J L Legras

V Chuat

M N Madec

C Grondin

C Marais

S Casaregola

C Neuvéglise

O Rué

F Valence

Microflora variability among grape musts in Languedoc: the case of two grape varieties during two years

Introduction and objectives

The wine fermentation ecosystem is very likely one of the most explored microbial ecosystem since the pioneer work of Pasteur. Microbiology has considerably evolved since then, and recent metabarcoding based studies have renewed its vision. However, very few studies combine metabarcoding analyses to culture-based making as well as these microbial genetic resources available. In addition, many recent works suggested that the non-Saccharomyces component of grape must microflora impacts wine quality. In this context, we undertook the characterization of two grape must varieties by combining metabarcoding analyses and cultural approach on two consecutive years.

Experimental Design

• 20 Samples of grape musts of Viognier and Sauvignon grape varieties collected in 2015 and 2016 on 8 sampling sites. • Bacterial and fungal component of the microbiota characterized using classical culture approaches (18) and metabarcoding ( 19) using the V4 region of the 16S rRNA gene for bacteria and the ITS1 region for fungi. • DNA was extracted using a Qiagen Plant Dneasy kit and amplified targets were sequenced using 2x250 MiSeq v2 sequencing (Illumina). After filtering for quality, reads were analysed using FROGS pipeline (frogs.toulouse.inra.fr).

Evaluation of different factors on Grape must Microbial diversity

The evaluation of these factors has been done from the metabarcoding data (Cultural methods did not reveal clear differences)

Diversity encountered in the grape must microbiote

Yeast 32 fungal species were detected at a frequency higher than 1%, and contain the main yeast species encountered in grape must Bacteria 107 bacterial OTU were detected at a frequency higher than 2%, and partially assigned to 32 bacterial families.

Conclusion

This sampling enabled us to show the effect of the "Millesime" on the grape microbiota, very likely through its climate, and of the locality. We could not detect regional differences, very likely because of insufficient power. Bacterial and yeast strains collected were integrated to both INRAE BRC: CIRM-BIA (https://collection-cirmbia.fr/) and CIRM-Levures (https://cirm-levures.bio-aware.com/) where they are distributed for research and development purpose.

PCA of fungal species found for two harvest years Firmicutes (48%), Proteobacteria (39%) and Actinobacteria 14%. Among lactic acid bacteria, Lactiplantibacillus plantarum is the most represented species and was found in 86% of samples.

=> PCA reveals clear differences in fungal species (i.e. B cinerea) with harvest year suggesting differences in sanitary conditions.

Harvest year and locality are the two main factors that drive grape microbiota diversity

Projet ANR-15-CE20-0010


Mary Mensah

Nana Yaw Asiedu
email: nasiedusoe@yahoo.co.uk

Prince Fabrice Abunde Neba

Prince Nana Amaniampong
email: prince.nana.amaniampong@univ-poiers.fr

Patrick Boakye

Ahmad Addo

Nana Yaw Asiedu

Fabrice Abunde Neba

Modeling, optimization and kinetic analysis of the hydrolysis process of waste cocoa pod husk to reducing sugars

Keywords: Dissolution, Optimization, Kinetic Modeling, Multiple Linear Regression, Simulations, Biomass, Reducing Sugar

