Review Reports

Round 1

Reviewer 1 Report

See file attached.

Comments for author File: Comments.pdf

Author Response

Reviewer 1

 

Reviewer stated that “The deep analysis performed to the results of the results is interesting, as well as the actual values of biobutanol concentration for the optimum production medium. The manuscript is well writes, self-contained and clear”.

Reviewer highlighted a major concern stating that in “It is mentioned that “p-value was 0.646”, and in Table 7, pvalue varies between 0.000 and 0.646. What is the meaning of these values? It is common to choose “p=0. 05” as the arbitrary threshold of the so-called "statistically significant effect”; in this concept “p=0.000” does not exist and “p>0.050” should be rejected”.

The reviewer is referring to the hypothesis p-value "statistically significant effect” in his comment. There is a difference between model p-value and p-value of “lack-of-fit” as in ANOVA as referred to in the manuscript. The lack-of-fit test is used as support test for adequacy of the fitted model. It is a measure of the significance of the lack-of-fit error which is its contribution to the residual error; a statistic made up of lack-of-fit and pure error. In the error probability used by ANOVA statistics, P of the small lack-of-fit F-statistic is large compared to the significance level, then the lack-of-fit is not significant and the model adequately explains data in the region of experimentation. Conversely, if the F-statistic of the lack-of-fit error is large (indicating that the bulk of the error of the residuals is due to lack-of-fit) and its corresponding error probability, p-value is small, then the lack-of-fit test is significant and suggests the inadequacy of the regression model to explain the data variations.

We know that higher the F statistics, the more likely we are to reject the null hypothesis. Also, the lower the p-value, the more likely we are to reject the null hypothesis.

We may get the F statistics value way greater than 0. So, the p-value tells us the probability of getting this F statistics value (which is greater than 0) and its significance level. If the p-value is less than our significance level, we reject the null hypothesis in ANOVA best fit analysis i.e. F statistics is 0.

 

Reviewer stated based on previous comment that “This affirmation means that, in Table 7, the only significant effect is the variation among blocks, p=0.003, and the other p-values are nonsense. Please explain what you are trying to say using these p-values or substitute them by another statistic indicator.”

If we get a F statistics value of 26.84 in Table 6, the p-value will tell us the likelihood of our F statistics value to be significant at 5% significance level (i.e. 0). As in Table 7, when we got p-value (i.e., Pr(>F)) = 0.127 (which is for F stat. = 2.66). So for 0.05 Level of significant, since p-value lack of fit exceeds that, then we accept null hypothesis. This is a common analysis in the ANOVA, and accordingly no need for revision as this is the indicator that is commonly used in all literature and previously published work.

Please note that in the revised manuscript, we replaced the “0.000” with “0” in Tables 6 and 7.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

The authors have carried out interesting work about exploring the strategies for improving lignocellulosic butanol fermentation. While the work itself is relevant as Butanol is considered a promising biofuel alternative given its proven advantages over other popular biofuels like Bioethanol, the novelty is quite missing in this work.

There have been several previous reports regarding Clostridial strain development for better biobutanol production via ABE fermentation. One such study reports higher biobutanol titers than that reported in this work (12.56 g/L) when cultures were co-cultivated with Anthrobacter species on Sweet Sorghum Juice (https://www.mdpi.com/2311-5637/8/4/160 ). Authors also report poor pretreatment performance of chosen method on both non-wood and softwood materials (wheat straw and Spruce). In absence of enough free sugar in pretreatment hydrolysates, solid glucose and xylose were added in hydrolysates which in turn would increase the cost of the process.

I would like to know the author’s thoughts on above points before this manuscript can be accepted for publication.

Thanks and best wishes!

 

 

Author Response

Reviewer 2

Reviewer stated that “The authors have carried out interesting work about exploring the strategies for improving lignocellulosic butanol fermentation. While the work itself is relevant as Butanol is considered a promising biofuel alternative given its proven advantages over other popular biofuels like Bioethanol, the novelty is quite missing in this work”.

I believe that we made this clear in the introduction part, as we discussed how “The challenge with most conventional biofuels is sustainability and GHG emissions as they are produced using foods such as corn and sugarcane as feedstock [2]. As compared to conventional biofuels, new generation of biofuels use lignocellulosic feedstock which produce less GHG emissions [3]. We also stated that “the development bottleneck of bio-butanol is its high selling price ($3~3.5/gallon), which is uncompetitive to $1.5/gallon of conventional bio-ethanol [5]. Therefore, improving the efficiency of production with lignocellulosic feedstock became the primary feasible way to lower the overall cost of production”. We do understand the importance of biobutanol biofuel that is widely described in the literation, and accordingly our interest was to improve production in order to reduce market price.

We also added the following paragraph to the revised manuscript: “Butanol has several advantages over ethanol as a biofuel as it has higher energy density, lower hygroscopicity, compatibility with Existing Infrastructure, lower Vapor Pressure, reduced corrosivity, and wider temperature range. Despite these advantages, butanol production has faced challenges related to cost, scalability, and the development of efficient fermentation processes. Ethanol, on the other hand, has been more widely adopted as a biofuel due to its established production processes and lower production costs. However, ongoing research and development in the field of biofuels may lead to increased use of butanol in the future, especially as technologies improve and economies of scale are achieved”.

