Sequential acid hydrolysis and enzymatic saccharification of coconut coir for recovering reducing sugar: Process evaluation and optimization

Accepted Manuscript Sequential acid hydrolysis and enzymatic saccharification of coconut coir for recovering reducing sugar: Process evaluation and optimization

Marttin Paulraj Gundupalli, Debraj Bhattacharyya PII: DOI: Reference:

S2589-014X(19)30015-5 https://doi.org/10.1016/j.biteb.2019.01.015 BITEB 143

To appear in:

Bioresource Technology Reports

Received date: Revised date: Accepted date:

23 November 2018 17 January 2019 18 January 2019

Please cite this article as: M.P. Gundupalli and D. Bhattacharyya, Sequential acid hydrolysis and enzymatic saccharification of coconut coir for recovering reducing sugar: Process evaluation and optimization, Bioresource Technology Reports, https://doi.org/ 10.1016/j.biteb.2019.01.015

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ACCEPTED MANUSCRIPT Sequential Acid Hydrolysis and Enzymatic Saccharification of Coconut Coir for Recovering Reducing Sugar: Process Evaluation and Optimization

Department of Civil Engineering, IIT Hyderabad, Kandi – 502285

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Marttin Paulraj Gundupalli1 , Debraj Bhattacharyya1*

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Corresponding author mail ID: [email protected]

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Abstract

Sulphuric acid hydrolysis of coconut coir under case I (time: 2-10 min; temperature: 160-

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240o C; acid concentration: 0.2-0.7 % w/w) and case II (time: 10-60 min; temperature: 100160o C; acid concentration: 0.7-2 % w/w) was studied. The optimal conditions for maximum

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recovery of reducing sugar for case I and case II were 8.2 min, 200°C, and 0.21% w/w acid ; and 13 min, 155°C, and 1.27% w/w acid. Under these conditions, 52% and 48% of reducing

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sugar were recovered. Enzymatic saccharification was performed after hydrolysis using cellulase and β-glucosidase enzymes. Glucose yields of 90% and 55% were obtained in case I and case II, respectively. Changes in structure and functional groups in solid were observed when studied using SEM, XRD, and FTIR. The aromatic layer was removed in case I and cellulose layer was exposed. Crystallinity increased from 42 to 54% in case I and from 42 to 45% in case II.

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ACCEPTED MANUSCRIPT Keywords: coconut coir; hydrolysis; reducing sugar; response surface methodology; saccharification; sulphuric acid Abbreviation RSM – Response Surface Methodology; CrI - Crystallinity Index; CCD – Central Composite Design; DNS – Dinitrosalicylic acid; ANOVA –Analysis of Variance; RS – Reducing sugar;

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NREL – National Renewable Energy Laboratory; FTIR – Fourier Transform Infrared

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Spectroscopy; LDL - Low detection Limit; HDL - High detection limit; SEM – Scanning

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Electron Microscope; GHG - Greenhouse gas

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1. Introduction

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Growing demand for energy and constant depletion of fossil fuel have has stimulated considerable research interest in the area of alternative and renewable energy such as ethanol,

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biodiesel, biogas, etc. Besides, GHG and other pollutants pose a serious threat to humankind (Nizami et al., 2017). Biofuels are produced from carbohydrate-rich plant materials and

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organic wastes by physicochemical and biological processes. (Sivakumar et al., 2010). Fractionation and conversion of biomass to fuel and other chemical byproducts are

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considered promising methods to ensure a sustainable economy (Yamakawa et al., 2018). A wide range of lignocellulosic biomass has been used in the production of bioethanol to date. Lignocellulose biomass is a cellulose-rich plant material with layers of lignin and hemicellulose which are removed in a the hydrolysis step (Luo et al., 2018). The exposed cellulosic polymer is then degraded to glucose monomers during enzymatic saccharification (Binod et al., 2018). These glucose monosaccharides are fermented to ethanol using common yeast species like Saccharomyces cerevisiae (Manfredi et al., 2018). Chemical hydrolysis is an essential step to disrupt the complex matrix of carbohydrates and lignin in lignocellulosic 2

ACCEPTED MANUSCRIPT biomass. It is necessary to make cellulose accessible for saccharification to release fermentable sugars. Different hydrolysis techniques have been tried for different types of biomass,

such as warm water, dilute acid/base, ammonia, organosolv, ionic liquid,

microwave, biological and hybrid methods (Amarasekara, 2013). However, considerable work has to be done to make the processes commercially feasible. It is necessary to develop

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an effective hydrolysis method and optimum conditions to recover maximum sugar and

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enhance the cellulose digestibility during saccharification process.

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It has been reported that fractions of hemicellulose and lignin in lignocellulosic biomass can be removed by dilute acid hydrolysis process catalyzed mostly by conventional acids, such as

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sulphuric acid (Mahmoodi et al., 2018). Hemicellulose is a polysaccharide containing xylose as a primary key component. The process of dilute acid hydrolysis results in a hydrolysate

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which rich in xylose and soluble lignin (Mahmoodi et al., 2018). In addition to xylose, the hydrolysate may also include some monosaccharides such as glucose, galactose, mannose,

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and arabinose. Diluted acid hydrolysis can be carried out with short reaction time (5 - 8 min) and at high temperature (180 - 200o C) or larger reaction time (15-60 min) and lower

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temperature (110 – 160o C). These two different states, as reported for other lignocellulosic biomass, have shown a significant difference in xylose recovery and saccharification yield

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(Terán Hilares et al., 2018).

