose recovery
LGRLignin reduction
mg·gds−1Milligrams per gram of dry substrate
PBDPlackett–Burman design
PHCPotassium hydroxide concentration
PTEPretreatment temperature
PTIPretreatment time
RSMResponse surface methodology
RTReaction time
SDRSolid recovery
XU·mL−1Xylanase activity units per milliliter
XYXylose

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Fermentation 09 01009 g001
Figure 1. Effects of different surfactants in pretreatment on sugar yields from the pretreated corn cob. (Pretreatment conditions: potassium hydroxide concentration, 20 g·L−1; solid dose, 100 g·L−1; surfactant concentration, 2.0 g·L−1; temperature, 40 °C; duration, 20 min. Hydrolysis conditions: biomass loading, 130 g·L−1; enzyme loading, 2.5 FPU·gds−1; Tween 80 concentration, 1.5 g·L−1; pH, 4.4; temperature, 45 °C; duration, 17 h).
Figure 1. Effects of different surfactants in pretreatment on sugar yields from the pretreated corn cob. (Pretreatment conditions: potassium hydroxide concentration, 20 g·L−1; solid dose, 100 g·L−1; surfactant concentration, 2.0 g·L−1; temperature, 40 °C; duration, 20 min. Hydrolysis conditions: biomass loading, 130 g·L−1; enzyme loading, 2.5 FPU·gds−1; Tween 80 concentration, 1.5 g·L−1; pH, 4.4; temperature, 45 °C; duration, 17 h).
Fermentation 09 01009 g001
Fermentation 09 01009 g002aFermentation 09 01009 g002b
Figure 2. Contour plots showing the effects of interaction terms on glucose yield (GL) and xylose yield (XY). (a,b) Interaction of PHC and PTE; (c,d) interaction of PHC and PTI; (e,f) interaction of PTE and PTI. PHC: potassium hydroxide concentration; PTE: pretreatment temperature; PTI: pretreatment time.
Figure 2. Contour plots showing the effects of interaction terms on glucose yield (GL) and xylose yield (XY). (a,b) Interaction of PHC and PTE; (c,d) interaction of PHC and PTI; (e,f) interaction of PTE and PTI. PHC: potassium hydroxide concentration; PTE: pretreatment temperature; PTI: pretreatment time.
Fermentation 09 01009 g002aFermentation 09 01009 g002b
Fermentation 09 01009 g003
Figure 3. Effects of different surfactants in enzymatic hydrolysis on sugar yields. (Pretreatment conditions: potassium hydroxide concentration, 46 g·L−1; solid dose, 200 g·L−1; surfactant concentration, 3.0 g·L−1; temperature, 78 °C; duration, 50 min. Enzymatic hydrolysis conditions: biomass loading, 130 g·L−1; enzyme loading, 2.5 FPU·gds−1; Tween 80 concentration, 1.5 g·L−1; pH, 4.4; temperature, 45 °C; duration, 17 h).
Figure 3. Effects of different surfactants in enzymatic hydrolysis on sugar yields. (Pretreatment conditions: potassium hydroxide concentration, 46 g·L−1; solid dose, 200 g·L−1; surfactant concentration, 3.0 g·L−1; temperature, 78 °C; duration, 50 min. Enzymatic hydrolysis conditions: biomass loading, 130 g·L−1; enzyme loading, 2.5 FPU·gds−1; Tween 80 concentration, 1.5 g·L−1; pH, 4.4; temperature, 45 °C; duration, 17 h).
Fermentation 09 01009 g003
Fermentation 09 01009 g004aFermentation 09 01009 g004b
Figure 4. Contour plots showing the effects of interaction terms on glucose yield (GL) and xylose yield (XY). (a,b) Interaction of BL and EL; (c,d) interaction of BL and RT; (e,f) interaction of EL and RT. BL: biomass loading; EL: enzyme loading; RT: reaction time.