Introduction

Lignocellulosic biomass represents an important chemical resource and the most promising alternative to fossil fuels [START_REF] Pan | Biorefining of softwoods using ethanol organosolv pulping: Preliminary evaluation of process streams for manufacture of fuel-grade ethanol and co-products[END_REF][START_REF] Fitzpatrick | A biorefinery processing perspective: treatment of lignocellulosic materials for the production of value-added products[END_REF][START_REF] Zakzeski | The catalytic valorization of lignin for the production of renewable chemicals[END_REF]. However, the successful utilization of lignocellulosic biomass is hampered by its strong intermolecular bonding and high crystallinity of its cellulose structure, majorly accounting for the stability and resistive reactivity of lignocellulosic materials. In order to exploit the useful components of lignocellulosic materials, the complex nature of structural features of its plant cell wall must be depolymerized by the action of suitable or appropriate solvents and catalysts. The recalcitrant nature and complexity of the chemicals bonds in lignocellulosic biomass renders its dissolution in water and traditional organic solvents impossible, resulting in its depolymerization mainly by the use of corrosive and toxic chemicals at harsh conditions, making further upgrade ineffective, expensive and inconvenient [START_REF] Singh | Visualization of biomass solubilization and cellulose regeneration during ionic liquid pretreatment of switchgrass[END_REF][START_REF] Li | Rapid dissolution of lignocellulosic biomass in ionic liquids using temperatures above the glass transition of lignin[END_REF]. In this context, efficient conversion of lignocelluloses is emerging in recent years as one of the most crucial technologies for biomass derived compounds production. Although it has been established in the literature that ionic liquids (ILs) exhibits excellent intrinsic characteristics for the depolymerization and dissolution of lignocelluloses [START_REF] Li | Rapid dissolution of lignocellulosic biomass in ionic liquids using temperatures above the glass transition of lignin[END_REF][START_REF] Vriesmann | Cacao pod husks (Theobroma cacao L.): composition and hot-water-soluble pectins[END_REF][START_REF] Wu | Facile pretreatment of lignocellulosic biomass at high loadings in room temperature ionic liquids[END_REF] its high cost, recyclability difficulties and stability problems of ILs renders its commercial utilization problematic [START_REF] Yuan | Intensification effect of peroxide hydrogen on the complete dissolution of lignocellulose under mild conditions[END_REF][START_REF] Zhang | Catalytic oxidation of carbohydrates into organic acids and furan chemicals[END_REF]. Hydrogen peroxide is known to solubilize lignocellulose and it has no effect on the monomer composition of polysaccharides present [START_REF] Doner | An improved process for isolation of corn fiber gum[END_REF].

Its reaction with the lignin yields water soluble oxidation products with lower molecular weight [START_REF] Yuan | Intensification effect of peroxide hydrogen on the complete dissolution of lignocellulose under mild conditions[END_REF]. It is also known to reduce the crystallinity of cellulose causing reduction in the interaction between carbohydrates and lignin contents of biomass. Moreover, liquid-phase oxidation processes using H 2 O 2 is regarded as a green process [START_REF] Noyori | Green oxidation with aqueous hydrogen peroxide[END_REF][START_REF] Zhu | Ionic Liquids:"Green" Solvent for Catalytic Oxidations with Hydrogen Peroxide[END_REF] producing water and oxygen upon decomposition [START_REF] Wernimont | Capabilities of hydrogen peroxide catalyst beds[END_REF][START_REF] Sun | Delignification of maize stems by peroxymonosulfuric acid, peroxyformic acid, peracetic acid, and hydrogen peroxide. 1. Physicochemical and structural characterization of the solubilized lignins[END_REF]. Peroxide pretreatment of lignocellulosic materials has proved not only to be green, but also an affordable approach leading to the production of biorenewable feedstock. In the quest for the heightened interest for waste management and the successful utilization of solid waste materials into useful products, cocoa pod husk (herein referred to as CPH) an agricultural waste materials generated in several metric per year (~ 800, 000 MT/per) served as a potential source of lignocellulosic materials for chemicals and bio-fuel production. However, its usage is underutilized. We previously reported the successful thermochemical conversion of CPH into valuable platform chemicals via fast pyrolysis [START_REF] Adjin-Tetteh | Thermochemical conversion and characterization of cocoa pod husks a potential agricultural waste from Ghana[END_REF]. The results obtained constituted significant advancement in the direct conversion of agricultural waste materials generated in Ghana. Inspired by our previous results, herein, we report our investigations of the kinetics and optimization of hydrogen peroxide mediated hydrolysis of CPH, paying particular attention to the solvent concentration, residence time, temperature and reactor structure of CPH dissolution prior to its hydrolysis into reducing sugars. First, multiple linear regression is performed to model the effects of experimental conditions on the conversion process and determine the conditions that maximize percentage dissolution and overall yields of glucose and xylose. Second, the Saeman scheme for biomass hydrolysis was used to develop kinetic models to describe the dynamics of glucose and xylose production.

Theoretical developments

Multiple Linear Regression Modeling: Model Variables and Least Square Estimation

The explanatory variables considered for the multiple linear regression are, concentration of hydrogen peroxide, temperature and reaction time, while the response variables are the extent of dissolution, glucose yield and xylose yield. The general form of the multiple linear model is illustrated by Eq. ( 1).