The dilute acid hydrolysis may result in the formation of by-

products such as furfural and 5 – Hydroxymethyl furfural compounds. These compounds act as inhibitors for yeast metabolism, growth, and fermentation (Chaturvedi et al., 2018). Dilute acid

hydrolysis has always been reported to be sufficient for high lignin content

lignocellulosic biomass In this paper, the potency of coconut coir, lignocellulosic biomass, as a source of biofuel has been studied. Coconut fiber is extracted from mesocarp layers of coconut, which is about 25% coconut shell (Satyanarayana et al., 2007). With an annual production of more than 3

ACCEPTED MANUSCRIPT 150,000 tons, India is the world's leading producer of coconut coir (Kumar et al., 2017). Coconut coir has a chemical composition of 12-18% hemicellulose, 40-45% cellulose and 4550% lignin (Shamim et al., 2016). With such high lignin content, dilute acid hydrolysis technique is likely to be suitable for the hydrolysis process. In this study, two cases were considered for recovery of reducing sugar from coconut coir: case I – shorter retention time,

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higher temperature and low acid concentration; 2) case II – longer retention time, lower

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temperature and high acid concentration. Optimization of the process parameters was done

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using CCD of RSM. The objective was to maximize the reducing sugar recovery and to minimize the conversion of sugar to furan-based inhibitory compounds. The structural

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features of the initial coir sample and pretreated coir residue were investigated by SEM, XRD, and FTIR. Enzymatic saccharification for the pretreated solids under different

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conditions was also evaluated and studied using commercial enzymes such as cellulase and β

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2. Materials and methods

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- Glucosidase.

2.1 Collection and characterization of coconut coir

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Coconut coir obtained from an agro-based industry on the outskirt of the city of Hyderabad in India. The coir was processed for particle size reduction and further sieved with 0.9 mm mesh. The prepared sample was then oven dried at 45°C for 48 hours (Rabelo et al., 2009). The dried coir was stored in a refrigerator at 4°C for further use. Sugars such as glucose, xylose, mannose, arabinose, galactose, and lignin were analyzed according to the NREL method (Ruiz et al., 2011). Biomass samples were also subjected to two-stage acid hydrolysis: (1) 72% w/w of sulphuric acid for 1 hour at 30°C and (2) 4% w/w of sulphuric

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ACCEPTED MANUSCRIPT acid for 1 hour in an autoclave at 121°C. The solid residues obtained after the two-step hydrolysis were considered for determining acid insoluble lignin and ash content.

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2.2 Design of experiments

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Optimization of the process parameters to recover reducing sugar from coconut coir was done using a circumscribed CCD of Response Surface Methodology. This study was conducted in

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two cases, 1) Case I – shorter retention time, higher temperature and low acid concentration;

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2) Case II – longer retention time, lower temperature and high acid concentration. The independent variables were time (X1 , min); temperature (X2 , o C) and acid concentration (X3 ,

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% w/w). Each independent variables were examined at five coding levels (-α, -1, 0, 1, +α). The coded levels for the independent variables are shown in Table 1.

A default α-value of

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𝑋𝑖 − 𝑋𝑜 ∆𝑋

(1)

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𝑥𝑖 =

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1.687 was used for the axial runs (Montgomery, 2001).

where xi is the coded value of the independent variable, Xi is the uncoded value of the independent variable, X0 is the uncoded value of the independent variable at the center point, and △X is the step change value of the independent variable. Experimental data shown in Table 2 were selected to evaluate the influence of each independent variable on the response. This was then analyzed to obtain optimal process conditions to recover maximum reducing sugar. Recovery of reducing sugar is considered as a response variable, which is represented by the term, YI, and YII. In this study, the quadratic equation as shown in Eq.2 was used: 5

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𝑌 = 𝛽𝑜 + ∑ 𝛽𝑖 𝑋𝑖 + 𝑖=3

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∑ 𝛽𝑖𝑖 𝑋𝑖2 𝑖 =3

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+ ∑ ∑ 𝛽𝑖𝑖 𝑋𝑖 𝑋𝑗

(2)

𝑖 =1 𝑖 =3

where Y is the response, Xi and Xj are the independent variables, β0 is the constant

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coefficient, βi is the ith linear coefficient, βii is the quadratic coefficient, and βij is the ijth

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interaction coefficient. CCD consist of (a) 2k factorial points, (b) 2k axial points (± α), and (c)

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six central points, where k is the number of independent variables. For each case of dilute acid hydrolysis, a total of 20 experimental runs with 8 per factorial design, 6 per axial points

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and six runs at central points were conducted. Experiment design and data processing were

2.3 Acid hydrolysis of coconut coir

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done using Statistica 7.0.

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Coconut coir was treated with dilute sulphuric acid for hydrolysis of the hemicellulosic fraction and modification of lignin structure. The hydrolysis step was carried out in a non-

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stirred high-pressure batch reactor as shown in Fig. 1 with 10% solids loading. The solid to liquid ratio in the reactor was maintained at about 1:10 (w/v).

The coir samples were

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pretreated for different retention time, temperature and acid concentration as shown in Table 2. The non-stirred high-pressure batch reactor consists of a stainless steel vertical pressure vessel (SS 316) designed to withstand a working pressure of 100 bar and temperature of 300o C. The operational temperature of the process is controlled by a temperature relay and the flow of water from the chiller to the SS coil hose installed in the reactor. The liquid hydrolysate is collected from the vertical dip tube during and after the hydrolysis process. The non-stirred high-pressure reactor is also equipped with a pressure and temperature sensor. Furthermore, pressure valves and regulators were provided for the flow of inert gases 6

ACCEPTED MANUSCRIPT into the reactor. In this study, however, there was no inflow of any gas into the reactor during the hydrolysis process. After the hydrolysis step, the collected hydrolysate was subjected to vacuum filtration. The filtrate was analyzed for reducing sugar using the DNS method (Miller, 1959) and the data was later used for developing the second order model by multiple regression analysis. Further, the solids obtained after vacuum filtration were subjected to

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enzymatic saccharification to maximize the sugar yield.

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2.4 Quantification of sugars

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2.4.1 Quantification of total reducing sugar

Determination of the total reducing sugar was carried out following the dinitrosalicylic acid

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method (Miller, 1959). To a 10 ml test tube, 3 ml of DNS reagent and 3 ml of sample were added. The mixture was heated at 90°C for 8-10 minutes using a hot water bath. After 10

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minutes, the tubes were removed from the water bath, and 1 ml of potassium sodium tartrate solution (40% w/v) was immediately added. The test tubes were then cooled to room

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temperature. The total reducing sugar was measured using a UV-visible spectrophotometer

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(model: 3000 plus; Make: Labindia, India) at 575 nm.

