Skip to main content

Optimization of oxalic acid pre-treatment and enzymatic saccharification in Typha latifolia for production of reducing sugar

Abstract

Background

Plants with high biomass can be manipulated for their reducing sugar content which ultimately upon fermentation produces ethanol. This concept was used to enhance the production of reducing sugar from cattail (Typha latifolia) by oxalic acid (OAA) pre-treatment followed by enzymatic saccharification.

Result

The optimum condition of total reducing sugar released from OAA pre-treatment was found to be 22.32 mg/ml (OAA—1.2%; substrate concentration (SC)—6%; reaction time (RT)—20 min) using one variable at a time (OVAT). Enzymatic saccharification yielded 45.21 mg/ml of reducing sugar (substrate concentration (SC)—2.4%; enzymatic dosage—50 IU/g; pH 7.0; temp—50 °C) using response surface methodology (RSM).

Conclusion

We conclude that Typha can be used as a potential substrate for large-scale biofuel production, employing economical bioprocessing strategies.

Background

Renewable biofuel from agricultural biomass is a replacement option for fossil fuel depletion and can overcome problems in energy security and economy. Yet, there are problems in performing large-scale biofuel production like technical knowledge and economic investment [3]. Aquatic weeds have high biomass content and grow uncontrollably in wetlands; hence, they can be used as a potential source for bioethanol production as opposed to agricultural crops that require monetary investment. Cattail (Typha latifolia) has high cellulose content constituted by polysaccharides and low cell wall integrity which could ease hydrolysis. Cellulose matrix is covered with lignin and other polysaccharides which can be converted into sugars by pre-treatment and enzymatic saccharification [22, 31].

Lignocellulosic biomass is a complex molecule surrounded by lignin and hemicellulose which resist enzymatic hydrolysis [24]. The conversion of cellulose to sugar requires the biomass to undergo oxalic acid pre-treatment which decreases the structural components by disrupting the lignin and crystalline cellulose [19], thereby enhancing enzymatic saccharification of the substrate. Therefore, the present study efforts to optimize pre-treatment and enzymatic saccharification in Typha, for maximal yield of reducing sugars by using one variable at a time (OVAT) technique and response surface methodology (RSM). The sugars produced can further be fermented to yield bioethanol, a source of biofuel.

Method

Sample collection

Matured plants (182–213 cm tall) were collected during spring-summer transition (March–April 2017) from Yelahanka Lake (28–30 °C), Karnataka. The shoots and leaves were washed, chopped (2–4 cm), dried (70 °C; 48 h), ground to 2 mm (particle size) and stored in a glass container according to the protocol of Waghmare et al. [28]. The formal identification of the plant material was conducted by the University of Agricultural Sciences, Bangalore. A voucher specimen was deposited in the publicly available herbarium (deposition number—UASB4606).

Cellulase enzyme source

Crude cellulase enzyme was obtained from culture broth of Bacillus cereus (MH 590292) isolated from soil sample, was grown in Czapek-Dox medium for 24 h under optimum conditions (45 °C, pH 7.0) and agitated at 120 rpm. The supernatant was collected for purification and further experimentation.

Cellulase enzyme purification

The culture broth was purified by the fractional ammonium sulphate precipitation method [5]. Supernatant collected previously was mixed with ammonium sulphate (80% saturation range), gently stirred (20 min; 4 °C) and incubated for 4–8 h. After precipitation, the mixture was centrifuged at 10,000×g at 4 °C for 15 min. The supernatant was discarded, and the pellet was mixed with 0.02 M Tris HCL buffer solution (pH 8.0). The crude enzyme in the buffer solution was taken into a dialysis bag (maintained at 4 °C) with 20 mM Tris buffer solution and changed twice. The enzyme mixture was then lyophilized, freeze-dried to powder and was used as a crude cellulase enzyme source for further experimentation.

Pre-treatment of biomass

Pre-treatment was performed by different concentrations of oxalic acid (0.4–2.0% w/w), substrate (2.0–8.0% w/v) and reaction time (5–30 min), for yield of sugars. Since pre-treatment was performed in autoclave, temperature and pressure were kept constant at 121 °C under 15 psi [11]. Oxalic acid concentration, substrate concentration and reaction time were optimized by OVAT.