For convenience, Eq. ( 1) can be written in matrix form as follows The least square estimate for a given set of parameter values in is obtained by formulating the residual vector, Eq. ( 3), then the squared length of the residual vector, Eq. ( 4).

The parameter estimates are obtained when the derivative of the squared length of residual vector with respect to the model parameters is zero, Eq. ( 5), which results in Eq. [START_REF] Vriesmann | Cacao pod husks (Theobroma cacao L.): composition and hot-water-soluble pectins[END_REF] In practice, in order to improve upon the accuracy of the model fitting, the observed response values, will be transformed. The following variance stabilizing transformations were utilized for this study: logarithmic, reciprocal, reciprocal square root and reciprocal.

This was done by changing the value of as shown in Eqs. ( 7) and [START_REF] Yuan | Intensification effect of peroxide hydrogen on the complete dissolution of lignocellulose under mild conditions[END_REF].

Variability of Model Fitting

The kernel density estimation and the parameter confidence regions techniques were used in assessing the variability of the model identification. An assumption of the least square estimation was considered in checking if the errors are random and normally distributed. This study considered the use of joint confidence regions in checking the reliability of estimated parameters using least square regression techniques. The joint confidence region and the marginal confidence intervals of the parameter estimates were determined using Eqs. ( 9) and [START_REF] Doner | An improved process for isolation of corn fiber gum[END_REF] respectively Where is the approximate standard error of the parameter estimates given by Eq. ( 11)?

The kernel density estimate of the residual vector was obtained using the Kernel smoothing function estimate, of MATLAB (MathWorks Natick, NA) for univariate and bivariate data.

Process Optimization Approach

In determining the levels of the explanatory variables that maximizes the different responses, estimated model parameters were used to formulate a constraint optimization problem as follows.

The optimization problem was solved using the MATLAB routine fmincon, for constraint functional minimization

Modeling Kinetics of the Hydrolysis

The hydrolysis of the biomass residue with acid was described using two consecutive pseudohomogeneous first-order reactions according to the scheme first proposed by Saeman [START_REF] Saeman | Aerobic fermentor with good foam-control properties[END_REF] who studied dilute acid hydrolysis of a variety of wood species. The Saeman [START_REF] Saeman | Aerobic fermentor with good foam-control properties[END_REF] scheme for hydrolysis of cellulose and hemicellulose is shown in Eqs. ( 13) and ( 14) respectively:

Using the scheme, material balance is performed to obtain system of ordinary differential equations that describe the dynamics of the hydrolysis process

The material balance for cellulose hydrolysis, Eqs. ( 15) and ( 16)

The material balance for hemicellulose hydrolysis, Eqs. ( 17) and ( 18 The data used for estimating the kinetic constants was obtained using the developed regression models. In order to properly represent true experiments, a random normally distributed measurement error was added to the simulation as depicted in Eq. ( 21).

In this study, a 1% error with 95% confidence interval was used, represented by setting

Experimental study

Raw material preparation

Dried cocoa pod husks were crushed and milled using a Waring commercial blender. The finely milled CPH powder was then sieved to obtain ≤ 1mm sieve size powder and then kept in zip locked bags for storage and further use.

Characterization of the Cocoa Pod Husk (CPH)

The characterization of the Cocoa Pod Husk includes proximate analysis of CPH, the Neutral-Detergent Fiber (NDF), Acid -detergent fiber (ADF) and Acid Detergent Lignin (ADL). The NDF, ADF and ADL were analyzed using the Van Soest techniques. The amount of hemicellulose present CPH sample was calculated by the difference between %ADF from the %NDF. Cellulose content was determined by the Crampton and Maynard techniques.

Fourier Transform Infrared Spectroscopy (FT-IR) of CPH powder was obtained using Perkin Elmer Instrument, SN 94133 (Spectrum Version 10.03.09) with 24 scans in the wavelength range 400 to 4000 cm -1 to detect the main functional groups present in the lignocellulosic material.