2.4.2 Method development for analysis of sugar using LC-MSD

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The reducing sugars from coconut coir were quantified using LC-MSD (manufacturer: Agilent, Germany, model: Infinity II 1260) consisting of a quaternary pump, auto-sampler, column furnace, and single quadrupole mass spectrometer. The MS is equipped with electrospray ionization (ESI) source and a nitrogen generator unit (make: Peak, Germany). A Poroshell HILIC Z (2.7 mm X 150 mm), 3.0 μm particle size column was used to analyze sugars such as mannose, glucose, xylose, galactose, arabinose, and cellulobiose. Two mobile phases - A - 0.3% (w/v) ammonium hydroxide and B - acetonitrile with 0.3% (w/v) ammonium hydroxide were considered for the method development. The proper gradient 7

ACCEPTED MANUSCRIPT flow and the ratio of mobile phase A and B were determined based on the complete separation of the sugars in the chromatograms. A sample volume of 10 µl was injected into the column with mobile phases pumped at a flow rate of 0.4 ml/min for quantification of the sugars using LC-MS system. The temperature and the column pressure were maintained at 35o C and 220 psi respectively, and the capillary voltage (Vcap) was kept at 4000V. The

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molecular weight of the sugars (glucose, xylose, mannose, and cellulobiose) were detected in

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MS as M + Cl- in the negative mode (Shindo et al., 2013). The molecular weight and fragmentor voltage (FraV) of the monosaccharides detector were; i) Glucose (MW – 215.7) -

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100V, ii) Xylose and Mannose (MW – 185.7) - 70V, and iii) Cellulobiose (MW – 377.7) -

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140V. The gradient flow for mobile phase (A and B) with respect to time into column is as follows: a) 0 – 6.99 min (A – 10%:B – 90%), b) 7 – 11 min (A – 40%:B – 60%), c) 11.10 –

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13 min (A – 10%:B – 90%). It was observed that in our analysis there were traces of Cl-ion, which is an adduct present in the solution. A calibration curve (of the mixed standard) was

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generated using Chemlab Station software which was used in quantifying the sugars in the

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samples. The LDL and HDL of this method were 1 mg/L and 100 mg/L.

2.5 Quantification of furan and solubilized lignin compounds

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For the determination of furan compounds HPLC grade furfural and 5-hydroxymethylfurfural compounds were obtained (make: himedia, India). The hydrolysate obtained after the acid hydrolysis process under optimum condition were analyzed for furan compounds using the LC-DAD method. The furan compound was separated by a Zorbax SB -C18 column (2.1 X 50 mm, 1.8 micron) in LC-DAD system using 80% MS grade water and 20% methanol as the mobile phase. For the detection of furan compounds, isocratic conditions were maintained with a 280 nm wavelength signal, as reported in the literature (Lima et al., 2009) . The

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ACCEPTED MANUSCRIPT amount of sample taken for analysis was 10 μl with the column temperature set at 30°C ± 0.8 and pump flow rate at 0.1 ml/min.

2.6 Enzymatic saccharification of pretreated solids The hydrolysis efficiency for cellulose conversion to glucose monomers was investigated by

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subjecting untreated and pretreated solids of case I and case II to enzymatic saccharification

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process. A commercial Trichoderma reesei cellulase preparation (C2730) and β-glucosidase from almonds (G0395) were purchased from Sigma-Aldrich, India. The filter paper activity

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(FPU) and cellulobiose activity (IU) cellulase and β-glucosidase were 97 FPU/ml (Chu et al.,

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2014) and 5.1 IU/mg (Kim and Lee, 2006). The saccharification process was carried out at different time intervals of 24 h, 48 h, 72 h, 96 h, and 120 h with solid loading of 10 % in

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0.05M citrate buffer (de Almeida Antunes Ferraz et al., 2018). To the mixture, 0.3% (w/v) sodium azide was added to avoid the growth of microbes within the medium (Sun and Cheng,

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2005). To the mixture, 0.1% (v/v) Tween 80, a surfactant was added to enhance the saccharification process (Gupta et al., 2011). The effect of pH (3.8, 4.8, and 5.8), temperature

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(40o C, 50o C, and 60o C) and enzyme loading (15FPU:7.5IU, 20FPU:10IU, and 25FPU:12.5IU per gram of dry coir) on glucose yield were also studied. The sample was collected at

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different intervals and boiled for 5 min to deactivate the enzymes. The liquid sample was centrifuged at 4000 rpm for 10 min to remove the residual solids (Wang and Cheng, 2011). The supernatant was analyzed for glucose using LC-MS. Glucose yield % is calculated using the following Eq.3

𝐺𝑙𝑢𝑐𝑜𝑠𝑒 𝑦𝑖𝑒𝑙𝑑, % =

𝐺𝑙𝑢𝑐𝑜𝑠𝑒, 𝑚𝑔/𝑚𝑙 𝑋 0.9 𝑋 100 𝐺𝑙𝑢𝑐𝑜𝑠𝑒 𝑖𝑛 𝑏𝑖𝑜𝑚𝑎𝑠𝑠, 𝑚𝑔/𝑚𝑙

(3)

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ACCEPTED MANUSCRIPT 2.7 Morphological characterization of coconut coir 2.7.1 SEM analysis SEM images were taken for untreated coir, case I and case II pretreated coir using Scanning electron microscope (Model: ZEISS EVO18; Make: ZEISS, USA). Before acquiring images, the coir samples were mounted on a conductive graphite tape. To evaluate the changes on the

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surface using SEM image, the samples were dried and coated with a gold-palladium alloy

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using a sputter machine (model: SC7620 Mini for spray application, Make: Quorum

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Technologies, UK) and the representative images of untreated coir, case I and case II pretreated coir reported here were acquired with a 10 kV accelerating voltage.

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2.7.2 FTIR analysis

FTIR analysis was performed to determine the change in functional groups of the untreated

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and acid-treated coir using Fourier transform infrared spectrometer (manufactured by Bruker, model: Alpha II). The coir sample was scanned at a resolution of 4 cm-1 in the range of 4000

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2.7.3 XRD analysis

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– 300 cm-1 .

