Multivariate method for prediction of fumonisins B1 and B2 and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR)

Denize Tyska; Adriano Olnei Mallmann; Juliano Kobs Vidal; Carlos Alberto Araújo de Almeida; Luciane Tourem Gressler; Carlos Augusto Mallmann

doi:10.1371/journal.pone.0244957

Peer Review History

Original SubmissionFebruary 26, 2020
22 Apr 2020 Decision Letter - Kris Audenaert, Editor PONE-D-20-04995 Multivariate method for prediction of fumonisins B1 and B2 in maize using Near Infrared Spectroscopy (NIR) PLOS ONE Dear authors 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. A major concern from one of the reviewers is the novelty of the work and I have to agree with the reviewer. Nevertheless, we want to give you the opportunity to clarify more specifically what is new in your data compared to data published by other research groups in the past. In addition, also take into account the comments raised by reviewer 1 on the clarity of the discussion We would appreciate receiving your revised manuscript by 21/05/2020. When you are 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. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. 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The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf 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: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 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: Yes Reviewer #2: 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: 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: The paper describes the application of the Near Infrared Spectroscopy (NIR) to quantify the level of contamination of total fumonisins in ground maize, and its comparison with well assessed methods such as Liquid Chromatography Coupled Tandem Mass Spectrometry (LC-MS/MS), to verify the suitability of NIR to rapidly analyze a large numbers of maize samples for FBs contamination. The paper describes experiments well carried out and the conclusions are correct, showing the reliability of this new methods. However, the paper needs to be deeply revised for the English, that often is very difficult to understand. Also, the way to present data needs to be revised: . I would separate completely Results and Discussion sections in order to have more clear both parts, that in this version are extremely hard to follow. Finally, minor revisions are reported below. ABSTRACT Line 27: the mycotoxins are not phytopathogens. The fungi that produce them are phytopathogens, not the metabolites produced. Eventually, they can be defined phytotoxic compunds. Line28: Fusarium moniliforme is not acceptable at all as definition. It is an old nomenclature. Along the whole paper F. moniliforme must be replaced with F. verticillioides INTRODUCTION Line 47: Alternaria don’t produce fumonisins B Line48: Fusarium moniliforme is not acceptable at all as definition. It is an old nomenclature. Along the whole paper F. moniliforme must be replaced with F. verticillioides Line 50: the mycotoxins are not phytopathogens. The fungi that produce them are phytopathogens, not the metabolites produced. Eventually, they can be defined phytotoxic compunds. Line 84: replace the word corn with the word maize and replace it along the paper REFERENCE Are the references below reported correctly? Line 283: 1. RHEEDER J. Fusarium moniliforme and Fumonisins in Corn in Relation to Human Esophageal Cancer in Transkei. 1992; Line 285 2. BACON CW, NELSON PE. Fumonisin Production in Corn by Toxigenic Strains of 286 Fusarium moniliforme and Fusarium proliferatum. J Food Prot [Internet]. 1994;57(6):514–21. Available from: https://doi.org/10.4315/0362-028X-57.6.514 Line 288 3. JACKSON, L.; JABLONSKI J. Fumonisins. In: MAGAN, N.; OLSEN M (Org. ., editor. Mycotoxins in food. Cambridge: Woodhead Publishing; 2004. p. 367–91. Reviewer #2: The manuscript is written very clearly and appropriate methods are used to come to the results. However, it has one major drawback: The manuscript does not present any new information. If I compare the manuscript to that of e.g. Gaspardo et al. (2012, https://doi.org/10.1016/j.foodchem.2012.06.078). A similar method is used and I see exactly the same graphs and the descriptions are even very similar E.g. In Gaspardo et al. (2012) it was written: The data set was organised in a matrix with 143 rows and 926 + 1 columns, combining spectral and chemical data for each sample. In the current manuscript, it is written: The calibration set was arranged in a matrix with 236 rows and 4200 + 1 columns, combining spectral and chemical data for each sample. I can understand that this is indeed the way to deal with this information. But, the main issue is that I cannot really find new findings in this manuscript. You use the same methods for the same toxins in the same matrix. In addition, in 2018, Levasseur-Garcia, presented a clear review on this topic summarizing the toxins and matrices that could be measured with this method (doi: 10.3390/toxins10010038). In addition, the model presented in PONE-D-20-04995 seems to fit the data quiet well, but it is never mentioned which wavelengths or regions in the spectrum are important for toxin quantification. I suggested a major revision, since I would like to give the authors the chance to significantly improve their work. Now it is merely a copy of what has been done already a lot of times. If they can include e.g. new toxins in a new matrix, or detect mixtures of toxins… or if they can compare several other machine learning technique or new deep learning methods, so that the manuscript had some added value compared to what has been published in the past, I am willing to review this manuscript. ****** 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. Reviewer #1: No Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step. Attachments Attachment Submitted filename: Review_PONE-D-20-04995.docx https://doi.org/10.1371/journal.pone.0244957.r001