Design of Experiment for dissolution of CPH

500 mg of CPH (sundried) was weighed into the reactor (100 ml screw cup conical flask with a magnetic stirrer) along with 20 mL of varying molar concentrations of H 2 O 2 as solvent and subjected to various conditions of temperature (see Table 1). The samples were taken of the heater and submerged into a water bath at 25 o C immediately. The resulting solution was then centrifuged at 3000 RPM for 15 min and supernatant kept for further analysis. The undissolved residue was washed with 60 ml of deionized water, air dried to a constant weight for the analysis of reducing sugars via the double hydrolysis process.

The extent of dissolution was calculated using Eq. ( 24)

(24)
Where, and are the initial and final masses of sample respectively Central composite design (CCD) was used for the design of experiment. The CCD design was made up of a total of 20 runs consisting of 8 cube points, 6 center points in cube, 6 axial points, in a single base block. The model was generated as a function of factors in Table 1 on the predicted response of the extent of dissolution and yield of sugars from the residue.

Figure 1 presents the overall process utilized in this study for the conversion of CPH to reducing sugars. An amount of 300± 10 mg of raw CPH and residues was weighed into a glass tube was dissolved in a 3 mL of 72% H 2 SO 4 . The sample was kept at 30 o C for 1 h with intermittent shaking at constant intervals until complete hydrolysis was observed to have taken place. The solution was then diluted to 4% H 2 SO 4 by adding 84mL of distilled water. Samples were then autoclaved for 1 h at 121 °C and then cooled to room temperature. Hydrolysates were filtered through Gooch crucibles using vacuum suction and filtered again with a 0.2 µm nylon filter and kept for the analysis of reducing sugars. The reducing sugars was analyzed with a Shimazu LC 10/20 HPLC equipped with an Aminex 87H column, RI detector. Mobile phase used was H 2 SO 4 solution (0.005 M) with a flow rate of 0.6 mL/min.

Results and Discussion

Characteristics of cocoa pod

Fresh matured ripe Cacao pods fruits had an average weight of 360 ± 97g (50 samples), which is not in agreement with reports in the literature (∼500 g) [START_REF] Yapo | Extraction and characterization of gelling and emulsifying pectin fractions from cacao pod husk[END_REF][START_REF] Lachenaud | Twenty years of agronomic evaluation of wild cocoa trees (Theobroma cacao L.) from French Guiana[END_REF]. The seeds and pulp accounted for 41.39 wt% of the fruit with the husk being 58.61 wt% of the fresh fruits, which is less compared to reported values of 70-76% [16.18].These variations were however expected as the fruit characteristics vary with changes in climate and environment, as well as variety and pre-treatment methods employed [START_REF] Decker | Biomass conversion[END_REF][START_REF] Xu | Qualitative and quantitative analysis of lignocellulosic biomass using infrared techniques: a mini-review[END_REF].

Proximate analysis of CPH

The initial moisture content of the fresh cacao pod husks was 86.1% which is fairly good compared with 90% reported by Yapo [START_REF] Yapo | Extraction and characterization of gelling and emulsifying pectin fractions from cacao pod husk[END_REF] and 84.6% reported by Lachenaud [START_REF] Lachenaud | Twenty years of agronomic evaluation of wild cocoa trees (Theobroma cacao L.) from French Guiana[END_REF]. Table 2 includes the proximate analysis of the powder obtained from cacao pod husk after two weeks of sun drying. The CPH powder had a moisture content of 10.1% which was fair enough for storage and further analysis. The amount of volatile matter and ash content of the CPH powder accounted for 64.19% and 15.31% respectively similar that obtained for Cocoa pod husk from Ghana [START_REF] Adjin-Tetteh | Thermochemical conversion and characterization of cocoa pod husks a potential agricultural waste from Ghana[END_REF] 4) after the double hydrolysis. These sugars yield corelates with the total amount of cellulose and hemicellulose content of the CPH. 2). These functional groups detected further supports what was reported by [START_REF] Adjin-Tetteh | Thermochemical conversion and characterization of cocoa pod husks a potential agricultural waste from Ghana[END_REF]. Table 5 gives a summary of notable functional groups assigned to peaks detected in the FTIR spectrum.

Regression characteristics for higher temperature dissolution

Three responses were recorded in the CPH dissolution process in this study: the extent of dissolution (%D), yield of glucose (%G) and xylose (%X) from the hydrolysis the residue from the dissolution process. Eqs. ( 25) -( 27) presents the respective regression models for %D, %G and %X, while Figure 3 presents the regression characteristics for the higher temperature operation. By comparing the kernel density estimate and the normal probability density function (PDF) of the residuals, we observe that of %D and %G to be normally distributed while that of %X is skewed away from the normal distribution.