X-Ray powder diffraction patterns for initial and the acid-treated coir were obtained using

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Xpert Pro (Model: PW3040/60. Make: PANlytical, Germany). Xpert pro is equipped with a sealed tube Cu-Kα source, diffracted beam PreFIX carrier, and line detector. The samples were cast with double sided tape on microscopic slides. Scans were obtained at 45kV and 40mA from a Bragg angle (2θ) of 5 to 80o with a step size of 0.0167o . CrI of initial and pretreated coir were calculated according to the protocol already described. CrI were determined using the following Eq. 4 (Segal et al., 1959)

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ACCEPTED MANUSCRIPT 𝐶𝑟𝐼 =

𝐼𝑐𝑟𝑦𝑧 − 𝐼𝑎𝑚 𝑋 100 𝐼𝑐𝑟𝑦𝑧

(4)

Where, Icryz = Intensity of peak at Bragg angle 22.4 o , Iam = Intensity of the background scatter at Bragg angle 18.0o .

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3. Results and discussion

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3.1 Initial characteristics of coconut coir sample

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Following the protocols of the NREL, the initial coconut coir composition in the dry basis

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was 40% glucan, .12% xylan, 41.5% insoluble lignin, 2.5% soluble lignin and 3% of ash. The glucan and xylan analyzed in sample correspond to 52% of the total carbohydrates in the

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coconut coir.

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3.2 Model development

3.2.1 Statistical analysis and the model fitting

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Optimization of dilute acid hydrolysis in both the cases was performed by 23 factorial CCD. Experimental responses, YI, and YII for the two cases of dilute sulfuric acid hydrolysis of

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coconut coir are shown in Table 2. Multiple regression analysis of experimental data was performed using Statistica (7.0). The relationship between the response and the independent variable was expressed using a quadratic model. The quadratic models for the case I and case II, representing the real (uncoded) values have been shown in Eq. 5 and Eq. 6, as follows

𝑌𝐼 = −237.42 + 25.32𝑋1 − 0.73𝑋12 + 2.16𝑋2 − 0.004𝑋22 − 131.82𝑋3 + 353.55𝑋32 − 0.029𝑋1 𝑋2 − 18.87𝑋1 𝑋3 − 0.234𝑋2 𝑋3

(5)

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ACCEPTED MANUSCRIPT 𝑌𝐼𝐼 = −585.48 + 6.038𝑋1 − 0.019𝑋12 + 6.94𝑋2 − 0.0198𝑋22 + 93.55𝑋3 − 21.95𝑋32 − 0.04𝑋1 𝑋2 + 0.419𝑋1 𝑋3 − 0.344𝑋2 𝑋3

(6)

where, YI is the response for the case I and YII is the response for case II; X1 , X2 , and X3 are time (min), temperature (o C), and acid concentration (w/w), respectively. The recovery of

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reducing the sugar for the case I varied from 11.6 – 52.78%, whereas for case II, the recovery

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of reducing sugar varied from 16.95 – 43%, within the tested range. From the second order,

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models predicted values were determined. The predicted values were compared with the experimental response values for both the cases as shown in Fig. S1. In can be seen that, the

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predicted values matched well with the observed values. Furthermore, the significance of the model were also studied using residual plots and normality distribution plots. The residual

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values required for the plotting were determined from the difference of observed and predicted values. The residual plots were plotted with residual values against predicted values

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for case I and case II and are shown in Fig. S2a and Fig. S2b, respectively. Residual plots

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examine the sufficiency and significance of the model. It is observed that the models for case I and case II were significant since the data points in the plot was randomly distributed showing no evidence of patterns. Additionally, normality plots for case I and case II were

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plotted with residual values versus the theoretical standard values and shown in Fig. S3a and Fig. S3b, respectively. As seen from the figures, all the points fell within the 95% confidence interval. This shows the model is significant for case I and case II. The adequacy of the second order models was assessed by ANOVA as shown in table 3 and table 4 for the case I and case II, respectively, which provide model coefficients (R2 ), F values, and overall significance of the models. The obtained models for both the cases were observed to be significant since p<0.05. “Lack-of-fit” measures the failure of the regression model to represent the experimental data. As shown in the ANOVA tables for the case I and case II, 12

ACCEPTED MANUSCRIPT lack-of-fit was not significant (p>0.05) which implies that the second order models can be applied to fit the experimental response values. The model terms with p-value<0.05 are considered significant. For the case I, the recovery of reducing sugar was greatly influenced by three linear terms (X1 , X2 , and X3 ), one interaction terms (X1 X3 ) and three quadratic terms (X1 2 , X2 2 , and X3 2 ). On the other hand, for case II, the recovery of reducing sugar was

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influenced by two linear terms (X2 and X3 ), three interaction terms (X1 X3 , X1 X2 , and X2 X3 )

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and three quadratic terms (X1 2 , X2 2 , and X3 2 ). Moreover, the coefficient of determination (R2 )

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for the case I and case II (0.96, 0.97) agreed well with the adjusted coefficient (adj. R2 ) of

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determinations (0.93, 0.95).

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3.2.2 Interactive effect and optimization of independent variables for the case I To determine the influence of the independent variables and their interactions on the recovery

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of reducing the sugar for the case I, the contour plots and response surface plot plots were plotted and shown in Fig. 2 (a-c). The combined effect of time and temperature on reducing

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sugar recovery is shown in Fig. 2a. The figure shows that an increase in reaction time resulted in a 30% recovery of reducing sugar. However, as the reaction time increases with an

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increase in temperature, the recovery of the reducing sugar decreased. This implies the importance of the combined effect of time and temperature on the recovery of reducing sugar. On the other hand, given the combined effect of sulphuric acid and time (Fig. 2b), the contour and the surface plot appeared as a saddle point. This pattern means that increasing or decreasing the time and sulphuric acid at the same time increased the recovery of reducing sugar. Also, increase in time and decrease of sulfuric acid concentration reduced the recovery of reducing sugar. A similar saddle point pattern was observed in Fig. 2c, which represents the combined effect of sulphuric acid and temperature. The recovery of reducing sugar is 13

ACCEPTED MANUSCRIPT predicted around 80% with a temperature in the range of 160 - 190o C. However, the recovery of reducing sugar varied from 60 to 80% by the temperature range (150 - 230°C). In another condition as seen in Fig. 2c, only 60% reducing sugar was predicted for sulphuric acid concentration ranging from 0.1 to 0.15% w / w with a temperature set between 170 - 210°C. By evaluating the independent variables of the second-order model equations, the optimal

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3.2.3 Interactive effect of independent variables for case II

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conditions were determined by maximum reducing sugar recovery.

