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Thank you for your careful revisions.
[# PeerJ Staff Note - this decision was reviewed and approved by Valeria Souza, a PeerJ Section Editor covering this Section #]
While Reviewer 1 did not have any specific comments to be addressed, Reviewer 2 raised several issues. Please revise your manuscript accordingly.
I would like to emphasize one point where I disagree with Reviewer 2, the reviewer's additional comments. I don't think you should omit equations just because they are well known. Providing well known equations can help refresh the reader's mind and also serves as a double-check to make sure everything was done correctly. As a case in point, is your equation for "Accuracy" correct? Shouldn't the denominator be TP + TN + FP + FN? Please make sure you have this correct, and also make sure you didn't accidentally use the wrong formula in your code.
**PeerJ Staff Note:** Please ensure that all review, editorial, and staff comments are addressed in a response letter and any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
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I have had the opportunity to thoroughly evaluate your article titled "Classification and Prediction of Klebsiella pneumoniae Strains with Different MLST Allelic Profiles via SERS Spectral Analysis," and I commend you for conducting a valuable study that explores an intriguing approach to bacterial strain classification.
I have had the privilege of reviewing the manuscript titled " Classification and prediction of Klebsiella pneumoniae strains with different MLST allelic profiles via SERS spectral analysis". I am pleased to share my assessment of the manuscript and provide some recommendations for its potential improvement.
Overall, I find the manuscript to be a valuable contribution. The authors have presented their research in a clear and coherent manner. The experimental setup and methodologies are adequately described, making it feasible for others to replicate the study. The manuscript's emphasis on utilizing Surface-Enhanced Raman Spectroscopy (SERS) and machine learning for detecting and classifying Klebsiella pneumoniae of is notable and demonstrates the authors' understanding of the subject matter.
Incorporation of Related Studies: I recommend that the authors include discussions on two relevant studies: Ciloglu et al.'s research on fast detection of colistin-resistant Klebsiella pneumoniae using SERS with machine learning-based feature extraction (Analytica Chimica Acta, 2022) and Lyu et al.'s work on deep learning analysis of SERS spectra for predicting multidrug-resistant Klebsiella pneumoniae (Microbiology Spectrum, 2023).
Referencing Original Technique Studies: On line 65-67, I suggest that the authors refer to the original studies that introduced the technique being discussed. This will provide readers with a more comprehensive understanding of the methodology's background.
Detailed Information on AgNP Substrate: The manuscript should provide additional information about the AgNP substrate used in the study. It is essential to clarify whether the AgNPs were synthesized internally or obtained from commercial sources. Furthermore, including details about the dimensions and morphology of the nanoparticles will enhance the experimental framework's clarity.
Correction of Terminology: On line 209, the authors should replace "PCA1 and PCA2" with "PC1 and PC2" for consistency and accuracy. In addition, at the last sentence of the conclusion, the combination of machine learning and SERS should be mentioned.
Machine Learning Algorithm Parameters: The manuscript should explicitly mention the parameters employed for the machine learning algorithms. This information is crucial for readers seeking to replicate the study or understand the results in greater depth.
Conciseness in Presentation: While the manuscript is well-written and comprehensible, I suggest that the authors consider omitting statements and equations that might be redundant or overly familiar to readers well-versed in the topic. This will streamline the presentation and enhance the manuscript's focus on novel contributions.
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