Design of experiments by RSM

Central composite design (CCD) was used to optimize the enzymatic hydrolysis of biomass [10]. Four independent variables, pH, temperature, substrate concentration and enzyme dosage were selected for this purpose (Table 1). Measured response and independent variables are represented in second-order polynomial equation (Eq. 1).

$$ Y={\beta}_0+{\beta}_1{x}_1+{\beta}_2{x}_2+{\beta}_3{x}_3+{\beta}_4{x}_4+{\beta}_{11}{x}_1^2+{\beta}_{22}{x}_2^2+{\beta}_{33}{x}_3^2+{\beta}_{44}{x}_4^2+{\beta}_{55}{x}_5^2+{\beta}_{12}{x}_1{x}_2+{\beta}_{13}{x}_1{x}_3+{\beta}_{14}{x}_1{x}_4+{\beta}_{23}{x}_2{x}_3+{\beta}_{24}{x}_2{x}_4+{\beta}_{34}{x}_3{x}_4 $$
(1)
Table 1 The central composite design with four independent variables and the experimental results (C*—control)

Y Reducing sugar production in milligram/gram

β0 Regression coefficient of the model

β1, β2, β3 Linear effect of independent variables

β11, β22, β33 Square effect of independent variables

β12, β13, β14, β23, β24, β34 Interaction effect of selected independent variables

Statistical analysis

The CCD model data was analysed with Statistica 13.4 (TIBCO Software Inc. CA, USA). The statistical inference drawn was based on analysis of variance (ANOVA) and F test. The quality of second-order polynomial regression equation was assessed by regression of coefficient (R2). Further, the model predicted optimal levels of independent variables which were tested in triplicates, and the observed value was finally compared with the predicted value.

Optimization of enzymatic saccharification

Experiments were carried out in Erlenmeyer flasks (50 ml) containing sodium citrate buffer (50 mM; 20 ml) and sodium azide (2%) to prevent microbial contamination. All the parameters like substrate concentration, pH, temperature and enzyme dosage range were setup using RSM [25]. After 24 h of enzymatic saccharification, the samples were taken from the fermentable broth and centrifuged at 10,000×g for 5 min, the supernatant withdrawn for analysis of reducing sugars. The CCD model was used to optimize the effects of temperature (°C), pH, cellulase load (IU/g) and substrate concentration (%w/v) on enzymatic saccharification of cattail to obtain high sugar yield. In total, 26 experiments were designed (Table 1) for yield of reducing sugar and obtained by the following regression equation:

$$ Y=22.60-5.22{x}_1+3.40{x}_1^2-0.38{x}_2+0.008{x}_2^2+2.44{x}_3+0.16{x}_3^2-0.39{x}_4+0.001{x}_4^2+0.04{x}_1{x}_2-0.09{x}_1{x}_3-0.13{x}_1{x}_4-0.03{x}_2{x}_4-0.006{x}_2{x}_4-0.019{x}_3{x}_4 $$
(2)

Y Response of reducing sugar yield

X1 Independent variable of substrate concentration

X2 Enzyme load

X3 pH

X4 Temperature

Estimation of reducing sugar (DNS method)

The amount of total reducing sugar was determined by 3,5-dinitrosalicylic acid method [18]. Sample (1 ml) was mixed with 3,5-dinitrosalicylic acid (DNS) (2 ml) reagent in the test tube and immersed in boiling water bath for 5 min. The sample was cooled at room temperature and absorbance measured at 540 nm. The amount of reducing sugar liberated was calibrated by using standard glucose curve and expressed as milligram/milliliter.

Cellulase enzyme activity

Crude cellulase enzyme (0.5 ml) was mixed with 1% carboxymethyl cellulose (CMC; 0.5 ml) in 0.1 M sodium phosphate buffer (pH 7.0) at 37 °C for 30 min. DNS reagent (3 ml) was added to stop the reaction mixture which was boiled for 5 min in hot water bath. The absorbance was measured at 540 nm. One unit of enzyme activity was defined as the amount enzyme that liberates 1 μmol of glucose per millilitre per minute under specified condition [6].

Scanning electron microscopy

Scanning electron microscopy (SEM) was performed to understand the ultrastructural changes of untreated and treated biomass. The images were processed using gold sputter to get qualitative details of structural changes.

Fourier transform infrared spectroscopy

Fourier transform infrared spectroscopy (FTIR) was performed to understand the biochemical properties of untreated and pre-treated enzyme hydrolysed substrate. Samples were scanned within a range of 500–4000 cm−1.