For case II, the recovery of reducing sugar varied from 16-47% for the experimental run. As

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shown in Fig 3 (a – c), the predicted recovery of reducing sugars varied between 10-40% due to the combined effect of two independent variables with other variable operating in the

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center run of design. For the contour and surface plot of temperature and time (Fig. 3a), it was observed that the maximum reducing sugar recovery was predicted to be 40%. As the

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temperature increased, the recovery of reducing sugar increased with time at the lower range. On the other hand, a 30% recovery was predicted for temperature in the range of 100-120°C

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for a time between 50 -70 min. Considering the effect of sulphuric acid and time on the recovery of reducing sugar, the contour and surface plots shown in Fig. 3b represent inverted

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bowl shapes. The inverted bowl pattern indicates the maximum recovery of reducing sugar at the center with in the intermediate range of sulphuric acid and time. It is predicted that the recovery of reducing sugar increased as time and sulphuric acid increased. However, as the sulphuric acid concentration and time increased, the recovery of reducing sugar is decreased. Similar inverted bowl pattern was observed in Fig. 3c, emphasizing the interaction of sulphuric acid and temperature on the recovery of reducing sugars. For sulphuric acid and temperature, the recovery of reducing sugar varied up to 40% is predicted.

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ACCEPTED MANUSCRIPT 3.3 Validation of optimal conditions The optimum conditions for the independent variables were determined, and the conditions were repeated thrice to reduce the variance between the experimental data. The optimal conditions for cases I and II are shown in Table 5. After the hydrolysis step, the solid and liquid hydrolysate were separated by filtration. The liquid hydrolysate was analyzed for

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monosaccharides and furan compounds. For case I, it was observed that coir pretreated with a

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low acid concentration of 0.21% w/w for 8.2 min at 200 °C showed a reducing sugar

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recovery of 52%. However, a 48% reducing sugar recovery was observed for case II, when the coir was pretreated at 133°C for 13 min with an acid concentration of 1.27% w/w. The

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furan compounds and lignin derivatives were determined in the liquid hydrolysate of case I and case II acid hydrolysis and were shown in Fig. 4a. The monosaccharides and

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disaccharides profile were analyzed and shown in and Fig. 4b. It was observed that the reducing sugar yield were similar in case I and case II samples. However, the furan

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compounds generation was higher for case II compare to case I. Furan compounds cause inhibition to microbial growth and metabolism. Therefore, case I will be more favorable for

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subsequent fermentation process using Saccharomyces cerevisiae. Also, it was observed that lignin derivative compounds such as p – hydroxybenzoic acid, p – hydroxybenzaldehyde, and

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vanillic acid were more in case I hydrolysate compared to hydrolysate of case II. This analysis shows that more solubilization of lignin was observed in case I. Removal of lignin enhances the enzymatic saccharification process for higher yield of glucose. Studies on dilute acid hydrolysis of coconut coir with conditions similar to case II have been reported previously (Agustriyanto et al., 2012). It has been notified that acid hydrolysis at 100°C for 60 min using an acid concentration of 1.5% w/w showed a sugar recovery of 0.22 g/g of dry coir after enzymatic hydrolysis. However, the conditions applied were less severe compare to the conditions. Additionally, it has been reported by (Fatmawati and 15

ACCEPTED MANUSCRIPT Agustriyanto, 2015) that, 7.5% w/v of coir pretreated at 121o C with 1.5 % sulphuric acid concentration increased the exposure of cellulose content. This was due to the removal of an aromatic layer from the surface of the biomass to enhance the enzymatic saccharification process. However, acid concentration of 1.5% w/w can reduce the life of the reactor, thereby, increasing the operational and maintenance cost (Fang, 2013). For the conditions reported in

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this paper, the acid concentration is less severe during the acid hydrolysis process. It was

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reported previously that reducing sugar yield of 0.08g/g of dry coconut coir was observed

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when alkali-treated coir was hydrolyzed with 4% w/w acid concentration at 121 °C for 2 h (Miftahul Jannah and Asip, 2015). However, an acid concentration of 4% is too high and

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cannot be recommended since it will erode the lining of the reactor. In another set of studies, it was reported that hydrolysis of coconut coir with 1% v/v sulphuric acid at 40°C for 24 h

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improved the saccharification process (Teck Y. Ding et al., 2012). In the present study, about 0.25 g of reducing sugar was recovered per gram of dry coconut coir in both the cases (case I

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and case II). Previous studies were mostly conducted with high concentrations of acid. However, in the present study, experiments were performed in low (case II) as well as high

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(case I) acid concentration range to understand the effect of different hydrolysis conditions on the reducing sugar yield and the performance of the subsequent enzymatic saccharification

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process. It is understandable that case I (low acid concentration) will be preferred during scale-up due to less acid requirement.

3.4 Enzymatic saccharification of pretreated coir To evaluate the efficacy of the sulphuric acid treatment on breaking lignin and hemicellulose structures, the acid-treated solids (both case I and case II) were enzymatically saccharified, and samples were collected and analyzed at regular intervals.

16

ACCEPTED MANUSCRIPT 3.4.1 Effect of pH The effect of pH (3.8, 4.8, and 5.8) on saccharification of untreated and treated coir (case I and case II) were studied and shown in Fig. 5a. Maximum glucose yield for case I and case II was observed at 72 h and 96 h, respectively when pH was maintained at 4.8±0.2. A glucose yield of 90% was observed for case I during saccharification. On the other hand, for case II,

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the maximum yield was 58 %. Lower yield of glucose was observed for the untreated coir

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(control), with the glucose yield varying between 8 and 10%. It has been reported previously

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that at pH 4.8±0.2 the activity of Trichoderma reesei cellulase (C2730) and β-glucosidase from almond (G0395) increased during the enzymatic saccharification process (Aliee and

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Teimori, 2009). In another set of study, enzymatic saccharification of palm date fiber was

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performed at pH 4.8±0.2 resulting in a cellulose conversion of 84.3% (Shafiei et al., 2010).

