Dear Dr. Utshob,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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ACADEMIC EDITOR:

In light of the reviewers’ comments, the manuscript is invited for a major revision. The authors are requested to review the comments carefully and submit a revised manuscript with appropriate responses.

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Academic Editor

PLOS One

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Partly

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: N/A

Reviewer #3: Yes

Reviewer #4: N/A

Reviewer #5: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: No

Reviewer #4: Yes

Reviewer #5: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: No

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Reviewer #1: No novelty in the present state of the manuscript.

Authors have done work on the titlte "Hybrid Machine Learning Driven Optimization of Multilayer SPR Sensor for High Sensitivity Milk Fat Detection".

There are several papers on the similar topic.

Reviewer #2: Reviewer’s Comments

Manuscript Title: Hybrid Machine Learning Driven Optimization of Multilayer SPR Sensor for High Sensitivity Milk Fat Detection

Recommendation: Major Revision

The manuscript combines SPR sensing with machine learning for fat/milk analysis, which is an interesting direction. However, in its current form, the work needs significant clarification and strengthening in terms of methodology, validation, and practical relevance.

1. The idea of combining SPR with machine learning is promising, but the novelty is not clearly established. Similar approaches using ML for optical sensing and classification have already been reported. The authors should clearly highlight what is new in their framework (e.g., dataset, model, feature extraction, or sensing strategy).

2. The sensing mechanism itself is not sufficiently explained. It is unclear how variations in milk composition (fat content or adulteration) are translated into refractive index changes and how reliably SPR captures these variations.

3. A major concern is the source of refractive index values used for milk or fat variations. The manuscript does not clearly mention whether these values are taken from experimental data, literature, or assumed. Proper references or justification are required.

4. The manuscript does not address whether similar refractive index variations could arise from other components present in milk (e.g., proteins, lactose, added water, or other adulterants). This raises concerns regarding selectivity and specificity.

5. The role of machine learning is not deeply justified. It is not clear, What exact features are used (SPR angle, reflectance curve, etc.), also Why ML is necessary instead of direct analytical modelling and further, Whether the dataset size is sufficient for reliable training

6. There is no clear discussion of dataset preparation such as How many samples were used, Whether the data is simulated or experimental, How the data is split (training/testing)

7. How sensitive is the model to noise?

8. Will small measurement errors affect classification?

9. From the SPR side, the analysis is somewhat limited. The paper would benefit if authors mention the Clear resonance curve analysis, Shift explanation with changing refractive index and Physical interpretation of sensing behavior.

10. The practical applicability is not fully convincing. It is unclear how the proposed system would work in real milk samples where multiple variables change simultaneously.

11. Manuscript requires a comaparision table also in this field with the previous relevant published data.

Reviewer #3: I am uploading my detailed review as an attachment, as it exceeds the character limit of this section.

The manuscript describes a technically sound piece of scientific research, and the data presented adequately support the conclusions drawn by the authors. However, it requires further revisions and clarifications in certain sections to improve its overall quality and rigor. Detailed comments and specific suggestions are provided in the attached review document.

Reviewer #4: The manuscript deals with the numerical analysis of the surface plasmon resonance (SPR) sensor based on the Kretschmann configuration for the measurement of fat content in milk. The authors of the contribution form a team specializing in the design and optimization of highly sensitive SPR biosensors, particularly utilizing multilayer configurations for medical diagnostics.

It's worth taking note of the issue of selecting the right prism material for measuring milk's fat content, which was ultimately determined to be SiO2. Also, a clear explanation of the mechanism of operation of the SPR sensors would be a good read.

On the other hand, I am concerned about the assumption in the simulations that the thickness of some layers changes by 0.1 nm, which corresponds to the size of an atom or smaller, and therefore is probably not wholly justified.

It is also not entirely clear to me how the calculated sensitivity of the sensor translates into the sensitivity of measuring the fat content in milk.

Finally, the authors could add a few review publications on the measurement of fat in milk and in general of its adulteration [1-3], and above all, refer to publications dealing with exactly the same problem, i.e. the measurement of fat content in milk using SPR sensors. I am referring to the publications listed in the list below [4-7].

It's happened to me a few times that authors describe sensors in Kretschmann configuration and treat them as common knowledge, without citing their author [8]. It's crucial to give credit to the author and include him in the references.

In conclusion, I would be in favor of publishing this work if the authors could respond convincingly to the above comments.

References:

1. D Maheswara Reddy, K Venkatesh, and C Venkata Sesha Reddy: “Adulteration of Milk and Its Detection: A Review,” Int. J. Chem. Stud. 5, No. 4, (2017), 613–617.

2. Siuli Das, Bhaswati Goswami, and Karabi Biswas: “Milk Adulteration and Detection: A Review,” Sens. Lett. 14, No. 1, (2016), 4–18.

3. Sneh Lata Chauhan, Priyanka, Kruti Debnath Mandal, Babul Rudra Paul, and Chinmoy Maji: “Adulteration of milk: A Review,” Int. J. Chem. Stud. 7, No. 1, (2019), 2055–2057.

4. Abdulkarem H. M.Almawgani, Malek G. Daher, Sofyan A. Taya, Mohammad Mashagbeh, and Ilhami Colak: “Optical detection of fat concentration in milk using MXene-based surface plasmon resonance structure,” Biosensors 12, No. 7, (2022), 535.

