How Many Acerola (Malpighia emarginata DC.) Fruit Are Required for Reliable Postharvest Quality Assessment?

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper offers a new research perspective and has certain scientific value in the experimental design and data analysis of scientific research, especially providing important references in sample size optimization and variety comparison. I am very interested in this topic. Please revise it according to the following suggestions and then submit it to me for consideration.

 

Some specific comments are as follows:

The title optimization could be considered to be changed to: "Research on Sample Size Optimization for Postharvest Quality Assessment of Brazilian Cherries Based on Bootstrap and Maximum Curvature Point Method".

The current abstract is very disorganized, like a pile of statements of results. It is suggested to keep it within 250 words and clearly state the research objectives, methods, key results and conclusions.

The titles of Figures 2 and 3 should be more specific, such as "Confidence Interval Changes in Physical Traits of Different Varieties of Brazilian Cherries".

Ensure that the format of the references complies with the requirements of the journal and consider adding the following literatures to enrich the manuscript:

The combination treatment of chlorogenic acid and sodium alginate coating could accelerate the wound healing of pear fruit by promoting the metabolic pathway of phenylpropane. Food Chemistry, 414, 135689.

The crucial evaluation indexes and relative measurement methods of edible value for fresh fruits and vegetables: A review. Future Postharvest Food, 1-15.

Effective strategies to enhance ultraviolet barrier ability in biodegradable polymer-based films/coatings for fruit and vegetable packaging. Trends in Food Science & Technology, 104139.

Critical assessment of the delivery methods of chemical and natural postharvest preservatives for fruits and vegetables: A review. Critical Reviews in Food Science and Nutrition, 1-23.

Some parts of Table 1 and Table 3 can be combined to reduce repetitive information.

Modify this sentence: the gain in precision achieved by the LRP method compared to the PD method was minimal.

The Introduction section is rather scattered. It is suggested to reorganize it as follows: "Economic Value of Brazilian Cherries → Insufficient post-harvest research → Sample size issues → Research Objectives".

Before the method section, it should be stated that "We assume that the sample size requirements for different varieties vary."

In the discussion section, it should be stated that "This study only evaluated four varieties, and more genotypes can be expanded in the future."

The reason for "Cabocla requires a larger sample size" should be analyzed in combination with its CV value.

The conclusion section should emphasize that "This study provides a new method for the post-harvest experimental design of tropical fruits."

To supplement future research directions, machine learning can be combined to optimize sample size prediction.

It should be stated whether the fruits come from the same orchard, the age of the trees, the time of harvest, etc.

It should be explained why PD and LRP were chosen instead of other MCP methods (such as Spline).

Consider the influence of environmental factors (such as temperature and humidity) on variability.

Supplement data distribution tests, such as the Shapiro-Wilk test, to prove whether the data is normally distributed.

Why choose these four? Does it cover high/low variability varieties?

Discuss whether the results of this study (20 fruits) can be included in future post-harvest research standards or agricultural part testing. What scenarios of experimental analysis is it applicable to? Is it applicable to other highly variable fruits?

Comments for author File: Comments.pdf

Author Response

We would like to thank the reviewer for their careful reading and valuable comments, which have contributed to improve the quality and clarity of our manuscript. Below, we provide point-by-point responses to each suggestion.

 

Comments 1: The title optimization could be considered to be changed to: "Research on Sample Size Optimization for Postharvest Quality Assessment of Brazilian Cherries Based on Bootstrap and Maximum Curvature Point Method". 

Response 1: Thank you for your thoughtful suggestion regarding the article title. After careful consideration, we have chosen to retain the original title, "How Many Acerola (Malpighia emarginata DC.) Fruit Are Required for Reliable Postharvest Quality Assessment?", with the addition of the scientific name. This decision was made with the intent of reaching a broader readership, including researchers, extension professionals, and practitioners in the field of horticulture who may be less familiar with statistical techniques such as bootstrap and maximum curvature point methods.

