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. 2020 May 26;5(22):12603–12614. doi: 10.1021/acsomega.9b03681

Enhancing the Enzymatic Saccharification of Grain Stillage by Combining Microwave-Assisted Hydrothermal Irradiation and Fungal Pretreatment

Haiwei Ren †, Wenli Sun †, Zhiye Wang ‡, Shanfei Fu §,*, Yi Zheng #,*, Bing Song ∇, Zhizhong Li †, Zhangpu Peng ‡
PMCID: PMC7288354  PMID: 32548444

Abstract

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Grain stillage from the liquor industry was pretreated by using microwave-assisted hydrothermal pretreatment, fungal pretreatments, and their combination to enable efficient enzymatic hydrolysis for sugar production. The microwave-assisted hydrothermal (MH) pretreatment was optimized by using a response surface methodology, and the respective maximum reducing sugar yield and saccharification efficiency of 17.59 g/100 g and 33.85%, respectively, were achieved under the pretreatment conditions of microwave power = 120 W, solid-to-liquid ratio = 1:15 (g·mL–1), and time = 3.5 min. The fungal pretreatment with Phanerochaete chrysosporium digestion (PC) achieved the maximum ligninolytic enzyme activities in 6 days with 10% inoculum size at which the reducing sugar yield and saccharification efficiency reached 19.74 g/100 g and 36.29%, respectively. To further improve the pretreatment efficiency, MH and PC pretreatments were combined, but the sequence of MH and PC mattered on the saccharification efficiency. The MH + PC pretreatment (the MH prior to the PC) was better than PC + MH (the PC prior to the MH) in terms of saccharification efficiency. Overall, the MH + PC pretreatment achieved superior reducing sugar yield and saccharification efficiency (25.51 g/100 g and 66.28%, respectively) over all other studied pretreatment methods. The variations of chemical compositions and structure features of the raw and pretreated grain stillage were characterized by using scanning electron microscopy and Fourier transform infrared spectroscopy. The results reveal that both MH and PC pretreatments mainly functioned on delignification and decreasing cellulose crystallinity, thus enhancing the enzymatic saccharification of the pretreated grain stillage. The combined MH and PC pretreatment could be a promising method to enable cost-efficient grain stillage utilization for downstream applications such as biofuels.

1. Introduction

To reduce the greenhouse emission from the fuel consumption sector and enhance energy security, biorefinery has been studied as a sustainable way to produce fuels and chemicals that have been conventionally produced through petroleum refinery.1,2 Bioethanol from lignocellulosic materials is a great alternative to gasoline due to its clean-burning nature and no NOx/SOx emission.3 Lignocellulosic biomass is a more suitable feedstock for bioethanol production than food crops (corn and sugar cane) because it does not compete with food or feed supplies.4,5 About150–170 billion tons of lignocellulosic biomass have been generated every year all over the world, including agricultural residues, food processing byproducts, and forest products, which possess great potential to be used as feedstocks for biofuel production.6−8 The utilization of lignocellulosic biomass for biofuel production could have dual benefits in waste disposal and renewable energy generation.3

Grain stillage (locally called “Baijiu Diuzao”) is the solid residue from the Chinese liquor industry, which is one of the major industrial solid wastes in China. The annual grain stillage production in China is estimated to be more than 5 million metric tons, i.e., approximately two to four tons of grain stillage are generated per ton of distilled alcohol, which reflects the huge amount of associated solid wastes.9 At present, most of the grain stillage has been used as animal feeds due to its high protein content, but this practice is limited by the perishable characteristics and low rumen degradation efficacy of the grain stillage. In terms of the fiber composition, the grain stillage is primarily composed of cellulose (35–45%) and hemicellulose (15–25%), depending on the source, such as sorghum, rice, corn, and wheat.10 Because of its high carbohydrate content, the grain stillage has also been considered as a feedstock for biorefinery. Similar to all other lignocellulosic biomass, one of the key challenges of the grain stillage utilization for biorefinery is its structural recalcitrance to biodegradation by enzyme and microorganisms.11,12 As such, lignocellulosic biomass needs to be properly pretreated by using physical, chemical, biological, and/or combined methods to ease the downstream bioconversion.13−15 The selection of the pretreatment method and conditions should be well considered to achieve the trade-off between the preservation of cellulose and hemicellulose contents and the efficiency of the subsequent enzymatic saccharification.

Microwave radiation can directly apply the electromagnetic field to the molecular structure of biomass in the thermochemical conversion process for biofuel production.16 It has also been regarded as an alternative to the conventional heating method and recently attracted increasing attention in biomass pretreatment due to its advantages in better energy balance, direct heating, faster heating rate, shorter reaction time, higher efficiency, better temperature distribution, and easier reaction temperature control.17,18 Microwave radiation pretreatment can degrade the resistant lignocellulosic components to break down the lignin–cellulose–hemicellulose interlinkages by ionic conduction or dipolar rotation.19 The splitting of glycosidic linkages within the lignocellulosic matrix renders an easy enzymatic attack. Therefore, microwave radiation has been extensively studied to pretreat biomass and improve the bioconversion efficiency of biomass. It has been gradually scaled up from lab to pilot scales.20,21 Microwave radiation was used to aid the ionic liquid pretreatment of Eucalyptus to achieve high sugar yield of 411 mg/g in 48 h of enzymatic hydrolysis where the presence of microwave helped deconstruct lignin, remove hemicelluloses, break crystalline region, and create an eroded, pored, and irregular micromorphology.22 Gissibl et al.23 performed a microwave hydrothermal pretreatment on paramylon granules and significantly enhanced the enzymatic hydrolysis of such granules to bioactive oligosaccharides. In addition, biological pretreatment has also been considered as a low-cost and ecofriendly method to break down lignin and alter lignocellulosic structure to boost the enzymatic hydrolysis process.24 White-rot fungi, such as Phanerochaete chrysosporium, Trametes versicolor, and Pleurotus sajor-caju, have the unique ability to decompose lignocellulosic biomass and change its chemical composition, owing to their complex nonspecific extracellular enzymes, such as lignin peroxidase (LiP), manganese peroxidase (MnP), and laccases (Lac). Many species of white-rot fungi, which are highly selective lignin degraders, have been employed and examined in their ability to improve the biodegradability of lignocellulosic residues for ethanol or volatile fatty acid production.24−26 However, the long pretreatment period and low pretreatment efficiency are considered to be the major challenges of the fungal pretreatment. Both microwave-assisted hydrothermal irradiation and fungal pretreatments have been used for effective biomass pretreatment, but the combination of these two methods was rarely reported.

Therefore, in this paper, microwave-assisted hydrothermal (MH) and P. chrysosporium fungal (PC) pretreatments were studied separately. The conditions (time, power, and solid-to-liquid ratio of the MH pretreatment) were first optimized by using a Box–Behnken design-based response surface methodology, and the optimum pretreated time and inoculum size of the PC pretreatment were also determined. Meanwhile, the combination of the MH and PC pretreatments was investigated to further enhance the enzymatic saccharification of the grain stillage while the sequence of the MH and PC pretreatments (MH + PC vs. PC + MH) in the combined pretreatment was also studied. The efficiencies of different pretreatment methods were compared in terms of the yield of reduce sugar and saccharification efficiency upon enzymatic hydrolysis of the raw and pretreated grain stillage. Moreover, the chemical composition and structural variation that resulted from the pretreatments were characterized by using scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR).