Figure 4. Contour plots showing the effects of interaction terms on glucose yield (GL) and xylose yield (XY). (a,b) Interaction of BL and EL; (c,d) interaction of BL and RT; (e,f) interaction of EL and RT. BL: biomass loading; EL: enzyme loading; RT: reaction time.
Fermentation 09 01009 g004aFermentation 09 01009 g004b
Table 1. Analysis results of PBD for pretreatment optimization.
Table 1. Analysis results of PBD for pretreatment optimization.
TermsGlucose YieldXylose Yield
Constant118.10129.9542
Potassium hydroxide concentration8.228 ##3.4408 ##
Solid-to-liquid ratio0.0880.0008
Pretreatment temperature 10.411 ##4.2392 ##
Pretreatment time 7.329 ##3.2708 ##
Tween 800.1790.0608
R299.45%99.29%
Adj-R299.03%98.75%
Lack of fit0.1350.134
## Have significant effects to responses.
Table 2. Analysis results of BBD for pretreatment optimization.
Table 2. Analysis results of BBD for pretreatment optimization.
TermsGlucose Yield (Y1) Xylose Yield (Y2)
Constant229.500123.143
Potassium hydroxide concentration (x1)−6.749 ##−8.164 ##
Pretreatment temperature (x2)−6.710 ##−7.548 ##
Pretreatment time (x3)4.554 ##−7.276 ##
Potassium hydroxide concentration × potassium hydroxide concentration (x1 × x1)−25.358 ##−20.058 ##
Pretreatment temperature × pretreatment temperature (x2 × x2) −19.090 ##−14.330 ##
Pretreatment time × pretreatment time (x3 × x3)−15.797 ##−12.278 ##
Potassium hydroxide concentration × pretreatment temperature (x1 × x2)0.1771.750
Potassium hydroxide concentration × pretreatment time (x1 × x3)−0.185−3.973 ##
Pretreatment temperature × pretreatment time (x2 × x3)−0.682−0.410
R299.6%99.6%
Adj-R2 98.8%98.8%
Lack of fit0.1180.120
## Have significant effects to responses.
Table 3. Lists of recoveries of cellulose and hemicellulose and lignin reduction among different studies.
Table 3. Lists of recoveries of cellulose and hemicellulose and lignin reduction among different studies.
SubstanceConditionsCERHCERLGRRefs.
CCPotassium permanganate 20 g·L−1, solid dose 100 g·L−1, 50 °C, 360 min.94.56%81.47%46.79%[38]
CCEthanol solution 70% (v/v), solid dose 100 g·L−1, 170 °C, 60 min.85.1%81.1%51.1%[40]
CCMixture of glycerol and water and H2SO4 (80:19:1, w/w), solid dose 61 g·L−1, 121 °C, 60 min.89.9%47.77%54.12%[41]
CCFormic acid 880 g·L−1, solid dose 100 g·L−1, 80 °C, 180 min.87.2%9.9%87.1%[42]
CCHydrogen peroxide solution 2.0% (w/w) with pH 11.5, solid dose 50 g·L−1, 50 °C, 360 min.81.3%61.3%75.4%[43]
CCSodium hydroxide solution 5.0 g·L−1, solid dose 100 g·L−1, 80 °C, 180.0 min.84.15%-34.98%[44]
CCPotassium hydroxide solution 46 g·L−1, solid dose 200 g·L−1, Tween 80 3.0 g·L−1, 78 °C, 50 min.97.8%68.0%89.7%This work
CC: corn cob; CER: cellulose recovery; HCER: hemicellulose recovery; LGR: lignin reduction.
Table 4. Analysis results of PBD for hydrolysis optimization.
Table 4. Analysis results of PBD for hydrolysis optimization.
TermsGlucose YieldXylose Yield
Constant220.229128.361
Biomass loading14.563 ##8.176 ##
Enzyme loading15.498 ##10.573 ##
Reaction temperature0.3840.794
Reaction pH0.9830.346
Reaction time19.208 ##10.054 ##
Tween 80 concentration0.9680.456
R299.43%99.40%
Adj-R2 98.86%98.80%
Lack of fit0.1080.104