The selected title aims to be concise, engaging, and accessible, while still reflecting the core objective of the study. Methodological rigor is fully detailed within the manuscript, particularly in the Materials and Methods and Results sections, where the use of advanced statistical approaches is clearly described.

Furthermore, we opted to maintain the use of the term "acerola" throughout the manuscript for two main reasons: (1) "acerola" is the most widely used and internationally recognized common name for Malpighia emarginata DC., particularly in the scientific literature, databases, and commercial contexts; and (2) the term "Brazilian cherry" is often used ambiguously and can refer to other species such as Eugenia uniflora L. (commonly known as pitanga or Surinam cherry), which may lead to confusion among readers.

Therefore, to ensure clarity and accuracy, especially for an international scientific audience, we believe that "acerola" is the most appropriate and specific term to use in this context.

 

Comments 2: The current abstract is very disorganized, like a pile of statements of results. It is suggested to keep it within 250 words and clearly state the research objectives, methods, key results and conclusions.

 Response 2: Thank you for this important observation. We acknowledge that the initial version of the Abstract lacked a clear and structured flow. Following your suggestion, we have revised the Abstract to improve its organization and clarity. The updated version now presents the objective, methodological approach, key results, and main conclusion in a concise and logical sequence, and it has been adjusted to meet the 200-word limit established by the journal.

 

Comments 3: The titles of Figures 2 and 3 should be more specific, such as "Confidence Interval Changes in Physical Traits of Different Varieties of Brazilian Cherries". 

Response 3: We understand the importance of providing specific and informative figure titles. However, we would like to clarify that the actual changes in confidence intervals across different sample sizes are presented in Figure 4, which provides the maximum 95% confidence interval (CI_95%) widths and the corresponding sample sizes estimated by three different methods for each cultivar and trait.

Figures 2 and 3, in contrast, were designed to provide a visual representation of the bootstrap distributions of the mean estimates across resampled data for each trait and cultivar. These figures illustrate the variability and convergence behavior of the estimates, helping the reader to interpret how sample size affects estimation stability—but do not summarize confidence interval changes as explicitly as Figure 4 does. To improve clarity, we have revised the titles of Figures 2, 3, and 4 to better reflect their content. 

 

Comments 4: Ensure that the format of the references complies with the requirements of the journal and consider adding the following literatures to enrich the manuscript:

The combination treatment of chlorogenic acid and sodium alginate coating could accelerate the wound healing of pear fruit by promoting the metabolic pathway of phenylpropane. Food Chemistry, 414, 135689.

The crucial evaluation indexes and relative measurement methods of edible value for fresh fruits and vegetables: A review. Future Postharvest Food, 1-15.

Effective strategies to enhance ultraviolet barrier ability in biodegradable polymer-based films/coatings for fruit and vegetable packaging. Trends in Food Science & Technology, 104139.

Critical assessment of the delivery methods of chemical and natural postharvest preservatives for fruits and vegetables: A review. Critical Reviews in Food Science and Nutrition, 1-23.

Response 4: Thank you for the valuable suggestion. We have carefully revised the reference formatting to ensure full compliance with the journal’s style guide.

We thoroughly examined each of the proposed articles, and we chose to incorporate the article “The crucial evaluation indexes and relative measurement methods of edible value for fresh fruits and vegetables: A review” [ref. 32] due to its detailed explanation about titratable acidity and its influence on the sour taste of fruits, which supports and enriches our discussion on fruit sensory quality.

 

Comments 5: Some parts of Table 1 and Table 3 can be combined to reduce repetitive information.

Response 5: We appreciate the reviewer’s suggestion regarding the information presented in Tables 1 and 3. However, we would like to clarify that these tables address different aspects of the data.

Table 1 shows descriptive statistics of the raw data, providing an initial overview of the variables analyzed. In contrast, Table 3 presents the maximum curvature points (i.e. optimal sample sizes) estimated from the data after applying the bootstrap resampling method.