Results

Enzyme source

Cellulase enzyme extracted from Bacillus cereus showed an activity of 62 IU/g of dry cellulase enzyme.

Pre-treatment of plant material

The highest reducing sugar yield (6.35 mg/ml) was observed at 1.2% of oxalic acid concentration (Fig. 1). Substrate concentration of 6% produced 16.32 mg/ml total reducing sugar (Fig. 2). Reaction time of 20 min (120 °C) recorded 22.32 mg/ml reducing sugar using oxalic acid pre-treatment under optimized condition (Fig. 3).

Fig. 1
figure1

Total reducing sugar (mg/ml) yield for respective OAA (%) at standard SC (1%). Dotted line represents the projected trend

Fig. 2
figure2

Total reducing sugar (mg/ml) yield for SC (%) at standard OAA (1.2%). Dotted line represents the projected trend

Fig. 3
figure3

Total reducing sugar (mg/ml) yield for respective reaction time (min). Dotted line represents the projected trend

Optimization of enzymatic saccharification

Enzymatic saccharification showed significant results (p < 0.0001; R2 = 0.96) (Table 2). RSM revealed that the yield gradually increased with rise in temperature (20–50 °C), SC (0.5–4.8% w/v) and enzyme load (10–50 IU/g) (Fig. 4a–c). Contrary to the experimental value of 42.13 mg/g (SC—2.4%; enzyme dosage—50 IU/g; pH 7.0; 50 °C), desirability and prediction model forecasted 45.21 mg/g of yield (Fig. 5a, b).

Table 2 Analysis of variance for the model regression for yield of reducing sugars
Fig. 4
figure4

Two-dimensional contour response surface plots showing the mean effects independent variables and their interaction on reducing sugars (mg/g) for a enzyme load vs substrate, b pH vs substrate and c temperature vs substrate

Fig. 5
figure5

a Desirability profile. b Model builder prediction for yield of reducing sugars using response surface methodology

SEM

SEM revealed the untreated sample as a flat, regular and compact structure. The hydrolysed sample exhibited disorganized patterns, corroded and internal fibres of cell wall components. Display of porosity on the surface of the treated sample indicated successful hydrolysis [2, 33] (Fig. 6a, b).

Fig. 6
figure6

Scanning electron micrograph. a Untreated. b Pre-treated and enzyme hydrolysed sample

FTIR

The band stretching at 890 cm−1 corresponded to the glycosidic deformation C–H bond and 1750 cm−1 to asymmetric and symmetric bond in lignin and cellulose [4]. Xu et al. [29] reported the wavelength range of lignin in rice straw from 1000 to 2500 cm−1 and yellow poplar from 800 to 2500 cm−1. Similarly, we observed the wavelength between 3000 and 4000 cm−1 which corresponds to lignin (Fig. 7). It is evident that the lignin composition varies in plant biomass.

Fig. 7
figure7

FTIR. Red colour—untreated biomass; blue colour—pre-treated and enzyme hydrolysed biomass

Discussion

Aquatic weeds unpleasantly occupy a major portion of the aquatic ecosystem. Therefore, research regarding generation of biofuels from them has been quite popular. This is evident because of a huge quantity of cellulose content in them. The utilization of their biomass ultimately depends on first, the extraction of sugars, and secondly, the exploitation of the fermentable capacity of extracted sugars for maximal ethanol generation [14, 32]. The present work is aimed at achieving the maximal sugar production under optimal and economic conditions.

Pre-treatment of plant biomass

Rattanaporn et al. [21] have used OAA pre-treatment to get an elevated sugar yield of 1.7 mg/ml in liquid phase thereby enhancing enzymatic saccharification. Vanegas et al. [27] used 6% of OAA pre-treatment on Laminaria digitata and Saccharina latissimi which released approximately 21 and 24 mg/ml of reducing sugar, respectively. Lee and Jeffries [13] suggested that oxalic acid belongs to dicarboxylic acid which yields high monomer sugar and produces a smaller number of inhibitors in hydrolysate. Indeed, the mentioned studies throw light on the applicability of the OAA pre-treatment and substantial yield of sugar. Correspondingly, we found the sugar yield of 6.35 mg/ml after using 1.2% oxalic acid.