Revision 1
20 May 2020 Author Response Reviewer #1: The paper describes the application of the Near Infrared Spectroscopy (NIR) to quantify the level of contamination of total fumonisins in ground maize, and its comparison with well assessed methods such as Liquid Chromatography Coupled Tandem Mass Spectrometry (LC-MS/MS), to verify the suitability of NIR to rapidly analyze a large numbers of maize samples for FBs contamination. The paper describes experiments well carried out and the conclusions are correct, showing the reliability of this new methods. However, the paper needs to be deeply revised for the English, that often is very difficult to understand. Also, the way to present data needs to be revised: . I would separate completely Results and Discussion sections in order to have more clear both parts, that in this version are extremely hard to follow. Finally, minor revisions are reported below. The manuscript was thoroughly revised following the reviewer's suggestions; furthermore, results and discussion are presented separately. ABSTRACT Line 27: the mycotoxins are not phytopathogens. The fungi that produce them are phytopathogens, not the metabolites produced. Eventually, they can be defined phytotoxic compunds. This information was removed from the manuscript. Line28: Fusarium moniliforme is not acceptable at all as definition. It is an old nomenclature. Along the whole paper F. moniliforme must be replaced with F. verticillioides This information was removed from the abstract due to the changes made in the manuscript. INTRODUCTION Line 47: Alternaria don’t produce fumonisins B This information was removed from the manuscript. Line48: Fusarium moniliforme is not acceptable at all as definition. It is an old nomenclature. Along the whole paper F.moniliforme must be replaced with F. verticillioides Fusarium moniliforme has been replaced by Fusarium verticillioides in line 55 and throughout the text. Line 50: the mycotoxins are not phytopathogens. The fungi that produce them are phytopathogens, not the metabolites produced. Eventually, they can be defined phytotoxic compunds. This information was removed from the manuscript. Line 84: replace the word corn with the word maize and replace it along the paper. The word corn was replaced by maize throughout the manuscript. REFERENCE Are the references below reported correctly? Line 283: 1. RHEEDER J. Fusarium moniliforme and Fumonisins in Corn in Relation to Human Esophageal Cancer in Transkei. 1992; This reference has been adjusted (line 417). Line 285 2. BACON CW, NELSON PE. Fumonisin Production in Corn by Toxigenic Strains of 286 Fusarium moniliforme and Fusarium proliferatum. J Food Prot [Internet]. 1994;57(6):514–21. Available from: https://doi.org/10.4315/0362-028X- 57.6.514 This reference has been adjusted (line 420). Line 288 3. JACKSON, L.; JABLONSKI J. Fumonisins. In: MAGAN, N.; OLSEN M (Org. ., editor. Mycotoxins in food. Cambridge: Woodhead Publishing; 2004. p. 367–91. This reference has been adjusted (line 467). --------------------- Reviewer #2: The manuscript is written very clearly and appropriate methods are used to come to the results. However, it has one major drawback: The manuscript does not present any new information. If I compare the manuscript to that of e.g. Gaspardo et al. (2012, https://doi.org/10.1016/j.foodchem.2012.06.078). A similar method is used and I see exactly the same graphs and the descriptions are even very similar E.g. In Gaspardo et al. (2012) it was written: The data set was organised in a matrix with 143 rows and 926 + 1 columns, combining spectral and chemical data for each sample. In the current manuscript, it is written: The calibration set was arranged in a matrix with 236 rows and 4200 + 1 columns, combining spectral and chemical data for each sample. I can understand that this is indeed the way to deal with this information. But, the main issue is that I cannot really find new findings in this manuscript. You use the same methods for the same toxins in the same matrix. In addition, in 2018, Levasseur-Garcia, presented a clear review on this topic summarizing the toxins and matrices that could be measured with this method (doi: 10.3390/toxins10010038). In addition, the model presented in PONE-D-20-04995 seems to fit the data quiet well, but it is never mentioned which wavelengths or regions in the spectrum are important for toxin quantification. I suggested a major revision, since I would like to give the authors the chance to significantly improve their work. Now it is merely a copy of what has been done already a lot of times. If they can include e.g. new toxins in a new matrix, or detect mixtures of toxins… or if they can compare several other machine learning technique or new deep learning methods, so that the manuscript had some added value compared to what has been published in the past, I am willing to review this manuscript. We are very grateful for the suggestions given to improve the manuscript. We read all the considerations and understood that the paper needed some changes. Indeed, there are other studies using NIR to predict FBs in maize. However, they were carried out in different countries, with positivity and concentrations that may vary from one region to another. In Brazil, positivity of FBs in maize varies from 90 to 100%, and its inclusion in animal diet is quite high. Furthermore, in the poorest regions of the country, maize is the basis of daily meals, being widely consumed by the population. The use of quick tools, such