3.4.2 Effect of Temperature

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Saccharification process for untreated and treated coir were done at 40°C, 50°C, and 60°C, respectively, while pH was fixed at 4.8±0.2. The glucose yield from the untreated and treated

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coir (case I and case II) is shown in Fig. 5b. Throughout this study the enzyme loading was fixed at 20FPU:10IU/g of dry coir. For the untreated cand treated coir (case I and case II),

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maximum glucose yield of 11.2%, 90.32%, and 50% was observed at 120 h, 72 h, and 96 h, respectively. For all the cases, highest yield of glucose was observed at 50°C. The temperature of 50°C has been reported as the optimum condition for Trichoderma reesei cellulase (C2730) and β-glucosidase from almond (G0395) to enhance the enzymatic saccharification (Alrumman, 2016; Kassanov et al., 2017; Lu et al., 2012).

3.4.3 Effect of Enzyme Loading 17

ACCEPTED MANUSCRIPT Three enzyme loading - 15FPU:7.5IU/g, 20FPU:10IU/g, and 25FPU:12.5IU/ g of dry coir, were selected to study the effect of enzyme loading on glucose yield. The temperature and pH for this study were maintained at 50o C and 4.8±0.2, respectively. The glucose yield at different time intervals under different enzyme loading are shown in Fig. 5c. It is observed that, the rate of cellulose conversion to glucose increased with the increase in enzyme

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loading. Glucose yield of 91% and 55% was observed at 72 h and 96 h for case I and case II,

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respectively. The graphs indicate that the enzyme dose of 15FPU:7.5CPU per gram of dry

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coir is adequate to attain the maximum possible yield within a reasonable time period. Similar observations were reported by other researchers (Christia et al., 2016; Jafari et al.,

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2011; Jeihanipour et al., 2010; Shafiei et al., 2010; Talebnia and Taherzadeh, 2012; Zheng et al., 2007). which is summarized in Table. 6 in comparison with the current study cellulose

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conversion efficiency. Case I showed a higher yield than case II during enzymatic saccharification process, with an enzyme loading of 15FPU:7.5IU/ g of dry coir at pH and

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temperature of 4.8±0.2, 50o C, respectively.

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3.5 Morphological characteristics of untreated and treated coir Fig. S4a – Fig. S4f, are the SEM images of the untreated coir, and the acid-hydrolyzed solids.

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The untreated coir (Fig. S4a and Fig. S4b) seems to be inaccessible due to its rigid and highly ordered cell wall structure. However, as evident from Fig. S4c – S4d and Fig. S4e – S4f, the outer lignin layer has been altered due to acid hydrolysis in both case I and II. Acid hydrolysis is likely to weaken the outer lignin structure, thereby, increasing its accessibility towards enzyme action for subsequent saccharification. This will cause an enhancement in sugar yield and further improvement of the overall process From the XRD analysis as shown in Fig. 6a, it was observed that there was an increase in the height of the crystalline peak for coir sample from case I due to the higher exposure of 18

ACCEPTED MANUSCRIPT cellulose compared to the solid peak of case II. As shown in the Fig.6b, the CrI for the case I increased from 42 to 54 %, but for case II the increase in CrI was less (from 42 to 45 %). It is observed that the crystallinity peak (2θ = 22.4o ) and amorphous peak (2θ = 18o ) varied for untreated coir, the case I solids, and case II solids. The crystalline peak for the case I was sharper compared to the peaks of the untreated samples and case II solids. It is reported in the

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literature that untreated lignocellulosic biomass is rich in lignin and hemicellulose that reduce

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the exposure of crystalline structure. The increase in the case I peak was caused by the

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removal of these amorphous components leading to the disclosure of cellulose. The changes in the structure and crystallinity of cellulose for the case I and case II caused an increase in

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the surface area which enhanced the enzymatic saccharification process. The higher value of CrI for the case I explain the reason for obtaining higher glucose yield during enzymatic

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hydrolysis compared to case II and untreated coir.

Fourier transform infrared spectroscopic (FTIR) analysis is a fast and non-destructive

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technique for the qualitative and quantitative determination of biomass components and functional groups. The FTIR spectrum of the sample was analyzed in the IR range from 400

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to 4000 cm -1 . The FTIR spectrum of untreated coir (Fig. S5a), the case I (Fig. S5b), and case II (Fig. S5c) solids show different transmittance peaks corresponding to varying vibrations of

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functional groups. The main components of lignocellulosic biomass are hemicellulose, cellulose, and lignin. These components are mainly composed of esters, aromatic ketones, and alcohols with different oxygen functional groups. The characteristic peaks for the cellulose structure in coconut coir were 3329 cm-1 , 2928 cm-1 , 1606 cm-1 , 1245 cm-1 and 1024 cm-1 (Dany G. Kramer et al., 2014). The peak 3329 cm-1 corresponds to the stretching vibration of carboxyl acid O-H groups due to the presence of cellulose–I in biomass (Jayaramudu et al., 2010). For untreated, case I and case II samples, a broad peak located at 3328 cm-1 , 3267 cm-1 and 3248 cm-1 was observed. These figures show that the transmission 19

ACCEPTED MANUSCRIPT intensities reduced after acid treatment compared to untreated coconut. This explains the fact that, after removal of components such as hemicellulose and lignin during the acid treatment step, cellulose I was exposed, therefore, the OH bond absorbed more IR compared to the untreated coconut coir. The major peak at 1024 cm-1 represents the C-O, C=C, and C-C-O stretch vibrations due to the presence of hemicellulose, cellulose and lignin in plant material

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(Teck Y Ding et al., 2012). For untreated coir, case I and case II samples, the transmittance

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band at 1031 cm-1 , 1026 cm-1 and 1019 cm-1 were observed. The figures show that there was

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a change in peak intensity after acid-treatment. The peak intensity for the case I and case II were substantially higher compared to the peak intensity of the untreated coconut coir. This

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shows that there has been a change in the concentration of compounds with C-O, C = C and C-C-O functional groups in coconut coir after the acid treatment step. The peak at 2928 cm-1

2016).