We noted an increased yield up to 22.32 mg/ml at 120 °C and reaction time of 20 min. Since maximum reaction time is invested behind substrate conversion, sugar concentration is proportional to degradation time [20]. Higher biomass causes mechanical delay, uneven mix up of substrate and availability of water molecule to disintegrate lignin, hemicellulose and cellulose [17]. Lower temperatures and short reaction time (using concentrated sulphuric acid) have known to degrade sugars and release levulinic acid which inhibits ethanol production during fermentation [8, 12, 26].

Optimization of enzymatic saccharification

Maximum sugar yield is obtained at optimum conditions, whereas lower yield is due to unfavourable condition which has been previously reported by Yoonan and Kongkiattikajorn [30]. In combination, enzyme dosage ratio and temperature have known to influence sugar yield [16]. Our study revealed temperature and enzyme-substrate ratio to be the most important factors influencing the sugar yield during enzymatic saccharification.

Krishna and Chowdary [9] observed a maximal saccharification rate at 50 °C, which supports our study in that the maximum yield was noted at enzyme load 50 IU/g and temperature 50 °C, liable for sugar production. Beyond this condition, decline of hydrolysis rate, thermal deactivation, loss of enzyme adsorption and enzyme inhibition were reported by Robinson [23].

In our study, substrate concentration and enzyme dosage of 2.4% and 50 IU/g, respectively, showed a better fit for the releasing sugar. As substrate concentration and enzyme activity are inversely related, eventually, a saturation point will descend, where all the available enzyme will get exhausted thereby increasing the substrate concentration. Therefore, there will be no effect in hydrolysis, and hence, reduction in saccharification and sugar production is expected to occur in this situation [1].

In our study, pre-treated material underwent better hydrolysis of the native biomass structure. This enhanced the efficiency of enzymatic saccharification yielding high amount of sugar, which is similar to hydrolysis of sugarcane bagasse [15]. We observed maximum saccharification at substrate concentration of 2.4%, which depends on enzyme-substrate synergism. Higher substrate concentration inhibits the saccharification process [33].

Gregg and Saddler [7] suggested that 2% substrate concentration, 49.56 IU/g cellulase dosage and 50 °C are the best conditions for maximum yield. Their results corroborate our study since the influencing parameters for sugar production were in approximation with their outcomes.

Conclusion

The present work aims to enhance reducing sugar production from Typha latifolia using pre-treatment and enzymatic saccharification. In our study, the total reducing sugar content amounted to 22.32 mg/ml under pre-treated optimum conditions (OAA—1.2%; SC—6%; RT—20 min), while enzymatic saccharification yielded 45.21 mg/ml of reducing sugar (SC of 2.4%, enzymatic dosage—50 IU/g; pH 7.0; temp—50 °C). Rapid industrialization has seen a rise in energy demands, and preference has always been given to cost-effective strategies of energy generation. We attempted an effective economical method for bioconversion of this aquatic weed biomass into sugar content, thereby making it a potential substrate for bioethanol production.

Availability of data and materials

The authors declare that all generated and analysed data are included in the article.

Abbreviations

OAA:

Oxalic acid

SC:

Substrate concentration

RSM:

Response surface methodology

OVAT:

One variable at a time

CCD:

Central composite design

SEM:

Scanning electron microscopy

FTIR:

Fourier transform infrared radiation

IR:

Infrared radiation

References

  1. 1.

    Alrumman SA (2016) Enzymatic saccharification and fermentation of cellulosic date palm wastes to glucose and lactic acid. Braz J Microbiol 47:110–119

    Article  Google Scholar 

  2. 2.

    Cui L, Liu Z, Si C, Hui L, Kang N, Zhao T (2012) Influence of steam explosion pretreatment on the composition and structure of wheat straw. BioResources 7:4202–4213

    Article  Google Scholar 

  3. 3.

    Das S, Bhattacharya A, Haldar S, Ganguly A, Gu S, Ting YP, Chatterjee PK (2015) Optimization of enzymatic saccharification of water hyacinth biomass for bio-ethanol: comparison between artificial neural network and response surface methodology. Sustain Mater Technol 3:17–28

    Google Scholar 

  4. 4.

    Esteves B, Marques AV, Domingos I, Pereira H (2013) Chemical changes of heat-treated pine and eucalypt wood monitored by FTIR. Maderas Ciencia y Tecnología 15:245–258

    Google Scholar 

  5. 5.

