Classification of glucose-level in deionized water using machine learning models and data pre-processing technique

Tri Ngo Quang; Tung Nguyen Thanh; Duc Le Anh; Huong Pham Thi Viet; Doanh Sai Cong

doi:10.1371/journal.pone.0311482

Peer Review History

Original SubmissionJuly 7, 2023
15 Jul 2024 Decision Letter - Hongju He, Editor PONE-D-23-21128Classification of glucose-level in deionized water using machine learning models and data pre-processing techniquePLOS ONE Dear Dr. nguyen thanh, 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. Please submit your revised manuscript by Aug 29 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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Please remove them and upload them with the file type 'Supporting Information'. Please ensure that each Supporting Information file has a legend listed in the manuscript after the references list. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: No Reviewer #2: Yes Reviewer #3: Partly ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: No Reviewer #2: Yes Reviewer #3: No ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: No Reviewer #2: No Reviewer #3: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: No Reviewer #2: No Reviewer #3: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: This paper titled "Classification of glucose-level in deionized water using machine learning models and data pre-processing technique" is lack of innovation and badly written. The structure of this article is chaotic, far from the requirements of this journal. Therefore, I suggest the rejection of this paper. Reviewer #2: The authors described the classification of glucose-level in deionized water using machine learning models coupled with Raman spectroscopy. Before publication can be considered, the authors need to address the following issues: 1. Authors should specify and comment (in abstract) on their main findings 2. The authors indicated measuring the analyte at 10 different concentrations but do not mention how many times they measure each concentration, especially the best peak. 3. How about signal reproducibility of the proposed method? 4. The authors should highlight the limitations and strengths of their proposed method 5. Change Fig 1a to Fig 2a and Fig 1b to Fig 2b 6. The authors used lots of abbreviations that should be properly defined in the text Reviewer #3: 1. No reference in section 1 2. Comparison with the state-of-the-art is missing 3. Future direction, limitations and threats to validity is missing 4. Data collection part is very impressive , congratulations 5. Last and the most major concern is the lack of novelty in the work, only already available methods are implemented in the work . Please keep in mind that in current state the work cannot be published, you have to make significant improvements in the methodology and experimentation required for the publication in this highly reputed SCI indexed journal. I hope you understand my concern and I expect you will overcome this issue by incorporating the novelty in the methodology. Best Regards ****** 6. PLOS authors have the option to publish the peer review history of their article (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. 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Attachments Attachment Submitted filename: PLOS ONE REVIEW - Classification of glucose-level in deionized water using machine learning models and.docx Attachment Submitted filename: Classification of glucose.docx https://doi.org/10.1371/journal.pone.0311482.r001
Revision 1
23 Aug 2024 Author Response ANSWER FILE Reviewer 1: Classification of glucose-level in deionized water using machine learning models and data pre-processing technique The overall quality of the paper is poor. This manuscript was not well written and suggest a rejection of the manuscript. The line in the whole document is not numbered 1. I suggest the authors get editing help from someone with full professional proficiency in English. There are improper or ambiguous English usages. The writing of the paper needs to be polished. 2. Abstract is not well written. The abstract is rewritten with better structure. 3. The introduction section is not well organized. The introduction is rewritten with better structure 4. Methodology is poorly written. We restructure the methodology section and divide into 3 sections including Related Work, Related works and description of methodology, Introduction to the dataset, Data preprocessing methods in proposal. Each section focuses on a specific tasks related to our proposal. Reviewer 2: The authors described the classification of glucose-level in deionized water using machine learning models coupled with Raman spectroscopy. Before publication can be considered, the authors need to address the following issues: 1. Authors should specify and comment (in abstract) on their main findings. The abstract is rewritten with better structure. We specify the results about improvement of machine learning. 2. The authors indicated measuring the analyte at 10 different concentrations but do not mention how many times they measure each concentration, especially the best peak. We collected 5 Raman sequences for each glucose fluid with specific concentration. With 10 different concentrations, we collected 50 sequences. 3. How about signal reproducibility of the proposed method? Both the hotspot series extraction procedure and the VRA have the ability to restore the data to its original state because both involve operations that result in data loss. The hotspot series extraction procedure removes Raman sequences that are deemed non-characteristic of the sample, while the VRA transforms the data sequence by trimming extreme points to smooth the Raman sequence. This argument is added in part 3.2 of rewritten paper. 4. The authors should highlight the limitations and strengths of their proposed method We add the advantage and disadvantage of our 2 proposals for pre-processing data including hotspot series extraction procedure and Vancouver Raman Algorithm in Part 3.1.4 and 3.2. In categories of machine learning, we use existing algorithms without modification so we focus on how these algorithms handle Raman data instead of their cons and pros. 5. Change Fig 1a to Fig 2a and Fig 1b to Fig 2b We changed Fig1a to Fig 2a and Fig 1b to Fig 2b. 6. The authors used lots of abbreviations that should be properly defined in the text We rewrite clearly an abbreviation ML by full word “Machine Learning”, with abbreviation VGA, we correct it to VRA as explanation of Vancouver Raman Algorithm. Reviewer 3: 1. No reference in section 1 The section 1 is introduction, in this section, we concentrate the general ideal and process of implementation for our research so the number of research is limited. In rewritten introduction, several references about the problem defining are added. 2. Comparison with the state-of-the-art is missing. We have several comparisons with the state-of-the-art technologies such as Convolutional Neural Networks (CNNs) or Long Short-Term Memory (LSTM) networks. We define the advantage and disadvantage of these deep learning model and, thus, explain the reason to not use this model. This argument is added in section 2.3 of rewritten paper. 3. Future direction, limitations and threats to validity is missing We add drawbacks of our research as well as the future development in Section 5: Conclusion 4. Data collection part is very impressive, congratulations 5. Last and the most major concern is the lack of novelty in the work, only already available methods are implemented in the work. Please keep in mind that in current state the work cannot be published, you have to make significant improvements in the methodology and experimentation required for the publication in this highly reputed SCI indexed journal. I hope you understand my concern and I expect you will overcome this issue by incorporating the novelty in the methodology. We define that the novelty of our research is creation of dataset by unique research strategy as well as the analyzation of our dataset for proposing new data pre-processing method. We describe it in section 2.3. Attachments Attachment Submitted filename: Answer File_PLOS ONE (1).docx https://doi.org/10.1371/journal.pone.0311482.r002
10 Sep 2024 Decision Letter - Hongju He, Editor Classification of glucose-level in deionized water using machine learning models and data pre-processing technique PONE-D-23-21128R1 Dear Dr. nguyen thanh, 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. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. 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If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #2: All comments have been addressed Reviewer #3: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #2: Yes Reviewer #3: Partly ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #2: Yes Reviewer #3: I Don't Know ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #2: No Reviewer #3: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #2: Yes Reviewer #3: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #2: (No Response) Reviewer #3: Almost all major issues have been resolved hence I think that this revised version can be accepted now . ****** 7. PLOS authors have the option to publish the peer review history of their article (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 #2: No Reviewer #3: No ******** Attachments Attachment Submitted filename: PONE-D-23-21128R1-.docx https://doi.org/10.1371/journal.pone.0311482.r003
Formally Accepted
8 Oct 2024 Acceptance Letter - Hongju He, Editor PONE-D-23-21128R1 PLOS ONE Dear Dr. nguyen thanh, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Hongju He Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0311482.r004

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