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refers to the stretching vibration observed in alkyl group with C-H stretch bonds (Khan et al., These functional groups are

mostly observed

in the lignin component of

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lignocellulosic biomass. The observed peaks at 2932 cm-1 , 2922 cm-1 for untreated coir and case I is seen due to the C-H stretching vibration occurring in Lignin component of coir.

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However, for case II the peak was not found in the FTIR spectrum. Similarly, the peaks at 2893 cm

-1

and 2841 cm

-1

also represent the C - H stretching

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oscillations occurring in lignin (Sills and Gossett, 2012). The lower intensities of C-H peaks in case I and case II spectra are occurred due to the solubilization of lignin compounds into the liquid hydrolysate after the hydrolysis. The broad peaks such as 1606 cm-1 and 1245 cm-1 represented the stretching vibration of C=O and COO- groups (Ezekiel et al., 2011; Sousa Neto et al., 2011). The peak at 1606 cm-1 and 1690 cm-1 in the spectrum corresponds to the untreated coir, case I and case II sample. This is due to the presence of C=O stretching vibration present in lignin (Shi and Li, 2012). The peaks associated with cellulose structure were 1319 cm-1 , and 1373 cm-1 (Adapa et al., 2011). These peaks were observed in the FTIR 20

ACCEPTED MANUSCRIPT spectra of untreated and treated coir sample. The peak at 1323 cm-1 of untreated coir and 1319 cm-1 of case II are characteristics of C-H plane bending vibration (Shi and Li, 2012). However, these bending vibrations were not observed in the spectra of the case I.

The

transmittance band at 1650 cm-1 corresponded to the asymmetric stretching vibration of C=C observed in aliphatic functional groups present in lignin (Arrakhiz et al., 2013). The other

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peak 1740 cm-1 is observed in samples due to C=O vibration of carbonyl functional groups in

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lignin (El Marouani et al., 2017). The peak at 1728 cm-1 for untreated coir and 1741 cm-1 for

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the case I was due to the presence of ketone/aldehyde C=O stretching vibration that is seen in hemicellulose (Kubo and Kadla, 2005). However, this peak was not observed in case II

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spectra.

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4. Conclusion

Coconut coir hydrolysis was studied under, Case I (Time: 2-10 min; Temperature: 160-

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240o C; acid: 0.2-0.7% w/w) and Case II (Time: 10-60 min; Temperature: 100-160o C; acid: 0.7-2% w/w) conditions. A maximum of 52% of sugar yield from Case I solids was obtained for condition (8.2 min, 0.21% w/w acid, and 200 o C). Higher sugar yield of 90% was observed from Case I solids during enzymatic hydrolysis (pH at 4.8±0.2, 50o C, and enzyme loading of 15FPU:7.5CBU/g). Overall, 0.49 g of total carbohydrates was recovered from 1 gram of dry coir. Further scope in recycling the enzymes is needed to make the process cost-effective.

5. Acknowledgment 21

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This research was funded by the Ministry of Human Resource and Development (MHRD), India, under the FAST program and by the Department of Science and Technology (DST), Government of India, under FIST program. Also, the authors would like to thank Dr. Tarun K Panda, Associate professor, Department of Chemistry, IIT Hyderabad, for his valuable

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input.

22

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ACCEPTED MANUSCRIPT Table. 1 The coded and uncoded values for case I and case II hydrolysis conditions

Coded

Coded values

variable

terms

-α

-1

0

+1

+α

Time, min

X1

2

3.6

6

8.4

10

Temperature, o C

X2

160

176.2

200

223.8

240

Sulphuric acid, % w/w

X3

0.2

0.3

0.6

0.7

0

+1

+α

20.1

35

49.9

60

112.2

130

147.8

160

0.96

1.35

1.74

2

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Independent

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CASE I

0.45

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CASE II

Coded values

variable

terms

-α

Time, min

X1

10

Temperature, o C

X2

100

Sulphuric acid, % w/w

X3

0.7

-1

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Coded

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Independent

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ACCEPTED MANUSCRIPT Table. 2 Sulphuric acid hydrolysis of coconut coir under case I and case II condition with recovery of reducing sugar

Case I S. No X2

X3

YI, %

YI -

O bserved

predicted

X1

X2

X3

YII, %

YII -

O bserved

predicted

17.5

15.6

3.6

176.2 0.30

11.6

13.0

20.1 112.2 0.96

2

3.6

176.2 0.60

38.4

36.0

20.1 112.2 1.74

19.0

18.7

3

3.6

223.8 0.30

13.3

13.4

20.1 147.8 0.96

47.1

45.2

4

3.6

223.8 0.60

34.8

33.0

20.1 147.8 1.74

39.2

38.8

5

8.4

176.2 0.30

39.6

40.1

49.9 112.2 0.96

30.6

30.4

6

8.4

176.2 0.60

37.6

36.2

49.9 112.2 1.74

41.8

43.1

7

8.4

223.8 0.30

32.8

33.8

49.9 147.8 0.96

17.0

16.5

8

8.4

223.8 0.60

29.4

26.6

49.9 147.8 1.74

18.6

19.8

9

2.0

200.0 0.45

6.6

7.6

10.0 130.0 1.35

29.5

31.8

10

10.0 200.0 0.45

24.0

25.0

60.0 130.0 1.35

29.7

28.3

11

6.0

160.0 0.45

23.5

24.0

35.0 100.0 1.35

22.1

22.4

12

6.0

240.0 0.45

14.7

16.2

35.0 160.0 1.35

26.9

27.6

13

6.0

200.0 0.20

46.3

43.9

35.0 130.0 0.70

27.8

30.2

14

6.0

200.0 0.70

52.8

57.1

35.0 130.0 2.00

37.0

35.5

15

6.0

200.0 0.45

28.9

28.0

35.0 130.0 1.35

43.7

42.1

SC

NU

MA

ED

EP T

AC C

16

PT

1

RI

X1

Case II

6.0

200.0 0.45

29.0

28.0

35.0 130.0 1.35

39.8

42.1

6.0

200.0 0.45

30.6

28.0

35.0 130.0 1.35

43.1

42.1

6.0

200.0 0.45

22.9

28.0

35.0 130.0 1.35

40.1

42.1

19

6.0

200.0 0.45

26.8

28.0

35.0 130.0 1.35

43.0

42.1

20

6.0

200.0 0.45

30.1

28.0

35.0 130.0 1.35

43.3

42.1

17 18

30

ACCEPTED MANUSCRIPT Table 3. ANOVA from the selected CCD for recovery of reducing sugar from coconut