    Fadel M (2000) Production physiology of cellulases and-glucosidase enzymes of Aspergillus niger grown under solid state fermentation conditions. J Biol Sci 1:401–404

    Google Scholar 

  6. 6.

    Ghose TK (1987) Measurement of cellulase activities. Pure Appl Chem 59(2):257–268

    Article  Google Scholar 

  7. 7.

    Gregg DJ, Saddler JN (1996) Factors affecting cellulose hydrolysis and the potential of enzyme recycle to enhance the efficiency of an integrated wood to ethanol process. Biotechnol Bioeng 51:375–383

    Article  Google Scholar 

  8. 8.

    Jonsson LJ, Martín C (2016) Pretreatment of lignocellulose: formation of inhibitory by-products and strategies for minimizing their effects. Bioresour Technol 199:103–112

    Article  Google Scholar 

  9. 9.

    Krishna SH, Chowdary GV (2000) Optimization of simultaneous saccharification and fermentation for the production of ethanol from lignocellulosic biomass. J Agric Food Chem 48(5):1971–1976

    Article  Google Scholar 

  10. 10.

    Kshirsagar SD, Waghmare PR, Loni PC, Patil SA, Govindwar SP (2015) Dilute acid pretreatment of rice straw, structural characterization and optimization of enzymatic hydrolysis conditions by response surface methodology. RSC Adv 5(58):46525–46533

    Article  Google Scholar 

  11. 11.

    Kundu C, Lee JW (2015) Optimization conditions for oxalic acid pretreatment of deacetylated yellow poplar ethanol production. J Ind Eng Chem 32:298–304

    Article  Google Scholar 

  12. 12.

    Laluce C, Igbojionu LI, Silva JL, Ribeiro CA (2019) Statistical prediction of interactions between low concentrations of inhibitors on yeast cells responses added to the SD-medium at low pH values. Biotechnol Biofuels 12:114

    Article  Google Scholar 

  13. 13.

    Lee J, Jeffries TW (2011) Efficiencies of acid catalysts in the hydrolysis of lignocellulosic biomass over a range of combined severity factors. Bioresour Technol 102:5884–5890

    Article  Google Scholar 

  14. 14.

    Madian HR, Sidkey NM, Elsoud AMM, Hamouda HI, Elazzazy AM (2019) Bioethanol production from water hyacinth hydrolysate by Candida tropicalis Y-26. Arab J Sci Eng 44:33–41

    Article  Google Scholar 

  15. 15.

    Mahamud MR, Gomes DJ (2012) Enzymatic saccharification of sugars cane bagasse by the crude enzyme from indigenous fungi. J Sci Res 4:227–238

    Article  Google Scholar 

  16. 16.

    Marcos EM (2012) Optimization of enzymatic hydrolysis conditions of steam-exploded wheat straw for maximum glucose and xylose recovery. J Chem Technol Biotechnol 88:237–246

    Article  Google Scholar 

  17. 17.

    Meilany D, Kresnowati MT, Setiadi T (2018) Temperature, solid loading and time effects on recovery of sugars from OPEFB. MATEC Web Conf 156:03022

    Article  Google Scholar 

  18. 18.

    Miller GL (1959) Use of dinitrosalicylic acid reagent for determination of reducing sugar. Anal Chem 31:426–428

    Article  Google Scholar 

  19. 19.

    Mosier N, Wyman C, Dale B, Elander R, Lee YY, Holtzapple M, Ladisch M (2005) Features of promising technologies for pretreatment of lignocellulosic biomass. Bioresour Technol 96:673–686

    Article  Google Scholar 

  20. 20.

    Rahmawati Y, Trisanti PN, Mayangsari NE (2015) The effect of decomposition time on cellulose degradation in ionic liquid/acid with pressurized CO2. Mod Appl Sci 9:69–73

    Article  Google Scholar 

  21. 21.

    Rattanaporn K, Tantayotai P, Phusantisampan T, Pornwongthong P, Sriariyanun M (2018) Organic acid pretreatment of oil palm trunk: effect on enzymatic saccharification and ethanol production. Bioprocess Biosyst Eng 41:467–477

    Article  Google Scholar 

  22. 22.

    Rebaque D, Martinez-Rubio R, Fornale S, Garcia-Angulo P, Alonso-Simon A, Alvarez JM, Encina A (2017) Characterization of structural cell wall polysaccharides in cattail (Typha latifolia): evaluation as potential biofuel feedstock. Carbohydr Polym 175:679–688

    Article  Google Scholar 

  23. 23.