2. Results and Discussion

2.1. Effects of the Power, Time, and Solid-to-Liquid Ratio on the Microwave-Assisted Hydrothermal Pretreatment

Microwave could cause vibration of polar bonds inside biomass as they align themselves with the magnetic field of the microwave. Appropriate MH conditions (e.g., solid loading, time, and power) could increase the collision frequency and movement of molecular chains, thus modifying the biomass structures, improving the effective adsorption of enzymes to the substrates, and enhancing enzymatic hydrolysis.27 As shown in Table 1, both reducing sugar yield and saccharification efficiency increased significantly with the increase in solid-to-liquid ratio from 1:5 to 1:15, and the maximal reducing sugar yield and saccharification efficiency reached 17.02 g/100 g and 33.59%, respectively, at a solid-to-liquid ratio of 1:15. Bichot et al.28 pointed out that the solid-to-liquid ratio is an important parameter during the microwave treatment as it is difficult to heat matter with a low water content. If the polar liquid content is less than 20% (w/w) in the reaction vessel, then motion of polar molecules, according to oscillating waves, is impossible. However, further increase in the solid-to-liquid ratio reduced the reducing sugar yield and saccharification efficiency because the frequency of the thermal energy-driven molecular collisions declined at a high water content, thus decreasing the pretreatment efficiency. The extension of pretreatment time from 0 to 3 min resulted in a significant increase of both reducing sugar yield and saccharification efficiency from 13.52 g/100 g to 16.26 g/100 g and from 26.68 to 32.08%, respectively. The microwave heating can enhance enzymatic saccharification of the biomass through fiber swelling and fragmentation as a result of the internal uniform and rapid heating of large biomass particles.29 However, if the pretreatment lasts too long (>3 min), solvent water absorbed overmuch energy, and the biomass sample was therefore heated in excess, which resulted in the extreme temperature and the evaporation of water, accounting for the decrease in reducing sugar yield and saccharification efficiency.28 Therefore, time is critical for water molecule penetration and heat transfer that leads to the loosing and swelling of lignocellulosic structures. As for the microwave power, respective maximal reducing sugar yield (17.29 g/100 g) and saccharification efficiency (34.12%) were achieved at microwave power of 150 W, whereas both reducing sugar yield and saccharification efficiency decreased significantly when the power was less or higher than 150 W. It should be noted that the suitable microwave power is actually a range of values rather than a fixed value given the fact that both power and time can determine the amount of thermal energy delivered to biomass materials during the MH pretreatment, i.e., the interactions between power and time should be considered when selecting MH pretreatment conditions. As such, appropriate combinations of power and time are important to achieve an efficient MH pretreatment. Based on the results from this single-factor study, the ranges of solid-to-liquid ratio (1:10, 1:15, and 1:20), time (2, 3, and 4 min), and power (0, 150, and 300 W) were selected for the following MH optimization experiments.

Table 1. Effect of Solid-to-Liquid Ratio, Time, and Power on the Yield of Reducing Sugars and Saccharification Efficiency.

factora level reducing sugar yield (g/100 g) saccharification efficiency (%)
solid-to-liquid ratio (g·mL–1) 1:5 14.32 ± 0.06d 28.26 ± 0.36d
1:10 14.75 ± 0.06c 29.10 ± 0.53c
1:15 17.02 ± 0.03a 33.59 ± 0.04a
1:20 15.12 ± 0.09b 29.84 ± 0.38b
1:25 14.37 ± 0.05d 28.36 ± 0.07d
time (min) 0 13.52 ± 0.06d 26.68 ± 0.27d
1 15.29 ± 0.04bc 30.17 ± 0.43bc
2 15.54 ± 0.04b 30.66 ± 0.42b
3 16.26 ± 0.04a 32.08 ± 0.49a
4 14.86 ± 0.05c 29.33 ± 0.42bc
5 15.20 ± 0.05bc 29.99 ± 1.40c
power (W) 0 13.52 ± 0.06e 26.68 ± 0.27e
150 17.29 ± 0.06a 34.12 ± 0.46a
300 16.17 ± 0.05b 31.90 ± 0.57b
450 13.88 ± 0.04de 27.39 ± 0.43de
600 14.33 ± 0.03c 28.28 ± 0.53c
750 14.14 ± 0.02 cd 27.90 ± 0.50 cd
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a

For each factor, values within the same columns (reducing sugar yield and saccharification efficiency) followed by different lowercase letters are significantly different at P < 0.05.

2.2. Optimization of the Microwave-Assisted Hydrothermal Pretreatment by RSM

With Box–Behnken design-based response surface methodology, 17 experiments in total were carried out to determine the effects of the solid-to-liquid ratio, time, power, and their interactions on both reducing sugar yield and saccharification efficiency. The experimental results are displayed in Table S1, and variance analysis of the second-order polynomial models was also determined. The final prediction mathematical models in terms of the coded factors are the following:

Reducing sugar yield

2.2. 1
2.2. 2

To test the significance of the developed model (eq 1), the ANOVA for reducing sugar yield was performed, and the results are presented in Table 2. The model F value of 36.00 shows a high significance for the regression model. As the P value for the model is <0.0001 for reducing sugar yield, the model equation can adequately describe the response. As to the lack of fit analysis, the P value is 0.9707, which implies that the lack of fit is not plausible. The R2 of the model is 0.9789, which indicates a high model correlation or agreement between the predicted and experimental data. The high value of the calculated adjusted determination factor (adjusted R2 = 0.9517) indicates that the estimated quadratic equation is appropriate to describe reducing sugar yield in terms of the studied factors. In brief, the developed model can describe the relationship between the selected factors and reducing sugar yield with sufficient adequacy. The F values for factors suggest that the solid-to-liquid ratio (A) and power (C) have significant effects on reducing sugar yield, but time (B) is insignificant. According to the F values for interactions, AB and AC interactions were found to have significant effects on reducing sugar yield, but the BC interaction does not affect reducing sugar yield significantly. Among square effects, A2 is the only insignificant term.

Table 2. Analysis of Variance (ANOVA) for the Response Surface of the Saccharification Efficiency and the Reducing Sugar Yielda.

source sum of squares df mean square F value p value
ANOVA for the Quadratic Model for Reducing Sugar Yield
model 14.17 9 1.57 36.00 < 0.0001***
A 0.55 1 0.55 12.49 0.0095**
B 0.01 1 0.01 0.18 0.6852
C 0.55 1 0.55 12.61 0.0093**
AB 2.53 1 2.53 57.82 0.0001***
AC 3.59 1 3.59 82.14 < 0.0001***
BC 0.05 1 0.05 1.06 0.3380
A2 0.02 1 0.02 0.41 0.5408
B2 2.37 1 2.37 54.24 0.0002***
C2 4.07 1 4.07 93.06 < 0.0001***
residual 0.31 7 0.04    
lack of fit 0.02 3 0.01 0.07 0.9707
R2 = 0.9789; adjusted R2 = 0.9517
ANOVA for the Quadratic Model for Saccharification Efficiency
model 178.79 9 19.87 26.06 0.0001***
A 6.28 1 6.28 8.24 0.0239*
B 0.01 1 0.01 0.026 0.9875
C 9.1 1 9.1 11.93 0.0106*
AB 0.14 1 0.14 0.18 0.6844
AC 7.37 1 7.37 9.67 0.0171*
BC 16.81 1 16.81 22.05 0.0022**
A2 8.66 1 8.66 11.36 0.0119*
B2 53.72 1 53.72 70.47 < 0.0001***
C2 64.35 1 64.35 84.42 < 0.0001***
residual 5.34 7 0.76    
lack of fit 3.69 3 1.23 3 0.8581
R2 = 0.9785; adjusted R2 = 0.9438
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a

*: significant; **: highly significant; and ***: extremely significant.

The shapes of 3D response plots determine the extent and nature of interactions between different process variables. If the nature of contour plates is circular then it indicates that the interactions are negligible or very weak. Strong or noticeable interactions are identified by the elliptical nature of the contour plots.30Figure 1a illustrates the interactive effects of the time and solid-to-liquid ratio on the reducing sugar yield while the power was retained at the median level.

Figure 1.

Figure 1

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(a–c) Response surfaces for the effects of SLR/ time, SLR/power, and time/power on the yield of reducing sugar (SLR: solid-to-liquid ratio). The coordinate axis of SLR ranged from 10 to 20 with an interval of two means; the solid-to-liquid ratios were 1:10, 1:12, 1:14, 1:16, 1:18, and 1:20, respectively.

The time in the range of 2–2.5 min and low solid-to-liquid ratio (less than 1:12) gave reducing sugar yield below 17.5 g/100 g. Simultaneous increments in the time (2–4 min) and solid-to-liquid ratio (1:10–1:20) increased the sugar yield from 18.26 g/100 g to 18.92 g/100 g. The higher reducing sugar yield observed with the increase in time and solid-to-liquid ratio could be attributed to the higher heating efficiency that unwound the lignocellulosic structures. Similarly, the power of 0 W and the highest solid-to-liquid ratio (1:20) gave the minimal sugar yields below 16.20 g/100 g (Figure 1b). Simultaneous increments in the power (from 0 to 300 w) and decrease in the solid-to-liquid ratio (from 1:20 to 1:10) increased the reducing sugar yield from 18.78 g/100 g to 19.02 g/100 g. Increasing the time and power above 150 W did not significantly influence the reducing sugar yield, indicating longer exposure to high irradiation power adversely affected the pretreatment efficiency, thus reducing the reducing sugar yield (Figure 1c).

