Analysis of a Commercial Red Wine Fermentation Dataset with a Wine Kinetic Model

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

General Comments

The main objective of the paper titled: Analysis of a Commercial Red Wine Fermentation Dataset with 2 a Wine Kinetic Model, according to the authors, is the robustness of the Boulton model is tested using the largest known data set of commercial red wine fermentations.

 

In my opinion, the aim of this work is acceptable, but it is necessary  revise  some small details according to the upcoming comments.

·       On page 2, line 97 the authors write: Figure 2 also shows the initial, change by Figure 3. The lines 124-125 on page 4 no would be necessary, because the Figure 3 is already presented.

·       On page 12, lines 415-416, the references 24 and 25 are not well indicated. Check.

·       In Figures, the red dots can be changed to another symbol (triangle, square), because if the paper is printed in black and white, the reader can differentiate the dots.

In summary, I think the paper is an excellent work, and interesting for the winemakers.

Author Response

Comments 1: On page 2, line 97 the authors write: Figure 2 also shows the initial, change by Figure 3. The lines 124-125 on page 4 no would be necessary, because the Figure 3 is already presented.

Response 1: Thank you for pointing this out.  The correct figure reference has been updated and the duplicate description of figure 3 has been removed.

Comments 2: On page 12, lines 415-416, the references 24 and 25 are not well indicated. Check.

Response 2: Thank you for your comment. We have updated these references for completeness.

Comments 3: In Figures, the red dots can be changed to another symbol (triangle, square), because if the paper is printed in black and white, the reader can differentiate the dots.

Response 3: Thank you for your suggestion. This paper is part of a series of papers in a special issue where we have utilized color in multiple figures.  For consistency in formatting across these papers, we prefer to keep the figure as is.

Reviewer 2 Report

Comments and Suggestions for Authors

his paper presents a valuable contribution to the understanding of wine fermentation kinetics using a kinetic model and a substantial dataset.
Here are some comments to improve the paper:
 
-The introduction could be enhanced by clarifying the novel contributions of this study compared to prior research. While prior studies on prediction models are mentioned, a stronger emphasis on the uniqueness of this dataset and methodology would reinforce the study’s originality.

-The paper lacks specific details on the statistical assumptions and conditions checked before applying MANOVA and CVA. 

-It would be valuable to discuss the practical implications of findings more directly, specifically how wineries might adjust their practices based on the identified lack of relationship between initial juice chemistry and fermentation completion. Moreover, the study could explore how this model could be applied or validated in different fermentation conditions (e.g., smaller batch sizes or different grape varieties) to ensure broader applicability.

Author Response

Comments 1: The introduction could be enhanced by clarifying the novel contributions of this study compared to prior research. While prior studies on prediction models are mentioned, a stronger emphasis on the uniqueness of this dataset and methodology would reinforce the study’s originality.

Response 1: Thank you for your comment. We have added in the introduction, “To our knowledge, this is the largest set of commercial fermentations evaluated with a fermentation model. Previous studies of wine fermentation models have been limited to bench-scale fermentations or small sets of commercial fermentations utilizing atypical measurements, mainly total sugar.”. This emphasizes both the uniqueness of the dataset and the study’s originality on analyzing this dataset with a fermentation model.

Comments 2: The paper lacks specific details on the statistical assumptions and conditions checked before applying MANOVA and CVA. 

Response 2: Thank you for your comment.  We believe this is regarding the selection of groups based on “days to zero Brix” (i.e. <4 days, 4 to 7 day, 7 to 10 days, and >10 days).  These groups were not selected based on statistical analysis but selected based on practical winemaking at commercial scale.  A fermentation that reaches zero Brix before 4 days has extremely rapid fermentation kinetics, while a fermentation greater than 10 days has the potential to stop or become ‘sluggish’ while finishing alcoholic fermentation.  The later fermentation can become a ‘problem’ for winemakers making a sugar ‘dry’ wine, and thus leading to quality down grading.

Comments 3: It would be valuable to discuss the practical implications of findings more directly, specifically how wineries might adjust their practices based on the identified lack of relationship between initial juice chemistry and fermentation completion. Moreover, the study could explore how this model could be applied or validated in different fermentation conditions (e.g., smaller batch sizes or different grape varieties) to ensure broader applicability.

Response 3: Thank you for your comment.  This paper is submitted as part of series of papers in a special issue that describes the use of sensors and modeling in commercial winemaking.  The conclusion of this paper is the emphasis that a large dataset of previous fermentations is less useful in predicting the outcome of the current fermentation than measuring and modeling in real time.  Lastly, the model is independent of volume or grape variety, though we do go into detail of how the model could be adapted or improved for various winemaking conditions elsewhere: Nelson, J.; Boulton, R. Models for Wine Fermentation and Their Suitability for Commercial Applications. Fermentation 2024, 10, 269. https://doi.org/10.3390/fermentation10060269