The variance stabilizing transformations presented in Eqs. ( 7) and ( 8) were utilized and a smoothing parameter of gave a residual vector where the kernel density estimates more closely fitted the normal PDF. Table 6 presents the quantitative model diagnostics for high temperature operation for all three responses. For the optimization of the responses from the dissolution process, contour plots of temperature as a function of H 2 O 2 concentration at various times used in this study, (1, 3.5 and 6 hours) were prepared (Figure 5). The optimal regions are indicated by the white portions of contour plots. In this higher temperature dissolution process, at 150 o C and 4 M H 2 O 2 , increasing the time from 1 hour to 3.5 hours caused a significant increase in the extent of dissolution from a maximum of 71 % to 85%. A maximum period of 6 hours projected the extent of dissolution to about 94 % as can be observed in Figure 5. %G of 32, 39 43 % was obtained from residue of higher temperature dissolution at 1, 3.5 and 6 hours respectively.

The maximum yield occurred in residues treated with 2-3.5 M H 2 O 2 solution within the temperature of range 120-150 o C after 6 hours indicating the probability of obtaining the optimal %G in this range. Caution should be taken when reading the results for xylose as a transformation was used on the response in order to improve upon the regression characteristics.

Figure 5: Contour plots of model responses for higher temperature operation

Regression characteristics for lower temperature dissolution

Eqs. ( 28) -(30) presents the respective regression models for %D, %G and %X, while the regression characteristic plots are presented in Figure 6. By comparing the kernel density estimate and the normal probability density function (PDF) of the residuals, we observe that of %D be normally distributed while those of %G and %X are slightly skewed away from the normal distribution. Unlike the case of higher temperature operation, use of variance stabilizing transformations did not improve fitting between kernel density estimates and normal PDF. The confidence contours of the main effect showed a similar pattern as those observed in the case of higher temperature operation (Figure 7). Table 7 presents the quantitative model diagnostics for low temperature operation for all three responses. Conditions for optimum lower temperature dissolutions are summarized further in Table 8.

Kinetics of acid hydrolysis process

The kinetics of glucose and xylose yield was studied in three different scenarios: The first scenario was for different temperatures and peroxide concentrations, the second was for different temperatures at constant peroxide concentration (4M) and the third was for constant temperature (150℃) at different peroxide concentrations. The regression models for the effect of the time, Hydrogen peroxide concentration and temperature on %G and %X yield was used to generate data needed to fit the kinetic models. Figure 9 presents the model fitting for glucose yield for the first kinetic scenario, which is observed that in all cases, the experimental values lie within the 95% confidence interval of the model predictions. The % G is also observed to increase with time, hydrogen peroxide concentration and temperature.

Since %G does not approach maximum value within the time interval considered, it is possible for higher %G to be obtained if the reaction time is extended beyond the 6 hours' period. The model fitting for xylose obtained for the first kinetic scenario is presented in From the rate expressions presented in Eqs. ( 19) and ( 20), the two kinetic constants governing the yield of each of the reducing sugars have been estimated and presented in Table 9. Figure 15 shows the profile of the cellulose and hemicellulose hydrolysis constants as a function of temperature (kinetic scenario 2), which shows an exponential dependence of the constants with temperature. The results corroborate the findings of previous studies, where the kinetic constants increase exponentially with temperature.

Conclusion

The optimization of operating conditions and synthesis of reactor structures to respectively maximize the overall and instantaneous yields of reducing sugars from Cocoa Pod Husk using hydrogen peroxide as solvent has been presented. The Saeman scheme for biomass hydrolysis was used to develop kinetic models to describe the dynamics of glucose and xylose production. Multiple linear regression was used to model the experimental data and determine the optimal conditions to maximize the overall yields of glucose and cellulose. The results show that the kinetic models agree well with data and the maximum glucose and xylose yields from biomass obtained are respectively 43.49% and 11.24% for higher temperature operation (100 0 C-150 0C) and 38.2780% and 10.272% for lower temperature operation (30 0 C-100 0 C). Partial dissolution of CPH in H 2 O 2 improves the total glucose yield by 190 % and xylose yield by 34.6 % at the optimum conditions specified in this study. The dissolution process considered in this study will serve a good pretreatment method for a higher yield of sugars from CPH.