Thus, we believe it is important to keep the information in separate tables to ensure clarity and precision in the presentation of the results, avoiding potential confusion for the readers.

 

Comments 6: Modify this sentence: the gain in precision achieved by the LRP method compared to the PD method was minimal.

Response 6: Thank you for the suggestion. We have revised the sentence (see lines 353–356).

 

Comments 7: The Introduction section is rather scattered. It is suggested to reorganize it as follows: "Economic Value of Brazilian Cherries → Insufficient post-harvest research → Sample size issues → Research Objectives".

Response 7: We appreciate the suggestion to reorganize the Introduction section to improve the logical flow and coherence of the text. We agree that the proposed structure, beginning with the economic value of acerola, followed by the lack of postharvest research, the sample size issue, and concluding with the research objectives, contributes to a clearer presentation of the topic. Based on this, we have revised the Introduction according to the suggested framework, while retaining the essential information and enhancing the cohesion between paragraphs.

 

Comments 8: Before the method section, it should be stated that "We assume that the sample size requirements for different varieties vary."

Response 8: As suggested, we have explicitly stated our assumption about cultivar-specific sample size variability in the Introduction, just before the Methods section (see lines 82–84).

 

Comments 9: In the discussion section, it should be stated that "This study only evaluated four varieties, and more genotypes can be expanded in the future."

Response 9: As recommended, we have added a statement in the Discussion section acknowledging that our study evaluated four cultivars and suggesting the inclusion of more genotypes in future research (see lines 401–403).

 

Comments 10: The reason for "Cabocla requires a larger sample size" should be analyzed in combination with its CV value.

Response 10: After further analysis, we identified that the larger sample size required for the ‘Cabocla’ cultivar is primarily associated with its wide 95% confidence interval (CI95%) for this quality trait (see line 259, Figure 4). Although the CV is an important measure of relative variability, in this case, the broader CI_95% had a greater influence on the sample size estimation, particularly when using the maximum curvature point (MCP) method, which is sensitive to the stability of precision gains. This indicates that higher uncertainty in the mean estimates of ‘Cabocla’ drove the need for a larger number of fruits to achieve reliable results.

 

Comments 11: The conclusion section should emphasize that "This study provides a new method for the post-harvest experimental design of tropical fruits."

Response 11: We revised the Conclusion section to emphasize the methodological contribution of our study. Specifically, we added a statement highlighting that this research introduces a novel and statistically sound framework for postharvest experimental design in tropical fruits (see lines 428–432).

 

Comments 12: To supplement future research directions, machine learning can be combined to optimize sample size prediction.

Response 12: We agree that incorporating machine learning approaches holds great potential for optimizing sample size prediction, especially when dealing with complex, nonlinear, and high-variability datasets commonly found in postharvest studies. We have included this perspective in the final paragraph of the Discussion (see lines 407–412) and Conclusion (see lines 437–439) sections as a potential avenue for future research.

 

Comments 13: It should be stated whether the fruits come from the same orchard, the age of the trees, the time of harvest, etc.

Response 13: We agree that providing additional information regarding the experimental conditions helps enhance the transparency and reproducibility of the study. Accordingly, we have updated the Materials and Methods section to include details on the orchard location, tree age, and harvest timing (see section 2.1 Experimental Conditions and Plant Material).

 

Comments 14: It should be explained why PD and LRP were chosen instead of other MCP methods (such as Spline).

Response 14: Thank you for your comment. We acknowledge that multiple MCP-based methods can be used to define the optimal sample size, including the Spline method, as demonstrated by Silva and Lima (2017). However, in our study, we chose to focus on the PD and LRP methods because they consistently provide sample sizes closer to the stabilization point of the confidence interval curve, which is considered essential for practical applications in experimental planning.

This choice was based on findings from Bittencourt et al. (2022), who compared four MCP-based methods (General, PD, LRP, and Spline) for sample size determination in cauliflower seedlings. They concluded that, although the Spline method typically suggested a higher sample size than the General method, it still underestimated the true stabilization point of the curve. In contrast, the PD and LRP methods led to narrower confidence intervals and more reliable estimates of the overall mean, which are crucial in studies aiming for high experimental precision.