The 2D and 3D elliptical contours shown in Figure 2a–c indicate that all three factors have strong effects on saccharification efficiency. As shown in Table 2, the solid-to-liquid ratio (A) and power (C) have higher F values than time (B), which suggests that the solid-to-liquid ratio and power have much stronger influence than time on saccharification efficiency. According to the P value, solid-to-liquid ratio and power are significant factors, but time is not. All quadratic terms except the AB (solid-to-liquid ratio and time interaction) are significant (p < 0.05), especially BC, B2, and C2. As shown in Figure 2a, maximum saccharification efficiency can be always achieved in the tested range of time (or solid-to-liquid ratio) at a specific solid-to-liquid ratio (or time). The similar results can be acquired from Figure 2b,c. Therefore, the selection of MH pretreatment conditions are important to determine the enzymatic hydrolysis efficiency, and the optimal solid-to-liquid ratio, time, and power to maximize saccharification efficiency in this study are within the selected level ranges of these factors. In addition, Table 2 also shows that the P value for the saccharification efficiency model (eq 2) is 0.0001, meaning the model is significant. Meanwhile, the lack of fit (P = 0.8581) is insignificant. The values of R2 and adjusted R2 for the model are 0.9785 and 0.9438, respectively, which indicate a high correlation between predicted and experimental data. The ANOVA results for saccharification efficiency demonstrate that this model (eq 2) can predict saccharification efficiency with sufficient accuracy in certain ranges of the solid-to-liquid ratio, power, and time.

Figure 2.

Figure 2

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(a–c) Response surfaces for the effects of SLR/time, SLR/power, and time/power on saccharification efficiency. SLR means solid-to-liquid ratio. The coordinate axis of SLR ranged from 10 to 20 with an interval of two means; the solid-to-liquid ratios were 1:10, 1:12, 1:14, 1:16, 1:18, and 1:20, respectively.

The predicted optimal conditions for maximizing reducing sugar yield and saccharification efficiency by response surface methodology were determined to be A = 1:15 g·mL–1, B = 3.56 min, and C = 123.75 W, which were validated in triplicate. The experimental optimal reducing sugar yield and saccharification efficiency were compared with the predicted values obtained from the model equations. In view of experimental operability, the abovementioned optimal conditions were slightly modified and used to validate the model predictability. Under the modified conditions (A = 1:15 g·mL–1, B = 3.5 min, C = 120 W), the reducing sugar yield and saccharification efficiency reached 17.59 g/100 g and 33.85%, respectively, which were consistent with the predicted results.

2.3. Fungal Pretreatment of Grain Stillage with P. chrysosporium

P. chrysosporium degrades lignin with the aid of its complex ligninolytic systems, which has been taken advantage to pretreat biomass for biofuel production. Therefore, the ligninolytic enzymes such as Lac, MnP, and LiP were measured during pretreatment. As shown in Figure 3a, both MnP and LiP activities increased with the pretreatment time (2–6 days) and then decreased at day 8. The respective maximal MnP and LiP activities of 440.08 and 207.97 U·L–1, respectively, occurred at the sixth day. However, the Lac activity kept increasing from 27.71 to 83.72 U·L–1 in eight days of pretreatment. These three enzymes have been reported to play critical roles in fungal pretreatment to remove lignin and enhance enzymatic hydrolysis of the pretreated biomass. Therefore, the initial rising of both reducing sugar yield (4–6 days) (Figure 3b) and saccharification efficiency (2–6 days) (Figure 3c) could be related to the increase in ligninolytic enzyme activities. As reported by Fang et al.,26 white-rot fungi possessed a stronger ability of delignification with the help of various secreted ligninolytic enzymes. Hence, it is reasonable to infer that MnP was the dominant ligninolytic enzyme during PC pretreatment. Figure 3b,c also shows that PC pretreatment of less than four days did not achieve significant effects on reducing sugar yield and saccharification efficiency. However, with the running of fungal pretreatment, the nutrient was quickly consumed, which led to a nutrient deficiency problem, and therefore, the activity of the fungus would be partly inhibited accompanied by the decreased ligninolytic enzyme activities.31

Figure 3.

Figure 3

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Fungal pretreatment of grain stillage with P. chrysosporium. (a–c) Effects of pretreatment time on the enzyme activities, the yield of reducing sugar, and saccharification efficiency, respectively. (d–f) Effects of inoculum size of P. chrysosporium on the enzyme activity, reducing sugar yield, and saccharification efficiency, respectively. Different lowercase letters on the top of columns indicate significant difference at P < 0.05 based on three replicates.

The inoculum size of PC pretreatment significantly affected the production of Lac, MnP, and LiP (Figure 3d) as well as reducing sugar yield and saccharification efficiency (Figure 3e,f). During the PC pretreatment, these three enzymes showed different variation trends. Lac activity increased from 30 to 60 U·L–1 upon the inoculum size increase from 5 to 10%, while further increase in inoculum size reduced the Lac activity. The MnP shared the similar trend with Lac, i.e., its maximum activity of 440 U·L–1 occurred at 10% inoculum size. However, the LiP reached its maximum activity of 210 U·L–1 with 15% inoculum size and did not show a monotonic change trend within the range of an inoculum size of 5–20%. These results are in agreement with the previous study done by Rothschild et al.32 Different activities among the three enzymes are likely due to the preferential use of carbon sources at different stages of the growth of P. chrysosporium. Lac is not a secondary metabolite and can be synthesized in the vegetative growth stage while its synthesis is strictly nitrogen limited. In contrast, LiP and MnP can be produced during the secondary metabolism with limited nitrogen and carbon.33 The degradation of lignin generally occurs in the secondary metabolism where LiP and MnP may flourish more than Lac, i.e., LiP and MnP appear to be more important than Lac for lignin degradation in the PC pretreatment with P. chrysosporium to enhance the enzymatic hydrolysis of pretreated biomass. Therefore, the decrease in ligninolytic enzyme activities at a higher inoculum size can be explained by the nutrient deficiency in the medium. As shown in Figure 3e,f, the highest reducing sugar yield and saccharification efficiency were achieved with 10% inoculum size. Overall, the PC pretreatment with 10% of the inoculum size for six days is the optimal pretreatment condition to achieve maximum reducing sugar yield and saccharification efficiency.

2.4. Comparison between Different Pretreatment Methods on the Reducing Sugar Yield and Saccharification Efficiency

As shown in Figure 4, all the pretreatment methods enhanced the reducing sugar yield and saccharification efficiency compared to the untreated grain stillage. It is surprising that the PC pretreatment resulted in significantly higher reducing sugar yield and saccharification efficiency than the MH pretreatment, which could be because the PC pretreatment is more effective in lignin degradation than the MH pretreatment. The combined MH and PC pretreatment achieved significantly higher reducing sugar yield and saccharification efficiency than the separate MH and PC pretreatment methods (P < 0.05). For the combined pretreatment, the order of MH and PC had no significant effect on the reducing sugar yield (P > 0.05), while MH + PC achieved significantly higher saccharification efficiency than PC + MH. This could be because the MH pretreatment increased the pore size and disrupted the lignocellulosic structure of the grain stillage, thus exposing a more accessible surface area to the PC and promoting the PC pretreatment. As such, more lignin can be degraded by the PC pretreatment via ligninolytic enzymes, which enabled more effective enzymatic hydrolysis to achieve higher saccharification efficiency for the MH + PC than the PC + MC.33

Figure 4.

Figure 4

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Comparison between different pretreatment methods upon the yield of reducing sugar (YRS) and saccharification efficiency (SE). CK: no treatment; MH: microwave hydrothermal pretreatment; PC: P. chrysosporium pretreatment; MH + PC: combined MH and PC pretreatment (MH followed by PC); PC + MH: combined PC and MH pretreatment (PC followed by MH). Note: Different lowercase letters (a–e) show significant differences among different pretreatment methods at P < 0.05 based on three replicates.

In addition, the reducing sugar yield from the pretreated grain stillage by MH + PC in this paper was much higher than the reported results (Table 3) even though different raw materials were used, i.e., distillers dried grains with solubles (DDGS) in the literature34−41 and grain stillage in this study. In sum, the combined pretreatment can maximize the advantages of the separate pretreatments and significantly improve the enzymatic hydrolysis efficacy.