as NIR, speeds up decision making by the Industry. So, we understand that this study is valid and important from an epidemiological point of view. We followed your suggestion and included the assessment of another very important mycotoxin: zearalenone. This toxin has been little explored in maize using the NIR technology. Besides, it causes much damage to the reproductive system of farm animals, with significant impacts on production. There are also studies indicating changes in children, associating early puberty with maize-based food contaminated with zearalenone. As I mentioned above, there is a great human consumption of maize-based by-products. It should also be highlighted that this is the first scientific examination carried out in Brazil using the NIR methodology to predict fumonisins and zearalenone in maize. We used samples from several Brazilian states to compose the database. Many studies are aimed at countries with different climates and frequencies of positivity. Regarding the investigation of spectral bands, as mentioned in the manuscript, this information remains to be clarified, so we used the entire spectrum. However, work approaching this matter is under development by our research group and it will be promptly published. Specific comments L27: Fumonisins (FBs) are mycotoxins which are major phytopathogens => Fumonisins are no pathogens they are produced by phytopathogens. This information was removed from the manuscript. L28: produced mainly by Fusarium proliferatum and Fusarium moliniforme => F. verticillioides is also an important producer of Fumonisins This information was removed from the abstract due to the changes made in the manuscript. L35: cross validation => cross-validation Adjusted as suggested: lines 36, 193 and 242. L38: No significant difference was observed between predicted values using NIR and reference values using Liquid Chromatography Coupled to Tandem Mass Spectrometry (LC-MS/MS) (p > 0.05). => This is only mentioned in the abstract, I never found it back in the results section, in addition which statistical test was used to test this? All statistical tests used to make comparisons between LC-MS/MS and NIR are mentioned in lines 197 to 200. In the results section, the statistical test and the p value for total fumonisins are mentioned in lines 312-315; the statistical results of zearalenone and the p value are mentioned in lines 325 to 328. L50: These 50 mycotoxins are chief phytopathogens of maize => Toxins are no pathogens This information was removed from the manuscript. Fig 5. What is the difference between the red and the blue line => I suggest calibration and validation, but it is not in the caption which color refers to calibration and which color to validation. Fig 5 was adjusted and the captions were inserted: the blue line corresponds to calibration and the red line to validation. L180: Several models were developed using different spectral treatments => Which models? Which spectral treatments? Several calibration models have been developed using individual and combination mathematical treatments such as smoothing, normalization (mean, maximum and range), baseline offset, multiplicative Scatter Correction (MSC), derivatives, standard normal variate (SNV) and detrending (DT). The model with the best accuracy was selected as the finest one. This information was entered in the results section in lines 242-246. L182 as they were considered outliers by the development software => What is defined as an outlier by the software? An anomalous sample, also known as an outlier, is an object or, in our case, a sample, which distances itself from the others within the sample set and may not belong to the same population as the majority. Generally, the presence of these samples allows the construction of models with high values of errors and low capacity of prediction, significantly influencing results prediction. Thus, the detection of outliers is of paramount importance and, often, the removal of these samples leads to the construction of more accurate and efficient models, ensuring their predictive quality. A sample can be considered as an outlier due to several reasons: measurement error, noise, extreme samples, deviating products or wrong tabling. The software offers several regression methods for making models (calibrations). Regression is a generic term used for all methods that try to model and analyze several variables (in our case, 4,200 independent variables, X, and 1 dependent variable, Y) in order to build a relationship between two groups of variables. The adjusted model can then be used only to predict new values. In regression, there are many ways for a sample to be classified as anomalous. It can be peripheral according to X variables only, or Y variables only, or both. Moreover, it may not be a discrepancy for a separate set of variables, but it can become a discrepancy when considering the relationship (X, Y). We used Partial Least Square (PLS) as a regression method. It is the most suitable for quantitative analysis and the calibrations are generally more robust and can be applied in complex mixtures, as in our case. The fundamental basis of this method is the Principal Component Analysis (PCA), which consists of manipulating the data matrix in order to represent the variations occurring in many variables, through a smaller number of "factors". From this, new systems of axes (called factors, main components, latent variables or even eigenvectors) are constructed to represent the samples, in which the multivariate nature of the data can be visualized in a few dimensions. The software uses several methodologies to detect outliers, among which are the ones we used: "leverage" and Student residues. Leverages