Additionally, the PD method was preferred over LRP in certain scenarios due to its balance between accuracy and practicality, as LRP often demands higher sample sizes, which may not be feasible due to resource constraints. Therefore, to ensure methodological robustness and practical feasibility, we opted to adopt the PD and LRP approaches and excluded the Spline method from our analysis.

 

Comments 15: Consider the influence of environmental factors (such as temperature and humidity) on variability.

Response 15: We acknowledge that environmental conditions, such as temperature and humidity, can significantly influence fruit quality and, consequently, data variability. In our study, however, all genotypes were cultivated under the same irrigation regime and agronomic management practices, in order to minimize the influence of such factors and ensure that observed differences could be primarily attributed to genotypic variation.

 

Comments 16: Supplement data distribution tests, such as the Shapiro-Wilk test, to prove whether the data is normally distributed.

Response 16: Thank you for your insightful comment. We would like to clarify that the use of bootstrap resampling followed by nonlinear modeling and the application of maximum curvature point (MCP) methods was intentionally chosen because this approach does not require the assumption of normality. Unlike frequentist methods, which rely on parametric assumptions, the bootstrap-based strategy is particularly well suited for datasets with unknown or non-normal distributions, allowing for the more robust estimation of sample size across diverse conditions.

In response to the reviewer’s suggestion, we conducted Shapiro–Wilk normality tests for all measured variables. The results indicated that some variables did not follow a normal distribution, which reinforces the appropriateness of the adopted resampling-based methodology in this study. The description of the Shapiro–Wilk normality test was included in the Material and Methods section, and the outcomes have now been included in the Supplementary Material for transparency (see Table S2).

 

Comments 17: Why choose these four? Does it cover high/low variability varieties?

Response 17: Thank you for your question. The selection of these four acerola cultivars was based on their outstanding performance in recent breeding and evaluation programs. ‘BRS Rubra’, ‘Cabocla’, and ‘Costa Rica’ were identified by Ferreira et al. [23] as the top cultivars among 35 evaluated, presenting the most desirable combination of traits for fresh consumption.

‘Junko’, on the other hand, is the most widely cultivated commercial acerola cultivar, with several plantation areas in Northeastern Brazil. Recently, it was the best-performing genotype in a trial involving 95 genotypes conducted by Silva et al. [29], standing out as the most suitable for industrial vitamin C extraction.

Our intention was to focus on cultivars that are likely to be widely adopted in the coming years, both for fresh market and industrial purposes, as well as in genetic improvement programs. While variability levels were not the primary criterion for selection, these cultivars inherently represent a range of phenotypic diversity, which adds value to the investigation of optimal sample size for fruit quality traits.

 

Comments 18: Discuss whether the results of this study (20 fruits) can be included in future post-harvest research standards or agricultural part testing. What scenarios of experimental analysis is it applicable to? Is it applicable to other highly variable fruits?

Response 18: We appreciate the reviewer’s insightful question. As now addressed in the revised manuscript (see Discussion, lines 382–389), we included a specific discussion on the applicability of our findings to future postharvest research standards. Considering that the OSS estimations were derived using robust statistical approaches (nonlinear power models and MCP), the results provide a solid basis for standardizing sampling protocols in acerola research. These recommendations are particularly useful in diverse experimental scenarios, including quality trait evaluations, shelf-life studies, postharvest treatment trials, cultivar comparisons, determination of harvest timing, industrial quality control, and consumer acceptance testing.

We also clarified that the proposed statistical framework is not species-specific and may be extended to other highly variable fruits, as it is based on modeling variability and confidence interval precision rather than fixed biological parameters. However, due to the unique variability patterns observed among different fruits and traits, we recommend that OSS be estimated individually for each species or cultivar to ensure accurate and representative evaluations. This added discussion strengthens the relevance and broader applicability of our results (see lines 390–396).