Table 3. Comparison of Different Pretreatment Methods on the Yield of Reducing Sugar from Grain Stillage.

feedstocks pretreatment method and parameters components (%)a yield of reducing sugars (g/100 g) references
DDGS from the dry grind ethanol process ammonia fiber expansion carbohydrate 52.5crude fiber 6.5 13–30 Kim et al. (2008)
hydrothermal no date <60 Bootsma et al. (2008)
liquid hot water (LHW) no date 12–37.9 Kim et al. (2008)
acidic electrolyzed water (175 °C,10 min) cellulose 10.36hemicellulose 33.28lignin 1.05ADF 11.07NDF 44.45 23.25 Wang et al. (2013)
hot water (160 °C, 20 min) 17.01 Wang et al. (2013)
NaOH (140 °C,20 min) 15.01 Wang et al. (2013)
sulfuric acid (140 °C 20 min) 26.16 Wang et al. (2013)
ammonia fiber expansion cellulose 16, xylan8.2 arabinan 5.2, starch 5.2protein 26.4 16.68 Lau et al. (2007)
dilute sulfuric acid NDF 28.40crude fiber 7.82hemicellulose 17.2 38.3 Iram et al. (2019)
aqueous ammonia treatment 12.9 Iram et al. (2019)
steam explosion 5.5 Iram et al. (2019)
dilute sulfuricacid NDF 29.5ADF 12.9hemicellulose 16.5crude fiber 5.9 39.3 Cekmecelioglu et al. (2018)
condensedDDGS 0.5–1.0% (v/v) acid at 140 °C for 45 min cellulose 17hemicellulose 25.8lignin 8.7 49–57.0 Noureddini and Byun (2010)
dilute acid glucan 13.54, xylan 11.65 starch 2.58, cellulose 10.96 21.39 Li et al. (2019)
liquid hot water (LHW) 15.61
maize distillery stillage microwave pretreatment(300 W, 54 PSI, 15 min) cellulose 32.2 ± 1.4,hemicellulose 20.9 ± 1.2lignin 3.2 ± 1.9 10.40 Mikulski et al. (2019)
grain stillage from the liquor industry MH + PC cellulose 25.80hemicellulose 24.80lignin 14.87 25.51 this work
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a

DDGS: distillers dried grains with solubles; NDF: neutral detergent fiber; ADF: acid detergent fiber.

2.5. Effects of Pretreatment Methods on the Chemical Composition Change of the Grain Stillage

Lignin is a crucial barrier to efficient enzymatic hydrolysis of lignocellulosic biomass. Therefore, one aim of pretreatment is to remove the lignin and preserve cellulose and hemicellulose as much as possible. As shown in Table 4, the weight of all pretreated residues was reduced mainly because of delignification, partial hydrolysis of hemicellulose, and dissolution of nonstructural components, extractives, and ash. The biomass loss after single MH and PC pretreatments were only 3.40 and 4.74%, respectively, but the combined pretreatments (PC + MH and MH + PC) caused significant mass loss, 23.54 and 39.43%, respectively. The degrees of delignification by single MH or PC pretreatment are 16.86 and 29.08%, respectively, which was approximate with the result of sugarcane bagasse pretreated with white-rot fungus.42 A higher delignification degree in the PC-pretreated grain stillage is because of the fact that P. chrysosporium can produce robust ligninolytic enzymes that are able to degrade lignin to achieve a high delignification degree during pretreatment.40 Upon combined pretreatment, the delignification degrees were significantly increased to 32.80 (PC + MH) and 43.34% (MH + PC). Moreover, the delignification degree of the MH + PC pretreatment is significantly higher than that of the PC + MH. This could be because the MH pretreatment causes the breakage of hydrogen bonds and the destruction of lignocellulose structure by explosion and disruption, which promotes the subsequent P. chrysosporium attack for delignification.11,43 Therefore, the selective delignification could be the main mechanism of the MH + PC pretreatment to enhance the enzymatic saccharification efficiency of the pretreated grain stillage, which could also explain the results in Figure 4.

Table 4. Lignocellulosic Compositions of Raw and Pretreated Grain Stillages and Biomass Loss during Pretreatment.

samplea GS (dry basis, g) pretreated GS (dry basis, g) mass loss (%) cellulose (%, w/w) hemicellulose (%, w/w) lignin (%, w/w) delignification degree (%)
raw grain stillage 50 50 0 25.80 ± 1.03c 24.80 ± 0.98a 14.87 ± 0.14a  
MH 50 48.31 3.40 32.98 ± 1.54b 25.38 ± 1.01a 12.80 ± 0.18c 16.86 ± 0.25b
PC 50 47.64 4.74 25.13 ± 2.20c 24.19 ± 1.60b 11.07 ± 0.03d 29.08 ± 0.13c
PC + MH 50 38.24 23.54 32.85 ± 1.93b 22.65 ± 1.01d 13.07 ± 0.27c 32.80 ± 1.38a
MH + PC 50 30.29 39.43 36.63 ± 2.55a 23.19 ± 1.89c 13.91 ± 0.12b 43.34 ± 1.46a
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a

GS: grain stillage; MH: microwave-assisted hydrothermal pretreatment; PC: P. chrysosporium pretreatment; MH + PC: combined microwave-assisted hydrothermal and P. chrysosporium pretreatment; PC + MH: combined P. chrysosporium and microwave-assisted hydrothermal pretreatment. Values within the same columns followed by different lowercase letters are significantly different at P < 0.05.

2.6. Microstructural Changes of the Grain Stillage before and after Pretreatment

2.6.1. Scanning Electron Microscopy Analysis

As shown in Figure S1, significant morphological differences were observed between the untreated and pretreated grain stillage, which indicates certain disruption of the lignocellulosic structure by the pretreatment. It is clear that the untreated grain stillage shows a compact, dense, uniform, and smooth surface with few erosion traces, reflecting the tight cellulose–hemicellulose–lignin network. Different pretreatment methods could have different working modes in modifying biomass morphology. The MH-pretreated grain stillage displayed an eroded, porous, and irregular surface. The removal of lignin could contribute to the surface morphology changes, i.e., delignification causes cell wall disruption and fragmented surface appearance.44 A good colonization is a prerequisite for an effective fungal pretreatment of lignocellulosic materials.

In the PC pretreatment, P. chrysosporium digestion accompanied by the invasion of mycelium striped the outer layer of biomass and altered the organized polymer structure so that the PC-pretreated biomass could exhibit fragmented and highly porous structures with disintegration of cellular integrity.45 Roughly, the MH-pretreated grain stillage has more erosion traces on its surface compared to the PC-pretreated grain stillage. The combination of MH and PC resulted in severer modifications on the grain stillage structures than the single-method pretreatment due to the combined effects of microwave irradiation (e.g., dipolar rotation induced molecular collisions) and fungal digestion pretreatment (e.g., mycelial invasion). The PC + MH pretreatment fragmentized the outer layers of the grain stillage and loosened the polymer structure. The unfolded surfaces of the pretreated grain stillage suffered significant erosions that led to observable ravines and holes. The disruption of grain stillage structures and the presence of porosity could increase the accessibility of grain stillage to enzymes and improve the subsequent enzymatic hydrolysis.

2.6.2. Fourier Transform Infrared Spectroscopy Analysis

As shown in Figure S2a, the absorption peak at 897 cm–1 could be attributed to the damaged β-glycosidic linkages within cellulosic structures. The band peak at 1024 cm–1 shows the vibration of the pyranose ring (C–O–C) structures, while the band at 1152 cm–1 demonstrates asymmetric elongation of C–O–C within cellulose. The broad absorption peak at 1236 cm–1 was observed in the bands that are associated with hemicellulose and assigned to the vibration of the ring C–O–C in hemicellulose. The absorption peak at 1370 cm–1 corresponds to the twisting vibration of C–H bonds within the cellulosic components. Similarly, the band peaks that occurred at 1490and 1492 cm–1 are related to the asymmetric vibration of C–H deformation in the cellulosic structures (Figure S2b). The absorption peak at 1645 cm–1 is due to the vibration of C=O bonds of the carbonyl and carboxyl groups in lignin. The sharp absorption peak at 2918 cm–1 can be attributed to the C–H stretching vibration and shows a slight increase in peak intensity (Figure S2a), indicating that the methyl and methylene portions of cellulose were ruptured by the pretreatments.46,47 The broad band detected at 3436 cm–1 can be related to the stretching vibrations of H-boned −OH groups in the cellulose structures (Figure S2a).

Furthermore, the fingerprint region has been plotted from a wavenumber of 2000 to 500 cm–1 (Figure S2b). A small peak at 587 cm–1 region corresponds to the out-of-plane bending of C–O–H.48 The shift of the absorption peaks at 1000–1200 cm–1 after pretreatment indicates the increase in the cellulose content in the pretreated grain stillage samples, which is in agreement with the results of the composition analysis in Table 4. The significant reduction in the intensity of bands at 1587 cm–1 is assigned for lignin, which confirms the lignin solubilization via pretreatment.49 FTIR spectra also provide information for the qualitative analysis of cellulose crystallinity according to the absorption ratio of A1430 cm–1/A898 cm–1, which was known as the lateral order index (LOI).50 The LOI of the untreated grain stillage samples was calculated to be 1.18 (Table 5). The decrease in LOI after pretreatment indicates that the pretreatment could effectively disrupt the crystalline structure and enhance the digestibility of grain stillage.