## Have significant effects to responses.
Table 5. Analysis results of CCD for enzymatic hydrolysis optimization.
Table 5. Analysis results of CCD for enzymatic hydrolysis optimization.
TermsGlucose YieldXylose Yield
Constant515.824347.833
Biomass loading (X1)−8.651 ##−15.640 ##
Enzyme loading (X2)7.871 ##9.549 ##
Reaction time (X3)9.232 ##−11.097 ##
Biomass loading × biomass loading (X1 × X1)−18.894 ##−32.468 ##
Enzyme loading × enzyme loading (X2 × X2)−30.526 ##−33.709 ##
Reaction time × reaction time (X3 × X3) −16.813 ##−30.603 ##
Biomass loading × enzyme loading (X1 × X2)−2.4692.004
Biomass loading × reaction time (X1 × X3)−1.111−7.526 ##
Enzyme loading × reaction time (X2 × X3)0.566−1.466
R299.4%99.5%
Adj-R2 98.9%99.1%
Lack of fit0.1050.103
## Have significant effects to responses.
Table 6. Lists of enzymolysis conditions and sugar yields from corn cob.
Table 6. Lists of enzymolysis conditions and sugar yields from corn cob.
SubstanceCellulaseEnzymolysis ConditionsGL (mg·gds−1)XY (mg·gds−1)CECHCECRefs.
CCCellulase from Sigma-Aldrich, St. Louis, MO, USAEL 10.0 FPU·gds−1, BL 50 g·L−1, 48 h.433-60.9% [41]
CCCelluclast 1.5 L and Novozyme 188 from Sigma Co., St. Louis, MO, USAEL 61.62 FPU·gds−1 and 27 CBU·gds−1, BL 25 g·L−1, 96 h.500.8 81.2% [44]
CCCellulases from NovozymesEL 75.15 FPU·gds−1, BL 200 g·L−1, 72 h.408.2282.192.0%72.7%[54]
CCCellulases from NovozymesEL 10.0 FPU·gds−1, BL 100 g·L−1, 72 h.420 [55]
CCCommercial cellulase from Qingdao Vland Biological Co., Ltd., Qingdao, ChinaEL 31.1 FPU·gds−1, BL 50 g·L−1, 24 h.385.02221.2588.6%70.8%[56]
CCCellulase from NovozymesEL 31.1 FPU·gds−1, BL 10 g·L−1, 72 h.332.04-92.98%-[57]
CCTalaromyces verruculosus IIPC 324EL 2155.4 EU·gds−1, BL 75 g·L−1, 72 h.515.319.583.9%35.8%[58]
CCA. niger HQ-1EL 8.9 FPU·gds−1, BL 195 g·L−1,
Tween 80 3.0 g·L−1, 30.4 h.		518.48351.1497.2%82.9%This work
CC: corn cob; EL: enzyme loading; BL: biomass loading; GL: glucose yield, XY: xylose yield, CEC: cellulose conversion, HCEC: hemicellulose conversion; CBU: cellubioase activity unit; EU: endoglucanase activity unit.
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Zhang, H.; Wu, J. Statistical Optimization of Tween-80-Assisted Potassium Hydroxide Pretreatment and Enzymatic Hydrolysis for Enhancing Sugar Yields from Corn Cob. Fermentation 2023, 9, 1009. https://doi.org/10.3390/fermentation9121009

AMA Style

Zhang H, Wu J. Statistical Optimization of Tween-80-Assisted Potassium Hydroxide Pretreatment and Enzymatic Hydrolysis for Enhancing Sugar Yields from Corn Cob. Fermentation. 2023; 9(12):1009. https://doi.org/10.3390/fermentation9121009

Chicago/Turabian Style

Zhang, Hui, and Junhui Wu. 2023. "Statistical Optimization of Tween-80-Assisted Potassium Hydroxide Pretreatment and Enzymatic Hydrolysis for Enhancing Sugar Yields from Corn Cob" Fermentation 9, no. 12: 1009. https://doi.org/10.3390/fermentation9121009

APA Style

Zhang, H., & Wu, J. (2023). Statistical Optimization of Tween-80-Assisted Potassium Hydroxide Pretreatment and Enzymatic Hydrolysis for Enhancing Sugar Yields from Corn Cob. Fermentation, 9(12), 1009. https://doi.org/10.3390/fermentation9121009

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