 

English editing: In accordance with the reviewers’ recommendations, the English language of the manuscript has been thoroughly revised to improve clarity and readability. A certificate of professional English editing is attached.

 

Thank you once again for your constructive feedback and support in improving the quality of this work!

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Authors,

After reviewing the manuscript, my observations and recommendations are:

Title

Line 2  - Possibly adding the “botanical name” (Malpighia emarginata DC.)

The abstract

The abstract is suitable for a scientific publication, instructive, and well-structured. It describes in detail the purpose of the study, the materials and procedures used, the main findings, and how the findings will be used in practice.

Line 11: Acerola – botanical name

Line 11: In accordance to conventional language, vitamin C should at least once be referred to as ascorbic acid.

 The introduction

The introduction gives enough context for readers outside of the immediate field to comprehend the problem and hypotheses.

Lines 48-54: Indicate how your research differs or builds upon earlier OSS work.

Line 42: "Recent" is a subjective and ambiguous term. A timeline or particular citation explaining the emergence of postharvest investigations would be useful.

Lines 53, 55 and 57: sd  -  capitalize abbreviation (SD) for standard formatting

You may briefly describe the unique relevance of techniques like MCP to acerola.

Lines 62-64: Is not clear if the bootstrap is used in conjunction with optimization techniques or error thresholds to indirectly influence sample size selections.

Your findings fit into the body of previous literature quite well.

You've presented a strong argument for the necessity of your work, its connections to previous OSS studies on fruit crops, and the special significance of acerola for contemporary sampling techniques. The narrative will be considerably stronger with a few minor changes to increase clarity and emphasize importance.

Materials and Methods

The methods are generally suitable, well-structured, and scientifically justified, even though some areas might benefit from more clarification or detail.

The examination of four cultivars, including experimental (BRS Rubra, Cabocla, Costa Rica) and commercial ('Junko') types, contributes to the study's secondary objective (cultivar comparison).
The experiment was carried out under field circumstances typical of commercial production, with adequate geographical and agronomic detail.

While DFI titration is a traditional approach for vitamin C, it may be less precise than chromatographic methods (e.g., HPLC). If available, a rationale for using titration (e.g., practicality, field applicability) might be included.

Acknowledge and justify the simulated limit of 50 fruits per cultivar. Alternatively, explain explicitly that the bootstrapping method employed pooled or merged data, if applicable.

While coordinates are provided, climate conditions (such as temperature range, rainfall, and irrigation management) are not mentioned (Because postharvest quality is heavily impacted by preharvest climatic circumstances, a brief description of the local climate throughout the growth season or previous to harvest will add context).

Results

The findings are well described, well-organized, statistically sound, and given in a suitable and repeatable way. A competent researcher or reader can comprehend the findings, duplicate the analysis, and make valid conclusions.

 

Discussions

The findings are thoughtfully interpreted in relation to the context of published literature in the well-structured discussion section.

Lines 287-288: While ascorbic acid is an acid, titratable acidity (TA) in fruits is often dominated by organic acids such as citric and malic acid, rather than vitamin C.

more sour taste ➜ sourer taste.

Line 346: The term "minimal" is ambiguous unless validated by numerical comparisons. What is insignificant in one context may be significant in another.

Conclusions

The findings are in line with the data and arguments offered in the main section of the research.

Lines 398-399: A strong argument that may exaggerate the immediate economic usefulness in the absence of broader market research or cost-benefit statistics.

References

A large number of the references (more than 60%) date from the recent five years (2020-2025), which is suitable for a scientific investigation.

Efron (1979) and Hesterberg (2011) on bootstrap techniques are reliable statistical sources, regardless of age.

Strohecker and Henning (1967) is relatively ancient, however it might be incorporated in a standard technique for vitamin analysis.

Some of the authors (like Vilvert, Freitas, Souza, appear repeatedly (over 12 self-authored references). The high frequency may create questions regarding excessive self-citation.