Reviewer stated that “There have been several previous reports regarding Clostridial strain development for better biobutanol production via ABE fermentation. One such study reports higher biobutanol titers than that reported in this work (12.56 g/L) when cultures were co-cultivated with Anthrobacter species on Sweet Sorghum Juice”.

As stated in the abstract, “The present study investigates approaches to enhance the ability of bio-butanol production using lignocellulosic feedstock via supplements of metabolism perturbation”. This implies that the examined metabolism perturbation will enhance any production achieved under optimized conditions. Conditions and bacterial strain that were examined in the present study acted as a model system, while we do understand that there are others systems that can achieve higher production of ABE. We have reviewd and cited other systems that we adapted in our lab that achieved higher production.  Accordingly, the published work that reviewer suggested can apply the tested metabolic perturbation in the present work, and we postulate that it will even improve production. We clearly stated that “As compared to traditional lignocellulosic feedstock post-treatment method, metabolic perturbations method shows advantages in terms of productivity and economics”.

In the revised manuscript, we added “as a reference model fermentation” to clarify this point.

Reviewer stated that “Authors also report poor pretreatment performance of chosen method on both non-wood and softwood materials (wheat straw and Spruce)”.

In the present work, we established the very basic pretreatment methodology as a standard for the model system we used as a reference (physical and acidic pretreatment). Please note that this has widely been investigated in the literature, so there are other methodologies that are proven to enhance ABE fermentation. Our objective was to test perturbation approaches and how it impacts production in general, and used our model system as a reference. The model and optimization can always be applied to all improved process of ABE fermentation and achieve higher yield and production. In preliminary investigations, impacts of CaCO3, furfural and methyl red on cell growth, sugar utilization, acid production and butanol production were evaluated in glucose and xylose feedstock, separately. For that reason, addition of pure glucose and xylose (essential sugars in ABE production) was utilized to reach maximum obtained from WS in the literature according to reference 10. Following that, optimization was used to maximize butanol production from glucose and xylose feedstocks, respectively.

Reviewer stated that “In absence of enough free sugar in pretreatment hydrolysates, solid glucose and xylose were added in hydrolysates which in turn would increase the cost of the process”.

In this study, instead of using enzymatic treatment, solid glucose and xylose were directly added to make total sugar concentrations 60g/L and glucose concentration 27 g/L for lignocellulosic hydrolysates, which was comparable to the wheat straw hydrolysate after enzymatic treatment in previous study (reference 10). The purpose of optimization was to maximize butanol production in glucose and xylose, so addition of pure sugars was essential to mimic best pretreatment methodologies described in the literatures.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Please see the attached document.

Comments for author File: Comments.pdf

Author Response

Reviewer Comments_ Round Two_ 2562081

 

Reviewer stated that “The 2nd version of this manuscript is still well written, self-contained and clear”.

 

Reviewer stated that authors introduced new concerns:

“Line 43, page 2. Although ethanol is used as biofuel, up to date the problem of the energy required to produce it anhydrous against the energy that EtOH supply to the engine is still unsolved. This problem is beyond costs. Now, considering the biobutanol, coming from an ABE fermentation: Has been this problem addressed? Is environmentally friendly to separate and concentrate the biobutanol to obtain it anhydrous? Will this biofuel return more energy when burned than the amount used to purify it? The discussion introduced seems to be a ‘quick evasion’ of the reviewer comment instead of a reasonable reply.”.

The following paragraph was added on Page 2 and Line 51 in order to address the points arose by reviewer:

In spite of the better characteristics of biobutanol, its yield and titer through fermentation, in addition to production cost are lower compared with other biofuels such as ethanol. Therefore, the improvement of substrates, microbial strains and processes for its cost competitive production became a priority research [6]. Another issue is the dilute butanl-water solution produced by fermentation process, which requires further processing and thus energy consumption to produce anhydrous biobutanl with minimum water contents [7]. We also added/ cited two new references (references 6 and 7) discussing the points in further details.

 

Reviewer stated that “About my previous comment about the “p-value”: I read the author’s reply, acknowledge the information and, by now, I understand which is the indicator that you are talking about. However, do you really think I would be the only one confused? I consider that a brief explanation of which statistical indicator are you using should be in the manuscript. Please, include this explanation.”

The following paragraph was added on Page 17 and Line 423 in order to clear any confusion readers may have:

“The lack-of-fit F-test is used as support test for adequacy of the fitted model. In the error probability used by ANOVA statistics, small P of the lack-of-fit compared to the significance level (i.e., F-statistic is large), then the lack-of-fit is significant and the model doesn’t adequately explain data in the region of experimentation (there is lack of fit in the simple linear regression model). Accordingly, the lower the p-value (i.e., the higher the F statistics), the more likely to reject the null hypothesis in favor of the alternative”[28]. A new reference was added/cited in the revised manuscript.

Author Response File: Author Response.docx

Reviewer 2 Report

Thanks for your work in revising the manuscript. 

Author Response

Thank you for your help with reviewing the manuscript.. There is no comments provided by the reviewer in the second round of revision.