  concentrations of cellulose, glucose, hemicellulose and xylose. The rate of cellulose hydrolysis is described by while is the rate constant for breakdown of glucose into degradation products. Similarly, is the rate of hemicelluloses hydrolysis and is the rate of xylose degradation. Due to the simplicity of the model, it can be solved analytically to obtain Eqs. (19) and[START_REF] Xu | Qualitative and quantitative analysis of lignocellulosic biomass using infrared techniques: a mini-review[END_REF], which describe the kinetics of glucose and xylose yield respectively.

Figure 1 :

 1 Figure 1: Process schematics for conversion of CPH to reducing sugars

Figure 2 :

 2 Figure 2: FT-IR spectrum results for raw CPH powder

Figure 3 :

 3 Figure 3: Regression characteristics for higher temperature dissolution

10 4 .

 4 56e-07 4.56e-08 Total 19 2.37e-06 1.25e-07 The confidence contours of the three regression responses comparing the main effects of the three factors is presented by Figure 4. It can be observed from Figure 4 that the main effect of hydrogen peroxide ( ) and that of temperature ( ) show very little correlation with one another while the main effect of residence time ( ) is highly correlated with that of hydrogen peroxide and temperature. The results suggest that it would have been better to fix the residence time of the experiments and only use hydrogen peroxide and temperature as the experimental factors.

Figure 4 :

 4 Figure 4: Confidence contours showing degree of correlation amongst the main factor effects

Figure 6 :

 6 Figure 6: Residual comparism plots for low temperature operation

Figure 7 :H 2 O 2 .

 722 Figure 7: Joint confidence intervals for lower temperature dissolution

Figure 8 :

 8 Figure 8: Contour plots of model responses for higher temperature operation

Figure 10 .

 10 Figure 10. Unlike the case of glucose, the yield of xylose is observed to increase rapidly

Figure 11 :

 11 Figure 11: Kinetics of glucose yield for different temperatures at 4M H 2 O 2 .

Figure 12 :

 12 Figure 12: Kinetics of xylose yield for different temperatures at 4M H 2 O 2 .

Figure 13 :Figure 14 :

 1314 Figure 13: Kinetics of glucose yield for T=150℃ at different peroxide concentrations

Figure 15 :

 15 Figure 15: Temperature dependence of cellulose and hemicellulose hydrolysis constants

Table 1 :

 1 Factor levels in the design of experiment

	Factor	Low level	High level
	H 2 O 2 Concentration (M)	0	4
	Temperature ( o C)	100 (30*)	150 (100*)
	Time (h)	1	6
	*levels used for lower temperature dissolution.

Table 2 :

 2 Proximate analysis Lignocellulosic components accounted for a total of 79.31 % of the CPH powder. While cellulose is dominant in the lignocelluloses (Karimi et al., 2016), a rather low amount of cellulose was observed (23.04 wt%) in the CPH contrary to the 41.94% and 35.4% reported by Daud et al (2013) and[START_REF] Odubiyi | Waste water treatment with Activated Charcoal produced from Cocoa pod husk[END_REF]. This difference could be due to geographical locations of the samples. However, an expected amount of hemicellulose (38.08 wt%) and lignin (18.19 wt%) was obtained (Table3). This is consistent with what was reported by

	Component	Amount (%)
	Initial moisture	86.10±0.11
	Final moisture	10.10±0.18
	Volatile matter	64.19±0.15
	Fixed carbon	10.40±0.20
	Ash content	15.31±0.11
	Lignocellulosic components and sugars in CPH
	Daud at al (2013). For reducing sugars, raw CPH powder yielded 15 wt% glucose, 8.35 wt%
	xylose and 0.99 wt% arabinose (Table

Table 3 :

 3 Lignocellulose component of CPH

	Component	Amount, wt %
	Cellulose	23.04±0.02
	Hemicellulose	38.08±0.06
	Lignin	18.19±0.15
	Total	79.31%

Table 4 :

 4 Reducing Sugars from the hydrolysis of raw CPH

	Sugar	Amount (%)
	Glucose	15.00±1.10
	Xylose(%X)	8.35± 0.70
	Arabinose	0.99± 0.02
	FTIR analysis