5. Bhishma Karki, Youssef Trabelsi, Arun Uniyal, Amrindra Pal, and Ram Bharos Yadav: “Detection of fat concentration milk using TMDC-based surface plasmon resonance sensor,” Mod. Phys. Lett. B 38, No. 29, (2024), 2450253.

6. Leila Fatolahi, Tamara Shamil Addulrahman, Sepideh Alemi, Mohammed N. Al-Delfi, Ayat H. Athab, and Baadal Jushi Janani: “Optical detection of fat and adulterants concentration milk using TMDC (WS2 and MoS2)-surface plasmon resonance sensor via high sensitivity and detection accuracy,” Opt. Mater. 147, (2024), 114723.

7. Emranul Haque, Abdullah Al Noman, S. M. Rakibul Islam, Kh Shahriya Zaman, and Feroz Ahmed: “Numerical Analysis of a PCF-SPR Sensor for Adulterant Detection in Milk,” 2025 Photonics Global Conference (PGC), Sanya, China, 2025, pp. 1-3.

8. E. Kretschmann and H. Raether, "Notizen, "Radiative decay of non-radiative surface plasmons excited by light," Zeitschrift Naturforschung A 23, 2135-2136, (1968).

Reviewer #5: Comments

Manuscript Title: Hybrid Machine Learning Driven Optimization of Multilayer SPR Sensor for High Sensitivity Milk Fat Detection

Recommendation: Major Revision

The manuscript presents a multilayer SPR biosensor based on SiO₂/MgO/Ag/BaTiO₃/BP configuration for milk fat detection using refractive index variation. The authors employ TMM, FEM, and FDTD analyses along with hybrid optimization involving brute-force and machine learning-assisted refinement. The topic is relevant to optical biosensing and food-quality monitoring, and the manuscript demonstrates promising sensor sensitivity and comprehensive numerical analysis. However, the manuscript in its current form requires substantial revision before it can be considered for publication.

1. How does the proposed hybrid ML optimization differ from conventional parameter sweeps? What scientific advancement beyond incremental sensitivity enhancement is achieved?

2. The title emphasizes “Hybrid Machine Learning Driven Optimization,” yet the ML methodology lacks sufficient technical detail such as: Type of ML model used, Input features and output targets, Training/testing dataset size, Data splitting strategy, Hyperparameters, Validation metrics (RMSE, MAE, R², etc.), Comparison with conventional optimization methods and Computational efficiency gains.

3. The manuscript assumes RI variation from 1.345 to 1.3621 corresponding to milk fat concentration. The authors should: provide experimental references correlating RI with fat percentage, clarify whether temperature dependence is considered, discuss compositional variability in real milk samples

4. Explain whether adulterants or proteins affect RI measurements.

5. Fig. 11 captions appear inconsistent:

“The RI of the corresponding fluoride concentration...”

This is likely an editing error because the manuscript concerns milk fat detection, not fluoride concentration. All figure captions should be revised carefully.

6. Several grammatical and stylistic issues are present throughout the manuscript. Professional English editing throughout the manuscript is highly recommended.

7. Repetitive sentence structures are observed, such as:

“This is because...” appears repeatedly in the Introduction.

8. Some references appear duplicated: References [15] and [16] seem identical.

9. Typographical issues in many instances such as:

“Polinomial Fit” should be “Polynomial Fit.”

10. The introduction discussion is poor and insufficiently structured. The literature review lacks critical analysis of recent advancements in multilayer SPR biosensors, particularly those involving hybrid dielectric materials, 2D materials, and machine learning-assisted optimization techniques. The authors are strongly advised to enrich the Introduction section using:doi.org/10.1007/s11468-025-02961-1, 10.1016/j.microc.2026.117902, doi: 10.1109/TNB.2026.3664930, doi.org/10.1007/s11468-025-02823-w.

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what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: Yes:  Dr. Chella Santhosh

Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

Reviewer #5: No

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Attachments

Attachment

Submitted filename: review_report_PONE-D-26-15273.pdf

Hybrid Machine Learning Driven Optimization of Multilayer SPR Sensor for High Sensitivity Milk Fat Detection

PONE-D-26-15273R1

Dear Dr. Utshob,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Zaky A. Zaky, Ph.D.

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #4: All comments have been addressed

Reviewer #5: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: N/A

Reviewer #5: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

Reviewer #5: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

Reviewer #5: Yes

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Reviewer #1: The response given by the authors are satisfactory and can go for the accpetance of the publication.

Reviewer #2: Authors have incorporated all the comments and suggestions in the revised manuscript. on the basis of that, the manuscript can be accepted for publication.

Reviewer #4: First and foremost, it should be appreciated that the authors put a lot of effort into preparing a revised version of their manuscript. Honestly, this is the first time I have encountered a situation where a manuscript after corrections and responses to reviewers' comments is almost four times larger than its first version.

My comments were fully considered by the authors, and I no longer have any reservations about the content of the new version of the paper. Some overly far-reaching statements have been corrected or limited. I am thinking here in particular of the authors' clear statement in response to the second reviewer that “… the revised manuscript now clearly indicates that this proposed sensor can currently be considered as a refractive index sensing system rather than a specific milk fat biosensor” and to me “It is important to note that the suggested SPR sensor does not detect the concentration of milk fat, instead it detects changes in the effective refractive index (RI) of the milk sample”.

Concluding, in my opinion the manuscript in its current form can be accepted for publication in PLOS One.

Reviewer #5: (No Response)

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what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review?  For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: Yes:  Dr. Chella Santhosh

Reviewer #2: No

Reviewer #4: No

Reviewer #5: No

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