Table 5. Lateral Order Index (LOI) of the Grain Stillage before and after Different Pretreatments.
sample grain stillage MH PC MH + PC PC + MH
lateral order index (LOI) 1.18 1.06 1.10 0.95 0.99
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3. Conclusions

All these four pretreatment methods, including microwave-assisted hydrothermal (MH), fungal (by P. chrysosporium) (PC), and combined MH and PC pretreatments (MH + PC and PC + MH), efficiently improved the yield of reducing sugar and saccharification efficiency of the grain stillage upon enzymatic hydrolysis. The solid-to-liquid ratio, power, and time are critical factors affecting the efficiency of MH pretreatment. Under the optimal MH pretreatment conditions of solid-to-liquid ratio = 1:15 g·mL–1, time = 3.5 min, and power = 120 W, the respective maximum reducing sugar yield and saccharification efficiency were 17.59 g/100 g and 33.85%, respectively. For PC pretreatment, six days and 10% inoculum achieved the highest reducing sugar yield and saccharification efficiency of 19.74 g/100 g and 36.29%, respectively, which was related to the production of ligninolytic enzymes by P. chrysosporium. The combined MH and PC pretreatments were better than the separate MH and PC pretreatments, but the order of the MH and PC in the combined pretreatment had a significant effect on the enzymatic hydrolysis in terms of saccharification efficiency. The pretreatment method of MH + PC was better than that of PC + MH because the MH prior to the PC made the substrate much more accessible to the PC attack that led to more efficient delignification. The chemical composition and structure analyses revealed that the MH and PC pretreatments worked mainly through delignification accompanied by hemicellulose removal and cellulose crystallinity reduction. Therefore, microwave-assisted hydrothermal irradiation followed by fungal pretreatment could be a promising pretreatment method for the bioconversion of the grain stillage to biofuels.

4. Materials and Methods

4.1. Materials

Grain stillage was obtained from Jinghui Liquor Co., Ltd. (Gansu, China). The grain stillage samples were dried at 80 °C for 48 h in the oven and sieved through a screen to get particles of 200–250 μm. The cellulose, hemicellulose, and lignin contents of the grain stillage were determined to be 25.80 ± 1.03, 24.80 ± 0.98, and 14.87 ± 0.18% (dry weight basis), respectively. In addition, the contents of protein, starch, lipid, and ash were 17.37 ± 2.47, 8.06 ± 0.31, 3.04 ± 0.01,, and 3.51 ± 0.04% (dry weight basis), respectively. The rest of the other components was about 2.59 ± 0.02%. The white-rot fungus P. chrysosporium (CICC 40299) was obtained from the China Center of Industrial Culture Collection (CICC) and maintained on PDA (potato dextrose agar) slants at 4 °C. Fibrolytic enzymes (containing cellulase = 1 × 103 U·g–1, xylanase =5 × 102 U·g–1, and β-glucosidase = 1 × 102 U·g–1) produced by Penicillium sp. were purchased from the Imperial Jade Bio-technology Co., Ltd. (Ningxia, China) for enzymatic hydrolysis.

4.2. Single-Factor Study of Microwave-Assisted Hydrothermal Pretreatment

The MH pretreatment was carried out by using a microwave reactor (GAS-800, Xianghu Technology Co., Ltd., Beijing, China). For each batch experiment, 20 g of dried grain stillage was added to a 100-mL polytetrafluoroethylene vessel and mixed with distilled water at different solid-to-liquid ratios followed by hydrothermal pretreatment with microwave. The pretreatment parameters being studied in this section included the irradiation power (0–750 W), time (0–5 min), and solid-to-liquid ratio (1:5–1:25 g·mL–1). In each experiment, one factor was variable, and the other factors were kept constant on the basis of the preliminary experiment. At the end of pretreatment, the reactor was promptly cooled down in an ice bath for 10 min, and the solid residues were recovered by vacuum filtration. The solid residues were then thoroughly washed with distilled water until the pH of the filtrate water reached neutral. The washed solid residues were air dried and collected for enzymatic hydrolysis.

4.3. Optimization of Microwave-Assisted Hydrothermal Pretreatment

Based on the results from the single factor experiment, response surface methodology based on the Box–Behnken design was employed to optimize the solid-to-liquid ratio (A), microwave irradiation time (B), and microwave irradiation power (C) to maximize the yield of reducing sugar and saccharification efficiency of enzymatic hydrolysis of the pretreated grain stillage.51 The statistical analysis of experimental data was conducted by using Design Expert (version 8.06, Stat-Ease Inc., Silicon Valley, CA, USA), and the data were fitted by using the liner regress model (eq 3)

4.3. 3

where Y1 is the predicted reducing sugar yield; Y2 is the predicted saccharification efficiency; β0 is the model constant; βi, βii, and βij represent the linear, quadratic and interaction coefficients, respectively; and X1, X2, and X3 correspond to the studied factors (Table 6). Randomized experimental arrangement is shown in Table S1. This model evaluates the effects of each factor and their interactions on the responses.52 Analysis of variance (ANOVA) was used to test the adequacy of the developed model and statistical significance of the regression coefficients. The goodness of fit of the polynomial equation was expressed by the coefficient of determination R,2 i.e., a good model has a large R.2 Analysis also included the F-test and its associated probability P(F) (Pcritical = 0.05). Response surface and contour plots (3D surface plots) were drawn to show both the effects of factors on the response and the interactions between the significant factors. In addition, five confirmatory experiments were conducted to validate the model.53,54

Table 6. Factors and Levels for the Box–Behnken Design by Response Surface Methodology.

    level
code parameters –1 0 1
X1 (A) solid-to-liquid ratio (g·mL–1) 1:10 1:15 1:20
X2 (B) microwave irradiation time (min) 2 3 4
X3 (C) microwave irradiation power (W) 0 150 300
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4.4. Fungal Pretreatment by P. chrysosporium

P. chrysosporium reserved 4 °C was subcultured for 7 days before use and then inoculated onto PDA plates at 28 °C for 7 days. Six-millimeter-diameter discs cut from the margin of active fungal cultures on PDA were inoculated into a 500 mL cotton-plugged Erlenmeyer flask containing 100 mL of potato dextrose broth (PDB) medium (pH 5.5) and incubated at 28 °C, 180 r·min–1 for 5–7 days in a shaking incubator. After incubation, P. chrysosporium with a dry weight of 5.0 g/L were collected as a seed culture.33 Fungal pretreatment was performed in working volume 150 mL flasks containing 50 g of grain stillage (dry basis). Erlenmeyer flasks containing wet grain stillage were autoclaved at 121 °C for 20 min prior to inoculation with different volumes of seed culture (seed solutions of 7.5, 15, 22.5, and 30 mL, respectively, corresponding to inoculum sizes of 5, 10, 15, and 20%, v/v). Pretreatment was carried out in a constant temperature incubator at 28 °C under a static condition for 2, 4, 6, and 8 days. All the cultures were grown in triplicate. At the sampling points, flasks were taken out of the incubator and put in a freezer at −20 °C for 2 min to stop the pretreatment process. All insoluble substrates obtained after cultivation were freeze-dried to constant weight for the determination of mass losses during the pretreatment. The freeze-dried solid residues were used for composition analysis and subjected to enzymatic hydrolysis to evaluate saccharification efficacy. The activity of ligninolytic enzymes (LiP, Lac, and MnP) in an aqueous phase was determined. Samples without fungal inoculation were used as controls.

4.5. Combined Microwave and Fungal Pretreatments

Two combined pretreatment methods, such as MH followed by PC (MH + PC) and PC followed by MH (PC + MH), were investigated, respectively. For MH + PC pretreatment, the grain stillage samples (solid-to-liquid ratio = 1:15) were pretreated by MH at a microwave power of 120 W for 3.5 min. After MH, the pretreated solid was collected and sterilized at 121 °C for 20 min prior to the PC pretreatment with the inoculation size of 10% for six days. After the PC pretreatment, the pretreated solid was collected for enzymatic saccharification chemical compositions analysis. The PC + MH pretreatment was done similarly to the MH + PC except that the sequence of MH and PC was opposite.