FTIR detected the presence of the major functional organic groups that constitute the CPH biomass structure. Raw CPH components comprises of compounds with hydroxyl groups (O-H) indicating the presence of alcohols and phenols; Carboxylic acids shown by the presence of both O-H stretch peak and the C=O stretch detected at wavelengths of 2917.47 cm-1 and 1602.96 cm-1 respectively (Figure

Table 5 :

 5 functional groups detected in CPH

	Wavelength, cm -1 Remarks	Functional groups present
	1031.75	C-O-C stretch	Aliphatic Amines
	1602.96	C=O stretch	Alcohols, Carboxylic acids, Esters
	2917.47	C-H stretch	Alkanes
	3279.62	O-H stretch, vibration	Alcohols, Phenols

Table 6 :

 6 Quantitative model diagnostics for high temperature dissolution

		Source	DF	SS	MS	Test ratios	F-test
	Dissolution	Regression 9 Residual 10 Total 19	6635.30 100.17 6735.40	737.25 10.02 354.49
		Source	DF	SS	MS	Test ratios	F-test
	Glucose	Regression 9 Residual 10 Total 19	1228.40 38.36 1266.7	136.49 3.836 66.67
		Source	DF	SS	MS	Test ratios	F-test
	Xylose	Regression 9 Residual	1.91e-06 2.13e-07

Table 7 :

 7 Quantitative model diagnostics for low temperature dissolution

		Source	DF	SS	MS	Test ratios	F-test
	Dissolution	Regression 9 Residual 10 Total 19	3855.70 232.87 4088.6	428.41 23.29 215.19
		Source	DF	SS	MS	Test ratios	F-test
	Glucose	Regression 9 Residual 10 Total 19	633.63 145.55 779.18	70.40 14.55 41.01
		Source	DF	SS	MS	Test ratios	F-test
	Xylose	Regression 9 Residual 10 Total 19	38.89 10.37 29.26	4.321 1.037 1.540

Table 8 :

 8 Optimal conditions for dissolution and hydrolysis process

	Temperature regime	Responses	Optimized explanatory variables H 2 O 2 Conc, M Temp, o C Time, h	Optimized responses %D %G % X
	High temperature	%D %G %X	3.585 3.072 2.794	150.000 135.717 100.000	6.000 6.000 2.287	95.858 ------	---43.494 ---	---11.243
		%D	4.000	100.000	4.790	69.780	---	---
	Low temperature	%G	4.000	100.000	6.000	---	38.2780	---
		%X	4.000	100.000	6.000	---	---	10.272

Table 9 :

 9 Estimated kinetic constants for glucose and xylose yield models

	Glucose

Xylose Operating conditions Scenario 1: Changing temperature and peroxide concentration

	1.470	-0.039	1.987	-0.002	100	0
	1.673	-0.035	2.017	0.021	112	1
	1.889	-0.031	2.118	0.031	125	2
	2.285	-0.041	2.507	0.056	137	3
	2.736	-0.030	2.795	0.118	150	4
		Scenario 2

: Changing temperature and constant peroxide concentration

	2.001	-0.087	1.872	0.051	100	4
	2.017	-0.072	2.091	0.073	112	4
	2.056	-0.060	2.237	0.097	125	4
	2.164	-0.051	2.462	0.122	137	4
	2.364	-0.050	2.990	0.147	150	4
		Scenario 3

: Constant temperature and changing peroxide concentrations

	1.892	-0.011	1.552	0.037	150	0
	2.565	-0.026	2.458	0.029	150	1
	2.396	-0.027	2.631	0.042	150	2
	2.535	-0.038	2.753	0.058	150	3
	2.644	-0.039	2.850	0.104	150	4

Acknowledgement

The authors express profound appreciation to the local farmers of the Agona community, Kumasi-Ghana, for providing the raw cocoa pod husks needed for this investigation. The Authors wish to express gratitude to the Laboratory Manager and the Technicians of the Department of Chemical Engineering, College of Engineering, Kwame Nkrumah University of Science and Technology Kumasi Ghana.

Higher temperature dissolutions have a negative effect on the yield of xylose from the residue, which is in accord with what has been reported earlier in this study.