are useful for detecting samples which are far from the center within the space described by the model. Samples with high leverage differ from the average samples; in other words, they are likely outliers. A large leverage also indicates a high influence on the model. Student residues are the concentration residues, which are calculated in cross-validation, that is, poorly modeled samples have high residues. L190 At the end of this process, the model was reduced to 203 samples for FB1 and 202 samples for FB2, i.e., 33 and 32 samples were excluded, respectively. => Which samples were excluded. Is there a reason why these samples were excluded? It is always interesting to have an in-depth look at the outliers. Yes, it is important to look at these samples carefully, as in some cases it can be a measurement error. The software automatically excludes some samples when creating the model using specific detection tools (for example, leverage and student residue). In this study, the outliers were samples that presented very extreme results and were thus very distant from the population, or they were samples with different characteristics from the others in our database. In such cases, removing these samples is the best alternative. However, the calibrations are dynamic, so from time to time we insert new samples that are considered interesting to make the model more robust and expand the range of sample frequency so that all ranges of concentration and variability are represented. Attachments Attachment Submitted filename: Answers_paper.docx https://doi.org/10.1371/journal.pone.0244957.r002
6 Nov 2020 Decision Letter - Vijai Gupta, Editor PONE-D-20-04995R1 Multivariate method for prediction of fumonisins B1 and B2 and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR) PLOS ONE Dear Dr. Mallmann, 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 Dec 21 2020 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. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols We look forward to receiving your revised manuscript. Kind regards, Vijai Gupta, PhD in Microbiology Academic Editor PLOS ONE Additional Editor Comments (if provided): MS needs additional corrections before it may be considered for publication in PLOS One. Kindly do the needful changes and submit your revised MS. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. 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 #1: (No Response) Reviewer #2: (No Response) ******** 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 #1: Yes Reviewer #2: Partly ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 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 #1: Yes Reviewer #2: No ****** 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 #1: Yes Reviewer #2: 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 #1: I revised the first version of the paper and I think the authors much improved the original paper. Therefore I dont have any further criticism that would not allow the publication of the paper Reviewer #2: ADVISE: MAJOR REVISION. Although the manuscript improved a lot compared to the previous version, there are still some important changes necessary before it can be published. In your answer to my first question you mention that the novelty in the article lies in the fact that Many studies are aimed at countries with different climates and frequencies of positivity. Do you mean that samples with ZEN/FB from Brazil will be characterized by another spectrum of samples from another region (if the concentrations are similar)? In my opinion the shift in reflection caused by the presence of a certain toxin at a certain concentration is not highly depended on the region were you took the sample? Please explain in your discussion why it is important that you also do this analysis with samples from Brazil. Another answer was: Regarding the investigation of spectral bands, as mentioned in the manuscript, this information remains to be clarified, so we used the entire spectrum. However, work approaching this matter is under development by our research group and it will be promptly published. Ok you used the entire spectrum to fit the models, but based on the loadings of the PCA analysis and/or coefficients from the PLSR you can easily define characteristic bands for a certain toxin, I want to see this in the article before it can be published, otherwise it does not has an added value for other researchers, the only thing they now know is that you can predict these toxins. But it is important to compare these results with other findings…. In Figure 1 you show the spectrum of the fumonisin samples and of the zearalenone samples. It does not have an added value to divide it into “fumonisin” and “ZEN” samples as the samples you analysed for FB can also contain ZEN and vice versa (and also other toxins)? Did you analyse certain samples for both ZEN and FB, since both toxins often co-occur? In Figure 2 you show the results of the PCA analysis, however this figure is not very clear. Is it possible to make a 2D biplot and color the dots according to the concentration of ZEN/FB and then also draw the most important loadings so that the reader gains insight into which wavelengths are important for high ZEN or FUM concentrations Can you include the models (coefficients of the PLS model) in supplementary data so that they can be tested by the readers? I think you can reduce the number of figures, Figures 2-4 can be deleted, Fig. 5 replace it according to the previous comment. Fig 8-10 can be in the supplementary data. In the discussion you do not have to refer to figures L365 ZEA => ZEN ****** 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 #1: No Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0244957.r003