Please, see also the comments in text. 

 

Comments for author File: Comments.pdf

Comments on the Quality of English Language

The general quality of the English language in the work is satisfactory and does not impede understanding.

Author Response

We would like to thank the reviewer for their careful reading and valuable comments, which have helped to improve the quality and clarity of our manuscript. Below, we provide point-by-point responses to each suggestion.

 

Comments 1: Line 2  - Possibly adding the “botanical name” (Malpighia emarginata DC.)

Response 1: Thank you for your suggestion. We have revised the title to include the botanical name Malpighia emarginata DC., as recommended.

 

Comments 2: The abstract is suitable for a scientific publication, instructive, and well-structured. It describes in detail the purpose of the study, the materials and procedures used, the main findings, and how the findings will be used in practice.

Response 2: We thank the reviewer for the positive evaluation of our Abstract. In accordance with Reviewer 1's suggestions, we made a few adjustments to improve clarity, while maintaining the overall structure and content.

 

Comments 3: Line 11: Acerola – botanical name

Line 11: In accordance to conventional language, vitamin C should at least once be referred to as ascorbic acid.

Response 3: The botanical name has been added to the Abstract. Additionally, vitamin C is now referred to as ascorbic acid both in the Abstract and upon its first mention in the main text, as recommended.

 

Comments 4: The introduction gives enough context for readers outside of the immediate field to comprehend the problem and hypotheses.

Response 4: We thank the reviewer for the positive feedback. Following Reviewer 1’s recommendations, only minor modifications were made to reorganize the Introduction section in order to improve the logical flow and coherence of the text, while maintaining the contextual information for readers outside the immediate field.

 

Comments 5: Lines 48-54: Indicate how your research differs or builds upon earlier OSS work.

Response 5: As now stated in the revised manuscript (see lines 48–54), earlier studies on sample size estimation in fruits such as apple, mango, and papaya [8–14] primarily employed a traditional frequentist approach, relying on simple dispersion metrics like the standard deviation to suggest a fixed sample size per trait or species.

In contrast, our study introduces a more advanced and statistically grounded methodology by applying nonlinear power models in combination with maximum curvature point (MCP) methods, which allow for an objective determination of the point of diminishing returns in sampling effort. Furthermore, rather than proposing a general OSS across all materials, our work provides cultivar-specific OSS estimates, which account for differences in within-genotype variability and better support experimental precision and efficiency in postharvest evaluations.

 

Comments 6: Line 42: "Recent" is a subjective and ambiguous term. A timeline or particular citation explaining the emergence of postharvest investigations would be useful.

Response 6: We have revised the manuscript to clarify the timeline by specifying that research on acerola quality has mainly emerged in the last decade (see lines 40–41).

 

Comments 7: Lines 53, 55 and 57: sd  -  capitalize abbreviation (SD) for standard formatting

Response 7: The abbreviation “sd” has been corrected to “SD” throughout the manuscript to ensure consistency with standard scientific formatting.

 

Comments 8: You may briefly describe the unique relevance of techniques like MCP to acerola.

Response 8: Thank you for your valuable suggestion regarding the inclusion of a brief description of the unique relevance of the MCP approach to acerola research. In response, we have revised the Introduction to highlight the applicability of MCP in postharvest studies of acerola, emphasizing the fruit’s high biological variability and the need to optimize sample size to balance statistical accuracy with practical constraints such as time, labor, and cost (see lines 65–72).

 

Comments 9: Lines 62-64: Is not clear if the bootstrap is used in conjunction with optimization techniques or error thresholds to indirectly influence sample size selections.

Response 9: Thank you for your insightful comment. We clarify that the bootstrap method is a non-parametric resampling technique that estimates the variability of sample statistics by repeatedly resampling the observed data. While it does not directly incorporate optimization techniques or explicit error thresholds to define sample size, it enables the assessment of the stability and precision of estimates, which can indirectly guide sample size decisions.