4.6. Enzymatic Saccharification

Five grams of the dried pretreated grain stillage sample was mixed with 100 mL of citrate buffer solution (0.05 M, pH 4.8), and fibrolytic enzymes (enzyme loading of 150 U/g of the dry matter substrate) were added to initiate the reaction. The reactors with sodium azide (0.02%, w/v) were incubated at 50 °C for 72 h with a shaking speed of 150 rpm. After enzymatic hydrolysis, the hydrolyzed grain stillage slurries were centrifuged at 15,000 rpm for 10 min, and the supernatants were collected as hydrolysates for reducing sugar analysis. As a control for comparison, the raw grain stillage sample also underwent the same enzymatic hydrolysis as the pretreated grain stillage. The content of reducing sugars was quantified by using the 3,5-dinitrosalicylic acid method using glucose as the standard.53 The efficiency of the enzymatic hydrolysis was evaluated in terms of the yield of reducing sugars and saccharification efficiency, which were calculated by using eqs 4 and 5

4.6. 4
4.6. 5

Where, the factor 0.9 is the conversion coefficient of glucose to cellulose.12

4.7. Analytical Methods

4.7.1. Chemical Compositions

Neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), and ash were determined according to the analytical procedures developed by the National Renewable Energy Laboratory (NREL).55 The differences between ADF and NDF, ADF and ADL, and ADL and ash were calculated as hemicellulose, cellulose, and Klason lignin, respectively. The starch content was determined according to the NREL-TP-510-42624 protocol,44 and the protein was measured and calculated as Kjeldahl nitrogen.56 The degree of delignification was calculated as follows (eq 6)

4.7.1. 6

4.7.2. Activity of Ligninolytic Enzymes from P. chrysosporium

LiP, Lac, and MnP are the extracellular enzymes from P. chrysosporium and play important roles for delignification. Lac, MnP, and LiP activities were measured spectrophotometrically according to the described methods in the literature.57,58 The molar absorptivities for MnP and LiP activities are 11,590 and 9.3 cm–1·M–1, respectively. The lignocellulolytic enzyme production basal medium contained (per liter) 3 g of KH2PO4, 1.5 g of MgSO4·7H2O, 1 g of CaCl2·H2O, 0.5 g of MnSO4·H2O, and 0.1 g of CuSO4·H2O. Lac, MnP, and LiP activities were calculated as follows (eqs 7–9):

4.7.2. 7
4.7.2. 8
4.7.2. 9

4.7.3. Scanning Electron Microscopy for Structure Characterization

SEM analysis was performed using a JSM-5600LV scanning electron microscope (Jeol Instruments, Tokyo, Japan). Samples were prepared by dispersing dry powder on a double-sided conductive adhesive tape and coated with carbon with the arc discharge method. All samples were sputtered with gold by a high-resolution sputter coater before the microscopic observation. Samples were scanned in secondary electrons for morphology with an accelerating voltage of 20 kV.

4.7.4. Fourier Transform Infrared Spectroscopy for Structure Characterization

The dried samples were prepared by grinding with KBr at a ratio of 1:100 (w/w) and pressing into pellets. An FTIR spectrometer (Nexus 670, Thermo Nicolet Co.) was used for showing the changes of the functional groups in the untreated and pretreated grain stillage. The spectra were recorded in the absorption band mode between 4000and 500 cm–1 with the resolution of 4 cm–1 over 20 scans per sample.59

4.8. Data Analysis

All experiments were duplicated unless specified otherwise. SPSS 12.0 (IBM SPSS 12.0 software, New York, USA) was used to conduct statistical analysis, such as error and significant difference. Design Expert software (Version 8.06, Stat-Ease Inc., Silicon Valley, CA, USA) was used to design the experiments and analyze the results. The effects of each factor and interactions between factors on responses were analyzed by ANOVA. The model fitting was analyzed by determining P values (significance levels = 0.05).

Acknowledgments

This work was financially supported by the National Natural Science Foundation of China (nos. 51666010, 51366009), China Postdoctoral Science Foundation Funded Project (nos. 2018 M631217, 2019 T120961), West Light Foundation of Chinese Academy of Sciences (no. 2018XBZG XBQNXZ A), Red Willow First-class Discipline Project (no. 0807 J1), and Excellent Young Cultivation Project (no. YQ2018) of Lanzhou University of Technology.

Glossary

Abbreviations

microwave-assisted hydrothermal

MH

P. chrysosporium fungal digestion

PC

lignin peroxidase

LiP

laccases

Lac

manganese peroxidase

MnP

scanning electron microscopy

SEM

fourier transform infrared spectroscopy

FTIR.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.9b03681.

  • Box–Behnken design matrix, scanning electron micrographs, and Fourier-transform infrared spectra (PDF)

Author Contributions

H. R. and W. S. equally contributed to this work and should be considered co-first author.

The authors declare no competing financial interest.

Supplementary Material

ao9b03681_si_001.pdf (814.4KB, pdf)