    Robinson PK (2015) Enzymes: principles and biotechnological applications. Essays Biochem 59:1–41

    Article  Google Scholar 

  24. 24.

    Tanaka M (1979) Effect of chemical treatment on solubilization of crystalline cellulose and cellulosic wastes with Pellicularia filamentosa cellulase. J Ferment Technol 57:186–190

    Google Scholar 

  25. 25.

    Tasselli G, Filippucci S, D’Antonio S, Cavalaglio G, Turchetti B, Cotana F, Buzzini P (2019) Optimization of enzymatic hydrolysis of cellulosic fraction obtained from stranded driftwood feedstocks for lipid production by Solicoccozyma terricola. Biotechnol Rep 24:e00367

    Article  Google Scholar 

  26. 26.

    Torget RW, Kim JS, Lee YY (2000) Fundamental aspects of dilute acid hydrolysis/fractionation kinetics of hardwood carbohydrates. 1. Cellulose hydrolysis. Ind Eng Chem 39:2817–2825

    Article  Google Scholar 

  27. 27.

    Vanegas CH, Hernon A, Bartlett J (2015) Enzymatic and organic acid pretreatment of seaweed: effect on reducing sugars production and on biogas inhibition. Int J Ambient Energy 36:2–7

    Article  Google Scholar 

  28. 28.

    Waghmare PR, Watharkar AD, Jeon BH, Govindwar SP (2018) Bio-ethanol production from waste biomass of Pogonatherum crinitum phytoremediator: an eco-friendly strategy for renewable energy. 3. Biotech 8:158

    Google Scholar 

  29. 29.

    Xu F, Yu J, Tesso T, Dowell F, Wang D (2013) Qualitative and quantitative analysis of lignocellulosic biomass using infrared techniques: a mini-review. Appl Energy 104:801–809

    Article  Google Scholar 

  30. 30.

    Yoonan K, Kongkiattikajorn J (2005) A study of optimal conditions for reducing sugars production from cassava peels by diluted acid and enzymes. Kasetsart J (Nat Sci) 38:29–35

    Google Scholar 

  31. 31.

    Zhang Y, Lu C, Tang J (2011) Enhanced saccharification of steam explosion pretreated corn stover by the supplementation of thermoacidophilic β-glucosidase from a newly isolated strain, Tolypocladium cylindrosporum syzx4. Afr J Microbiol Res 5:2413–2421

    Google Scholar 

  32. 32.

    Zhang B, Joseph G, Wang L, Li X, Shahbazi A (2020) Thermophilic anaerobic digestion of cattail and hydrothermal carbonization of the digestate for co-production of biomethane and hydrochar. J Environ Sci Health A Tox Hazard Subst Environ Eng 55(3):230–238

    Article  Google Scholar 

  33. 33.

    Zheng Q, Zhou T, Wang Y, Cao X, Wu S, Zhao M, Guan X (2018) Pretreatment of wheat straw leads to structural changes and improved enzymatic hydrolysis. Sci Rep 8:1321

    Article  Google Scholar 

Download references

Acknowledgements

The authors express gratitude to the Government of Karnataka, Bangalore University and Fund for Improvement of Science & Technology (FIST) for providing laboratory facilities. The authors are thankful to the University of Agricultural Sciences, Bangalore, for their assistance in the identification of plant material. We thank Jyothy Institute of Technology (Bangalore) and B.M.S. College of Engineering (Bangalore) for facilitating us with Fourier transform infrared spectroscopy (FTIR) and scanning electron microscope (SEM) respectively.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

Author information

Affiliations

Authors

Contributions

KRS conceptualized the research idea and experimental analysis. PD designed and drafted the manuscript. KNL computed mathematical calculations and statistical analysis/software. STG revised the drafted manuscript and made necessary corrections. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Girisha Shringala Thimappa.

Ethics declarations

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ramaiah, S.K., Thimappa, G.S., Nataraj, L.K. et al. Optimization of oxalic acid pre-treatment and enzymatic saccharification in Typha latifolia for production of reducing sugar. J Genet Eng Biotechnol 18, 28 (2020). https://doi.org/10.1186/s43141-020-00042-w

Download citation

Keywords

  • Optimization
  • Oxalic acid
  • Pre-treatment
  • Reducing sugar
  • Saccharification
  • Typha latifolia