In contrast, the MCP method is applied to the bootstrapped data to explicitly identify a threshold sample size beyond which increasing the number of samples yields minimal gains in precision, thus serving as a more direct optimization approach.

We have revised the manuscript to better clarify these distinctions and the roles of each method in estimating optimal sample size (lines 58–64).

 

Comments 10: Your findings fit into the body of previous literature quite well. You've presented a strong argument for the necessity of your work, its connections to previous OSS studies on fruit crops, and the special significance of acerola for contemporary sampling techniques. The narrative will be considerably stronger with a few minor changes to increase clarity and emphasize importance.

Response 10: We sincerely thank the reviewer for the encouraging feedback regarding the relevance and contextualization of our findings within the existing literature. In response to your suggestion, we have made minor revisions throughout the Introduction section to enhance clarity and better emphasize the importance of our study, particularly with regard to its contributions to OSS-based sampling strategies in fruit crops and the specific case of acerola.

 

Comments 11: The methods are generally suitable, well-structured, and scientifically justified, even though some areas might benefit from more clarification or detail.

The examination of four cultivars, including experimental (BRS Rubra, Cabocla, Costa Rica) and commercial ('Junko') types, contributes to the study's secondary objective (cultivar comparison).

The experiment was carried out under field circumstances typical of commercial production, with adequate geographical and agronomic detail.

Response 11: Thank you for your positive evaluation of the methodology and for highlighting the relevance of the cultivar selection and field conditions. Some minor modifications were made to improve clarity.

 

Comments 12: While DFI titration is a traditional approach for vitamin C, it may be less precise than chromatographic methods (e.g., HPLC). If available, a rationale for using titration (e.g., practicality, field applicability) might be included.

Response 12: Thank you for your observation. We acknowledge that chromatographic methods, such as HPLC, offer greater specificity and sensitivity for vitamin C quantification. However, the DFI titration method remains widely accepted and routinely used in postharvest and food quality studies due to its practicality, low cost, and operational simplicity. It is particularly suitable for studies involving large sample sizes or conducted in field or semi-field conditions, where access to chromatographic equipment may be limited. Additionally, when properly standardized and performed under controlled conditions, DFI titration provides reliable and reproducible results, especially for comparative purposes, as in the present study. This information was added to the Material and Methods subsection about vitamin C analysis (see lines 144–149).

 

Comments 13: Acknowledge and justify the simulated limit of 50 fruits per cultivar. Alternatively, explain explicitly that the bootstrapping method employed pooled or merged data, if applicable.

Response 13: Thank you for your observation. The choice to simulate up to 50 fruits per cultivar was based on practical considerations. In postharvest research, it is uncommon to work with more than 50 fruits per treatment due to logistical constraints, including the limited availability of fruit material, increased labor, a higher processing time, and costs associated with sample handling and analysis. Therefore, the 50-fruit upper limit reflects a realistic scenario commonly encountered in experimental practice and ensures the results remain applicable and feasible for future studies. A brief excerpt has been added to the Material and Methods section (see lines 113–116).

 

Comments 14: While coordinates are provided, climate conditions (such as temperature range, rainfall, and irrigation management) are not mentioned (Because postharvest quality is heavily impacted by preharvest climatic circumstances, a brief description of the local climate throughout the growth season or previous to harvest will add context).

Response 14: Thank you for the important observation. We have now included a brief description of the climate conditions during the fruit development period in the Materials and Methods section (see lines 98–105). This information provides additional context on the preharvest conditions that may influence postharvest fruit quality. The data were obtained from a local automatic weather station and include temperature range, relative humidity, rainfall, and irrigation method. Daily climate data for the fruit development period are also provided in the Supplementary Material (Table S1).

 

Comments 15: The findings are well described, well-organized, statistically sound, and given in a suitable and repeatable way. A competent researcher or reader can comprehend the findings, duplicate the analysis, and make valid conclusions.