References

  1. Behera S.; Arora R.; Nandhagopal N.; Kumar S. Importance of chemical pretreatment for bioconversion of lignocellulosic biomass. Renewable Sustainable Energy Rev. 2014, 36, 91–106. 10.1016/j.rser.2014.04.047. [DOI] [Google Scholar]
  2. Tsui T. H.; Ekama G. A.; Chen G. H. Quantitative characterization and analysis of granule transformations: Role of intermittent gas sparging in a super high-rate anaerobic system. Water Res. 2018, 139, 177–186. 10.1016/j.watres.2018.04.002. [DOI] [PubMed] [Google Scholar]
  3. Han W.; Xu X.; Gao Y.; He H.; Chen L.; Tian X.; Hou P. Utilization of waste cake for fermentative ethanol production. Sci. Total Environ. 2019, 673, 378–383. 10.1016/j.scitotenv.2019.04.079. [DOI] [PubMed] [Google Scholar]
  4. Rios-Gonzalez L. J.; Morales-Martinez T. K.; Rodriguez-Flores M. F.; Rodriguez-De la Garza J. A.; Castillo-Quiroz D.; Castro-Montoya A. J.; Martinez A. Autohydrolysis pretreatment assessment in ethanol production from agave bagasse. Bioresour. Technol. 2017, 242, 184–190. 10.1016/j.biortech.2017.03.039. [DOI] [PubMed] [Google Scholar]
  5. Yu Q.; Tan X.; Zhuang X.; Wang Q.; Wang W.; Qi W.; Zhou G.; Luo Y.; Yuan Z. Co-extraction of soluble and insoluble sugars from energy sorghum based on a hydrothermal hydrolysis process. Bioresour. Technol. 2016, 221, 111–120. 10.1016/j.biortech.2016.08.118. [DOI] [PubMed] [Google Scholar]
  6. Guo H.; Wang X. D.; Lee D. J. Proteomic researches for lignocellulose-degrading enzymes: A mini-review. Bioresour. Technol. 2018, 265, 532–541. 10.1016/j.biortech.2018.05.101. [DOI] [PubMed] [Google Scholar]
  7. Zhang Z.; Harrison M. D.; Rackemann D. W.; Doherty W. O. S.; O’Hara I. M. Organosolv pretreatment of plant biomass for enhanced enzymatic saccharification. Green Chem. 2016, 18, 360–381. 10.1039/C5GC02034D. [DOI] [Google Scholar]
  8. Qing Q.; Guo Q.; Zhou L.; Wan Y.; Xu Y.; Ji H.; Gao X.; Zhang Y. Catalytic conversion of corncob and corncob pretreatment hydrolysate to furfural in a biphasic system with addition of sodium chloride. Bioresour. Technol. 2017, 226, 247–254. 10.1016/j.biortech.2016.11.118. [DOI] [PubMed] [Google Scholar]
  9. Ye G.; Luo H.; Ren Z.; Ahmad M. S.; Liu C.-G.; Tawab A.; Al-Ghafari A. B.; Omar U.; Gull M.; Mehmood M. A. Evaluating the bioenergy potential of Chinese Liquor-industry waste through pyrolysis, thermogravimetric, kinetics and evolved gas analyses. Energy Convers. Manage. 2018, 163, 13–21. 10.1016/j.enconman.2018.02.049. [DOI] [Google Scholar]
  10. Ren H.; Zhao T.; Xing J.; Zhang Y.; Li Z.; Wang Y.; Ma W. Isolation and Characterization of Microcrystalline Cellulose from Distillers Grains. J. Biobased Mater. Bioenergy 2015, 9, 515–522. 10.1166/jbmb.2015.1550. [DOI] [Google Scholar]
  11. Selig M.; Weiss N.; Ji Y., Enzymatic saccharification of lignocellulosic biomass. National Renewable Energy Laboratory NREL/TP-510-42629 ;Golden:Colo, USA: 2008. [Google Scholar]
  12. Frederix M.; Mingardon F.; Hu M.; Sun N.; Pray T.; Singh S.; Simmons B. A.; Keasling J. D.; Mukhopadhyay A. Development of an E. coli strain for one-pot biofuel production from ionic liquid pretreated cellulose and switchgrass. Green Chem. 2016, 18, 4189–4197. 10.1039/C6GC00642F. [DOI] [Google Scholar]
  13. Song B.; Yu Y.; Wu H. Solvent effect of gamma-valerolactone (GVL) on cellulose and biomass hydrolysis in hot-compressed GVL/water mixtures. Fuel 2018, 232, 317–322. 10.1016/j.fuel.2018.05.154. [DOI] [Google Scholar]
  14. Su T.-C.; Fang Z. One-Pot Microwave-Assisted Hydrolysis of Cellulose and Hemicellulose in Selected Tropical Plant Wastes by NaOH-Freeze Pretreatment. ACS Sustainable Chem. Eng. 2017, 5, 5166–5174. 10.1021/acssuschemeng.7b00509. [DOI] [Google Scholar]
  15. Li J.; Zhang M.; Wang D. Enhancing delignification and subsequent enzymatic hydrolysis of corn stover by magnesium oxide-ethanol pretreatment. Bioresour. Technol. 2019, 279, 124–131. 10.1016/j.biortech.2019.01.123. [DOI] [PubMed] [Google Scholar]
  16. Diaz A. B.; Moretti M. M.; Bezerra-Bussoli C.; Carreira Nunes Cda C.; Blandino A.; da Silva R.; Gomes E. Evaluation of microwave-assisted pretreatment of lignocellulosic biomass immersed in alkaline glycerol for fermentable sugars production. Bioresour. Technol. 2015, 185, 316–323. 10.1016/j.biortech.2015.02.112. [DOI] [PubMed] [Google Scholar]
  17. Aguilar-Reynosa A.; Romaní A. M.; Rodríguez-Jasso R.; Aguilar C. N.; Garrote G.; Ruiz H. A. Microwave heating processing as alternative of pretreatment in second-generation biorefinery: An overview. Energy Convers. Manage. 2017, 136, 50–65. 10.1016/j.enconman.2017.01.004. [DOI] [Google Scholar]
  18. Mikulski D.; Klosowski G.; Menka A.; Koim-Puchowska B. Microwave-assisted pretreatment of maize distillery stillage with the use of dilute sulfuric acid in the production of cellulosic ethanol. Bioresour. Technol. 2019, 278, 318–328. 10.1016/j.biortech.2019.01.068. [DOI] [PubMed] [Google Scholar]
  19. Sewsynker-Sukai Y.; Kana E. B. G. Microwave-assisted alkalic salt pretreatment of corn cob wastes Process optimization for improved sugar recovery. Ind. Crops Prod. 2018, 125, 284–292. 10.1016/j.indcrop.2018.08.086. [DOI] [Google Scholar]
  20. Cao H.; Fan D.; Jiao X.; Huang J.; Zhao J.; Yan B.; Zhou W.; Zhang W.; Zhang H. Effects of microwave combined with conduction heating on surimi quality and morphology. J. Food Eng. 2018, 228, 1–11. 10.1016/j.jfoodeng.2018.01.021. [DOI] [Google Scholar]
  21. Li H.; Qu Y.; Yang Y.; Chang S.; Xu J. Microwave irradiation--A green and efficient way to pretreat biomass. Bioresour. Technol. 2016, 199, 34–41. 10.1016/j.biortech.2015.08.099. [DOI] [PubMed] [Google Scholar]
  22. Hou X.; Wang Z.; Sun J.; Li M.; Wang S.; Chen K.; Gao Z. A microwave-assisted aqueous ionic liquid pretreatment to enhance enzymatic hydrolysis of Eucalyptus and its mechanism. Bioresour. Technol. 2019, 272, 99–104. 10.1016/j.biortech.2018.10.003. [DOI] [PubMed] [Google Scholar]
  23. Gissibl A.; Care A.; Parker L. M.; Iqbal S.; Hobba G.; Nevalainen H.; Sunna A. Microwave pretreatment of paramylon enhances the enzymatic production of soluble beta-1,3-glucans with immunostimulatory activity. Carbohydr. Polym. 2018, 196, 339–347. 10.1016/j.carbpol.2018.05.038. [DOI] [PubMed] [Google Scholar]
  24. García-Torreiro M.; López-Abelairas M.; Lu-Chau T. A.; Lema J. M. Fungal pretreatment of agricultural residues for bioethanol production. Ind. Crops Prod. 2016, 89, 486–492. 10.1016/j.indcrop.2016.05.036. [DOI] [Google Scholar]
  25. Fang W.; Zhang P.; Zhang X.; Zhu X.; van Lier J. B.; Spanjers H. White rot fungi pretreatment to advance volatile fatty acid production from solid-state fermentation of solid digestate: Efficiency and mechanisms. Energy 2018, 162, 534–541. 10.1016/j.energy.2018.08.082. [DOI] [Google Scholar]
  26. Fang W.; Zhang X.; Zhang P.; Carol Morera X.; van Lier J. B.; Spanjers H. Evaluation of white rot fungi pretreatment of mushroom residues for volatile fatty acid production by anaerobic fermentation: Feedstock applicability and fungal function. Bioresour. Technol. 2020, 297, 122447. 10.1016/j.biortech.2019.122447. [DOI] [PubMed] [Google Scholar]
  27. Virtanen T.; Svedström K.; Andersson S.; Tervala L.; Torkkeli M.; Knaapila M.; Kotelnikova N.; Maunu S. L.; Serimaa R. A physico-chemical characterisation of new raw materials for microcrystalline cellulose manufacturing. Cellulose 2011, 19, 219–235. 10.1007/s10570-011-9636-6. [DOI] [Google Scholar]
  28. Bichot A.; Lerosty M.; Radoiu M.; Méchin V.; Bernet N.; Delgenès J.-P.; García-Bernet D. Decoupling thermal and non-thermal effects of the microwaves for lignocellulosic biomass pretreatment. Energy Convers. Manage. 2020, 203, 112220. 10.1016/j.enconman.2019.112220. [DOI] [Google Scholar]
  29. Hassan S. S.; Williams G. A.; Jaiswal A. K. Emerging technologies for the pretreatment of lignocellulosic biomass. Bioresour. Technol. 2018, 262, 310–318. 10.1016/j.biortech.2018.04.099. [DOI] [PubMed] [Google Scholar]
  30. Tsegaye B.; Balomajumder C.; Roy P. Optimization of microwave and NaOH pretreatments of wheat straw for enhancing biofuel yield. Energy Convers. Manage. 2019, 186, 82–92. 10.1016/j.enconman.2019.02.049. [DOI] [Google Scholar]