Revision 2
15 Dec 2020 Author Response PONE-D-20-04995R1 Authors’ responses to reviewers Reviewer #1: I revised the first version of the paper and I think the authors much improved the original paper. Therefore I dont have any further criticism that would not allow the publication of the paper. • We greatly appreciate your time and effort regarding the revision of our manuscript. Reviewer #2: 1) In your answer to my first question you mention that the novelty in the article lies in the fact that Many studies are aimed at countries with different climates and frequencies of positivity. Do you mean that samples with ZEN/FB from Brazil will be characterized by another spectrum of samples from another region (if the concentrations are similar)? In my opinion the shift in reflection caused by the presence of a certain toxin at a certain concentration is not highly depended on the region were you took the sample? Please explain in your discussion why it is important that you also do this analysis with samples from Brazil. • It is important to develop calibrations of more specific databases for each region, since the contamination levels may vary from one place to another; so, samples originating from distinct regions and presenting diverse concentrations are required. In Brazil, for example, the levels of FBs are high, while the levels of ZEN are low. In Europe, in turn, the levels of ZEN and DON are high, while the levels of FBs are low. As suggested, this matter has been further addressed in the discussion section (lines 319 - 325). 2) Another answer was: Regarding the investigation of spectral bands, as mentioned in the manuscript, this information remains to be clarified, so we used the entire spectrum. However, work approaching this matter is under development by our research group and it will be promptly published. Ok you used the entire spectrum to fit the models, but based on the loadings of the PCA analysis and/or coefficients from the PLSR you can easily define characteristic bands for a certain toxin, I want to see this in the article before it can be published, otherwise it does not has an added value for other researchers, the only thing they now know is that you can predict these toxins. But it is important to compare these results with other findings…. • Indeed, we believe this information is relevant for future works, so we included our findings in the discussion section (lines 311-314). 3) In Figure 1 you show the spectrum of the fumonisin samples and of the zearalenone samples. It does not have an added value to divide it into “fumonisin” and “ZEN” samples as the samples you analysed for FB can also contain ZEN and vice versa (and also other toxins)? Did you analyse certain samples for both ZEN and FB, since both toxins often co-occur? • The samples characterized by the spectra belong to different databases. The figure intended to represent the total amount of these samples and the ranges used. The samples included when building the models were tested in routine laboratory analysis, so not all of them were analyzed for FBs and ZEN simultaneously. Anyhow, this figure was removed from the manuscript. 4) In Figure 5 you show the results of the PCA analysis, however this figure is not very clear. Is it possible to make a 2D biplot and color the dots according to the concentration of ZEN/FB and then also draw the most important loadings so that the reader gains insight into which wavelengths are important for high ZEN or FUM concentrations. • The image depicted in Figure 5 (now Figure 1) was replaced for a new one showing the most important variables, according to the reviewer’s suggestion. 5) Can you include the models (coefficients of the PLS model) in supplementary data so that they can be tested by the readers? • This work has been developed along the past 10 years, and several development attempts have been tested. Thus, it is not possible to include the information regarding the coefficients of the PLS model, since it refers to confidential data. 6) I think you can reduce the number of figures, Figures 2-4 can be deleted, Fig. 5 replace it according to the previous comment. • Figures 2-4 have been removed from the manuscript. Figure 5 was replaced by Figure 1, as suggested by the review. 7) Fig 8-10 can be in the supplementary data. • Done as suggested (lines 382-389). 8) In the discussion you do not have to refer to figures L365 ZEA => ZEN • ZEA was replaced for ZEN in lines 76, 290 and 360. Attachments Attachment Submitted filename: Response to reviewers_03.12.20.docx https://doi.org/10.1371/journal.pone.0244957.r004
21 Dec 2020 Decision Letter - Vijai Gupta, Editor Multivariate method for prediction of fumonisins B1 and B2 and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR) PONE-D-20-04995R2 Dear Dr. Mallmann, 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 for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. 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. Kind regards, Vijai Gupta, PhD in Microbiology Academic Editor PLOS ONE Additional Editor Comments (optional): All the editorial, as well as reviewers comments, have been addressed. Reviewers' comments: https://doi.org/10.1371/journal.pone.0244957.r005
Formally Accepted
26 Dec 2020 Acceptance Letter - Vijai Gupta, Editor PONE-D-20-04995R2 Multivariate method for prediction of fumonisins B₁ and B₂ and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR) Dear Dr. Mallmann: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. 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 plosone@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. Vijai Gupta Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0244957.r006

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