Response 15: We sincerely appreciate your positive evaluation of the Results section. We are glad to know that the organization, clarity, and reproducibility of our findings were considered adequate and scientifically sound. Thank you for your encouraging feedback.

 

Comments 16: Discussions - The findings are thoughtfully interpreted in relation to the context of published literature in the well-structured discussion section.

Response 16: We are pleased that the interpretation of our findings in the context of the existing literature was considered appropriate and well structured. Thank you for your encouraging remarks.

 

Comments 17: Lines 287-288: While ascorbic acid is an acid, titratable acidity (TA) in fruits is often dominated by organic acids such as citric and malic acid, rather than vitamin C.

Response 17: We thank the reviewer for the insightful observation. Indeed, titratable acidity (TA) in fruits is primarily attributed to organic acids such as citric and malic acids, while ascorbic acid typically contributes to a smaller fraction. However, previous studies have reported positive correlations between the ascorbic acid content and TA in acerola. We have revised the text to clarify this point and added appropriate references to support the observed relationship (see lines 294–296).

 

Comments 18: more sour taste ➜ sourer taste.

 Response 18: The expression “more sour taste” has been revised to “sourer taste” for grammatical accuracy.

 

Comments 19: Line 346: The term "minimal" is ambiguous unless validated by numerical comparisons. What is insignificant in one context may be significant in another.

Response 19: To address the ambiguity regarding the term “minimal,” we revised the text to clarify the comparison between methods. The sentence now reads:

“Both methods provided representative and reliable OSS estimates, with only small differences in the maximum CI_95% values. Therefore, although LRP provided slightly narrower confidence intervals, the gain in precision was relatively small, suggesting that either approach can be appropriately used for estimating the OSS in this context.” (see lines 352–356).

 

Comments 20: The findings are in line with the data and arguments offered in the main section of the research.

Response 20: We thank the reviewer for the positive feedback. We are glad that the conclusions were considered consistent with the data and arguments presented throughout the manuscript.

 

Comments 21: Lines 398-399: A strong argument that may exaggerate the immediate economic usefulness in the absence of broader market research or cost-benefit statistics.

Response 21: Thank you for the valuable feedback. We have revised the Conclusion and now highlight the methodological contribution and potential future applications, rather than suggesting immediate economic usefulness (see lines 433–435).

 

Comments 22: References – A large number of the references (more than 60%) date from the recent five years (2020-2025), which is suitable for a scientific investigation.

Efron (1979) and Hesterberg (2011) on bootstrap techniques are reliable statistical sources, regardless of age.

Response 22: We thank the reviewer for the positive feedback regarding the relevance and recency of the references cited. We also appreciate the recognition that key foundational works such as Efron (1979) and Hesterberg (2011) remain essential and reliable sources for bootstrap methodology, regardless of publication date.

 

Comments 23: Strohecker and Henning (1967) is relatively ancient, however it might be incorporated in a standard technique for vitamin analysis.

Response 23: Thank you for your observation. We have decided to replace the reference to Strohecker and Henning (1967) with the official AOAC method for vitamin C analysis, which is more widely accepted and up to date. This change ensures the methodology remains aligned with current international standards.

 

Comments 24: Some of the authors (like Vilvert, Freitas, Souza, appear repeatedly (over 12 self-authored references). The high frequency may create questions regarding excessive self-citation.

Response 24: We appreciate the reviewer’s observation. In response, we have reduced the number of self-authored references, retaining only those that are directly relevant to the objectives, methodology, or context of the present research [ref. 1,4,8,23,28,29]. These retained references provide the scientific basis for the development of our study, particularly in relation to acerola quality traits and sample size estimation approaches and were therefore considered essential for maintaining the rigor and coherence of the manuscript.

 

English editing: In accordance with the reviewers’ recommendations, the English language of the manuscript has been thoroughly revised to improve clarity and readability. A certificate of professional English editing is attached.

 

Thank you once again for your constructive feedback and support in improving the quality of this work!

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The author has revised the manuscript based on the comments.

Comments on the Quality of English Language

None