  31. Sedighi M.; Karimi A.; Vahabzadeh F. Involvement of ligninolytic enzymes of Phanerochaete chrysosporium in treating the textile effluent containing Astrazon Red FBL in a packed-bed bioreactor. J. Hazard. Mater. 2009, 169, 88–93. 10.1016/j.jhazmat.2009.03.070. [DOI] [PubMed] [Google Scholar]
  32. Rothschild N.; Novotný C.; Šašekb V.; Dosoretz C. G. Ligninolytic enzymes of the fungus Irpex lacteus (Polyporus tulipiferae): isolation and characterization of lignin peroxidase. Enzyme Microb. Technol. 2002, 31, 627–633. 10.1016/S0141-0229(02)00171-0. [DOI] [Google Scholar]
  33. Saha B.; Qureshi N.; Kennedy G. J.; Cotta M. A. Biological pretreatment of corn stover with white-rot fungus for enzymatic hydrolysis and bioethanol production. Int. Biodeterior. Biodegrad. 2016, 109, 29–35. 10.1016/j.ibiod.2015.12.020. [DOI] [Google Scholar]
  34. Kim Y.; Mosier N.; Ladisch M. R. Process simulation of modified dry grind ethanol plant with recycle of pretreated and enzymatically hydrolyzed distillers’ grains. Bioresour. Technol. 2008, 99, 5177–5192. 10.1016/j.biortech.2007.09.035. [DOI] [PubMed] [Google Scholar]
  35. Bootsma J. A.; Entorf M.; Eder J.; Shanks B. H. Hydrolysis of oligosaccharides from distillers grains using organic-inorganic hybrid mesoporous silica catalysts. Bioresour. Technol. 2008, 99, 5226–5231. 10.1016/j.biortech.2007.09.033. [DOI] [PubMed] [Google Scholar]
  36. Wang Y.; Yuan B.; Ji Y.; Li H. Hydrolysis of hemicellulose to produce fermentable monosaccharides by plasma acid. Carbohydr. Polym. 2013, 97, 518–522. 10.1016/j.carbpol.2013.05.017. [DOI] [PubMed] [Google Scholar]
  37. Lau M. W.; Dale B. E.; Balan V. Ethanolic fermentation of hydrolysates from ammonia fiber expansion (AFEX) treated corn stover and distillers grain without detoxification and external nutrient supplementation. Biotechnol. Bioeng. 2008, 99, 529–539. 10.1002/bit.21609. [DOI] [PubMed] [Google Scholar]
  38. Iram A.; Cekmecelioglu D.; Demirci A. Optimization of dilute sulfuric acid, aqueous ammonia, and steam explosion as the pretreatments steps for distillers’ dried grains with solubles as a potential fermentation feedstock. Bioresour. Technol. 2019, 282, 475–481. 10.1016/j.biortech.2019.03.009. [DOI] [PubMed] [Google Scholar]
  39. Cekmecelioglu D.; Demirci A. A statistical optimization study on dilute sulfuric scid pretreatment of distillers dried grains with solubles (DDGS) as a potential feedstock for fermentation applications. Waste Biomass Valorization 2018, 3243–3249. 10.1007/s12649-018-0376-9. [DOI] [Google Scholar]
  40. Noureddini H.; Byun J. Dilute-acid pretreatment of distillers’ grains and corn fiber. Bioresour. Technol. 2010, 101, 1060–1067. 10.1016/j.biortech.2009.08.094. [DOI] [PubMed] [Google Scholar]
  41. Thanh Mai Pham L.; Kim Y. H. Discovery and characterization of new O-methyltransferase from the genome of the lignin-degrading fungus Phanerochaete chrysosporium for enhanced lignin degradation. Enzyme Microb. Technol. 2016, 82, 66–73. 10.1016/j.enzmictec.2015.08.016. [DOI] [PubMed] [Google Scholar]
  42. Sasaki C.; Takada R.; Watanabe T.; Honda Y.; Karita S.; Nakamura Y.; Watanabe T. Surface carbohydrate analysis and bioethanol production of sugarcane bagasse pretreated with the white rot fungus, Ceriporiopsis subvermispora and microwave hydrothermolysis. Bioresour. Technol. 2011, 102, 9942–9946. 10.1016/j.biortech.2011.07.027. [DOI] [PubMed] [Google Scholar]
  43. Kan X.; Zhang J.; Tong Y. W.; Wang C. H. Overall evaluation of microwave-assisted alkali pretreatment for enhancement of biomethane production from brewers’ spent grain. Energy Convers. Manage. 2018, 158, 315–326. 10.1016/j.enconman.2017.12.088. [DOI] [Google Scholar]
  44. Li X.; Chen S.; Yu Y.; Wang S.; Xu Z.; Huang H.; Jin M. Ethanol production from mixtures of Distiller’s Dried Grains with Solubles (DDGS) and corn. Ind. Crops Prod. 2019, 129, 59–66. 10.1016/j.indcrop.2018.11.075. [DOI] [Google Scholar]
  45. Zabed H. M.; Akter S.; Yun J.; Zhang G.; Awad F. N.; Qi X.; Sahu J. N. Recent advances in biological pretreatment of microalgae and lignocellulosic biomass for biofuel production. Renewable Sustainable Energy Rev. 2019, 105, 105–128. 10.1016/j.rser.2019.01.048. [DOI] [Google Scholar]
  46. Kumar R.; Mago G.; Balan V.; Wyman C. E. Physical and chemical characterizations of corn stover and poplar solids resulting from leading pretreatment technologies. Bioresour. Technol. 2009, 100, 3948–3962. 10.1016/j.biortech.2009.01.075. [DOI] [PubMed] [Google Scholar]
  47. Zhang H.; Ye G.; Wei Y.; Li X.; Zhang A.; Xie J. Enhanced enzymatic hydrolysis of sugarcane bagasse with ferric chloride pretreatment and surfactant. Bioresour. Technol. 2017, 229, 96–103. 10.1016/j.biortech.2017.01.013. [DOI] [PubMed] [Google Scholar]
  48. Qiao X.; Zhao C.; Shao Q.; Hassan M. Structural Characterization of Corn Stover Lignin after Hydrogen Peroxide Presoaking Prior to Ammonia Fiber Expansion Pretreatment. Energy Fuels 2018, 32, 6022–6030. 10.1021/acs.energyfuels.8b00951. [DOI] [Google Scholar]
  49. Liu C. F.; Xu F.; Sun J. X.; Ren J. L.; Curling S.; Sun R. C.; Fowler P.; Baird M. S. Physicochemical characterization of cellulose from perennial ryegrass leaves (Lolium perenne). Carbohydr. Res. 2006, 341, 2677–2687. 10.1016/j.carres.2006.07.008. [DOI] [PubMed] [Google Scholar]
  50. Lin R.; Cheng J.; Song W.; Ding L.; Xie B.; Zhou J.; Cen K. Characterisation of water hyacinth with microwave-heated alkali pretreatment for enhanced enzymatic digestibility and hydrogen/ methane fermentation. Bioresour. Technol. 2015, 182, 1–7. 10.1016/j.biortech.2015.01.105. [DOI] [PubMed] [Google Scholar]
  51. Ghosh P.; Thakur I. S. Biodegradation of pyrene by Pseudomonas sp. ISTPY2 isolated from landfill soil: Process optimisation using Box-Behnken design model. Bioresource Technol. Rep. 2019, 100329. 10.1016/j.biteb.2019.100329. [DOI] [Google Scholar]
  52. Gu P.; Xu S.; Zhou S.; Liu Z.; Sun Y.; Ou N.; Hu Y.; Liu J.; Wu Y.; Wang X.; Wang D. Optimization of angelica sinensis polysaccharide-loaded Poly (lactic-co-glycolicacid) nanoparticles by RSM and its immunological activity in vitro. Int. J. Biol. Macromol. 2018, 107, 222–229. 10.1016/j.ijbiomac.2017.08.176. [DOI] [PubMed] [Google Scholar]
  53. Singh A.; Bajar S.; Bishnoi N. R. Physico-chemical pretreatment and enzymatic hydrolysis of cotton stalk for ethanol production by Saccharomyces cerevisiae. Bioresour. Technol. 2017, 244, 71–77. 10.1016/j.biortech.2017.07.123. [DOI] [PubMed] [Google Scholar]
  54. Wang J.; Zhao Y.; Li W.; Wang Z.; Shen L. Optimization of polysaccharides extraction from Tricholoma mongolicum Imai and their antioxidant and antiproliferative activities. Carbohydr. Polym. 2015, 131, 322–330. 10.1016/j.carbpol.2015.06.009. [DOI] [PubMed] [Google Scholar]
  55. Ren H.; Feng Y.; Liu T.; Li J.; Wang Z.; Sa F.; Zheng Y.; Peng Z. Effects of different simulated seasonal temperatures on the fermentation characteristics and microbial community diversities of the maize straw and cabbage waste co-ensiling system. Sci. Total Environ 2020, 708, 135113. 10.1016/j.scitotenv.2019.135113. [DOI] [PubMed] [Google Scholar]
  56. Sluiter A.; Hames B.; Ruiz R.; Scarlata C.; Sluiter J.; Templeton D.; Crocker D. Determination of structural carbohydrates and lignin in biomass. Laboratory analytical procedure 2007, 1. [Google Scholar]
  57. Oliver C. M.; Mawson R.; Melton L. D.; Dumsday G.; Welch J.; Sanguansri P.; Singh T. K.; Augustin M. A. Sequential low and medium frequency ultrasound assists biodegradation of wheat chaff by white rot fungal enzymes. Carbohydr. Polym. 2014, 111, 183–190. 10.1016/j.carbpol.2014.04.028. [DOI] [PubMed] [Google Scholar]
  58. Das R.; Li G.; Mai B.; An T. Spore cells from BPA degrading bacteria Bacillus sp. GZB displaying high laccase activity and stability for BPA degradation. Sci. Total Environ. 2018, 640-641, 798–806. 10.1016/j.scitotenv.2018.05.379. [DOI] [PubMed] [Google Scholar]
  59. Wang Z.; Hou X.; Sun J.; Li M.; Chen Z.; Gao Z. Comparison of ultrasound-assisted ionic liquid and alkaline pretreatment of Eucalyptus for enhancing enzymatic saccharification. Bioresour. Technol. 2018, 254, 145–150. 10.1016/j.biortech.2018.01.021. [DOI] [PubMed] [Google Scholar]

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