SS

df

MS

F

P

Significance

X1

364.754

1

364.7540

44.7822

0.001127

Significant

X1 2

247.030

1

247.0296

30.3287

0.002700

Significant

X2

73.164

1

73.1637

8.9826

0.030200

X2 2

113.199

1

113.1994

13.8979

0.013599

Significant

X3

211.857

1

211.8571

26.0105

X3 2

911.974

1

911.9744

X12

21.920

1

21.9204

SC

X23

361.710

1

361.7096

X13

5.618

1

Lack of fit

50.735

5

Pure error

40.725

Total SS

2539.975

5

Significant

0.003770

Significant

111.9664

0.000130

Significant

2.6912

0.161825

-

44.4084

0.001149

Significant

5.6176

0.6897

0.444103

-

10.1470

1.2458

0.407657

-

MA

NU

RI

PT

Factors

ED

coir hydrolysis under case I conditions

8.1451

AC C

EP T

19

31

Factors

SS

df

MS

F

P

Significance

ACCEPTED MANUSCRIPT 15.082

1

15.0822

4.9439

0.076790

-

X1 2

263.248

1

263.2479

86.2926

0.000243

Significant

X2

32.718

1

32.7183

10.7251

0.022078

Significant

X2 2

531.666

1

531.6662

174.2801

0.000045

Significant

X3

34.768

1

34.7683

11.3970

0.019761

Significant

X3 2

154.954

1

154.9542

50.7939

0.000844

Significant

X12

942.344

1

942.3443

308.9003

0.000011

Significant

X23

46.388

1

46.3877

15.2059

0.011415

Significant

X13

45.211

1

45.2105

14.8200

0.012005

Significant

Lack of fit

26.902

5

5.3804

1.7637

Pure error

15.253

5

3.0506

Total SS

1967.115

19

SC

RI

PT

X1

-

NU

0.274294

MA

Table 4. ANOVA from the selected CCD for recovery of reducing sugar from coconut

AC C

EP T

ED

coir hydrolysis under case II conditions

32

ACCEPTED MANUSCRIPT Table 5. The Optimal conditions for case I and case II determined from developed second order model Case I

Case II

Time, min

8.2

13

Temperature, o C

200

155

Sulphuric acid, % w/w

0.21

1.27

PT

S. No

Reducing sugar, %

54

Case II

48

Observed, % 52±1.2 47±0.8

AC C

EP T

ED

MA

NU

Case I

RI

Predicted, %

SC

Hydrolysis condition

33

ACCEPTED MANUSCRIPT

Table 6. Comparison of cellulose hydrolysis from the literatures to the current study Lignocellulosic

Enzyme

Enzyme

Cellulose

No biomass

source

loading

conversion

1

Lignocellulosic

Cellulase

biomass

(C2730) and

construction waste

β-glucosidase

20FPU:50IU

95 %

Cellulase

residuals

(C2730) and β-glucosidase

20FPU:10IU

Blended-fibers

Cellulase

waste textiles

(C2730) and

(G0395) Palm date fiber

Cellulase

ED

6

20FPU:30IU

90 %

MA

β-glucosidase

NU

(G0395) 3

93 %

SC

Paper tube

RI

(G0395) 2

(C2730) and

β-glucosidase

Reference

(Jafari et al., 2011)

PT

S.

20FPU:50IU

84.3 %

15FPU:30IU

75 %

15FPU:52CBU

87 %

20FPU:10IU

90 %

(Talebnia and Taherzadeh, 2012)

(Jeihanipour et al., 2010)

(Shafiei et al., 2010)

7

Oil palm empty fruit brunch Different

AC C

8

EP T

(G0395)

Lignocellulosic

biomass (Creeping wild rye, and Jose tall wheatgrass) 9

Coconut coir

Cellulase (C2730)

(Christia et al., 2016)

Cellulase (C2730) and β-glucosidase

(Zheng et al., 2007)

(C6105) Cellulase (C2730) and β-glucosidase

Present study

(G0395)

34

ACCEPTED MANUSCRIPT

AC C

EP T

ED

MA

NU

SC

RI

PT

Figure 1 Schematic diagram of Non – Stirred High Pressure Batch Reactor Figure 2. Contour and three dimensional surface plots shown for effect of interaction on recovery of reducing sugar. Case I (A – Temperature versus Time; B – Sulphuric acid versus Time; C – Sulphuric acid versus temperature). Figure 3. Contour and three dimensional surface plots shown for effect of interaction on recovery of reducing sugar. Case II (A – Temperature versus Time; B – Sulphuric acid versus Time; C – Sulphuric acid versus temperature) Figure 4. Chemical composition profile in Liquid hydrolysate under Case I and Case II (optimal) conditions. A – Furan compounds and Lignin derivative; B – Monosaccharides and Disaccharides. Figure 5. Glucose yield (%) after enzymatic saccharification of coconut coir pretreated under case I and case II conditions: A – Effect of pH; B – Effect of Temperature; C – Effect of enzyme loading Figure 6. Change in crystallinity index (CrI) for coir treated under case I and case II condi tion compared to untreated coir. A – XRD pattern B – Comparative study on CrI

35

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

AC C

EP T

ED

Graphical abstract

36

ACCEPTED MANUSCRIPT

HIGHLIGHTS Dilute sulphuric acid pretreatment of coconut coir to recover reducing sugar.



Process optimization using response surface methodology.



Saccharification of acid hydrolyzed coir using cellulase and β – glucosidase.



Morphological changes in coconut coir studied using SEM and XRD.



8.2min and 0.2%w/w sulphuric acid hydrolysis under 200 o C favoured high sugar

RI

PT



AC C

EP T

ED

MA

NU

SC

yield.

37

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6