Application of FTIR-ATR Spectrometry in Conjunction with Multivariate Regression Methods for Viscosity Prediction of Worn-Out Motor Oils

Round 1

Reviewer 1 Report

I read the paper titled "Application of FTIR-ATR Spectrometry in Conjunction with Multivariate Regression Methods for Viscosity Prediction of Worn-Out Motor Oils"
I found the research interesting. in fact, although the topic is well known, the results are interesting because provide a new approach to use for the prediction of viscosity and other parameters. The paper is well written and the goal is focused and well-realized.
The manuscript is potentially interesting for the readers of Applied Science. As a reviewer, I still have some comments and suggestions.

1.            3.1 Figure 1: a difference in the background is observed between the two spectra, do you observe the same behavior for other samples?

2.            3.1 usually a data treatment is performed before statistical approaches, such as normalization or background subtraction. Did you try something? 

3.            Please improve the quality of the figures and standardize the style. Furthermore, I suggest avoiding the use of the comma as a thousands separator, it makes confusion

4.            references. please check the years, some of them are not in bold

Author Response

Reviewer 1:

Author's Notes

Thank you for your comments and suggestions that allowed us to greatly improve the quality of the manuscript. We agree with all your comments.

Your comments are in bold text and our responses in plain italics.

1. 1 Figure 1: a difference in the background is observed between the two spectra, do you observe the same behavior for other samples?

Figure 1 shows the spectra of the oil samples with the most significant difference in spectra. For the other samples, this difference was not so significant. An explanation is given on lines 209-223. Based on this question, the authors added the text, see line 308-310.

Line 308-310: The proposed calibration model must be continuously supplemented by other standards – motor oils, the composition of which will correspond to the matrix of worn-out motor oils and the range of their values KV_100 °C.

2. 1 usually a data treatment is performed before statistical approaches, such as normalization or background subtraction. Did you try something? 

Yes, the background was scanned (ATR crystal without sample) and the data was normalized by mean centering - this is described on lines 192-197.

The authors added in section 2. Materials and methods, details regarding the methods used, please see lines 169-170 and 179-180.

Line 169-170: The capillary tube size 1C (capillary constant 0.03 mm²s^-1) is used for the standard analytical range 6–30 mm²s^-1.

Line 179-180: The new background spectrum was obtained before measuring each sample to reduce baseline shifting and ambient variations.

3.     Please improve the quality of the figures and standardize the style. Furthermore, I suggest avoiding the use of the comma as a thousands separator, it makes confusion

The authors improved the quality of the images and removed the commas as thousands separators.

4.       references. please check the years, some of them are not in bold

We looked at the instructions for authors and in the case of books and conference proceedings, the year of publication is not marked in bold (see below).

 

• Journal Articles:
  1. Author 1, A.B.; Author 2, C.D. Title of the article. Abbreviated Journal NameYear, Volume, page range.
• Books and Book Chapters:
  2. Author 1, A.; Author 2, B. Book Title, 3rd ed.; Publisher: Publisher Location, Country, Year; pp. 154–196.
  · Conference Proceedings:
  7. Author 1, A.B.; Author 2, C.D.; Author 3, E.F. Title of Presentation. In Title of the Collected Work (if available), Proceedings of the Name of the Conference, Location of Conference, Country, Date of Conference; Editor 1, Editor 2, Eds. (if available); Publisher: City, Country, Year (if available); Abstract Number (optional), Pagination (optional).

 

Thank you very much, authors

 

Author Response File: Author Response.docx

Reviewer 2 Report

In this manuscript, the authors used FTIR-ATR spectrometry and multivariate regression methods (Partial Least Squares and Principal Component Regression) to predict the worn-out motor oils' viscosity. The manuscript is overall well written and structured, and the result is interesting. I would recommend to accept this manuscript as long as the authors justify and address the following issues.

1. The model calibration depends on the height, the slope, and curvature of the spectrum. How are the slope and curvature calculated for the discrete spectrum? How would the slope and curvature calculated from different numerical change the results presented in Table 1 and the validation of the model in Section 3.2?
2. How good is the model prediction for the verification samples? Can the authors provide all the validation data and list them in a table in Section 3.2? Moreover, what is the predicted value of individual sample, in addition to the RMSEP value for the whole verification sample set?
   
   
   

Author Response

Reviewer 2:

Author's Notes

Thank you for your comments and suggestions that allowed us to greatly improve the quality of the manuscript. We agree with all your comments.

Your comments are in bold text and our responses in plain italics.

1. The model calibration depends on the height, the slope, and curvature of the spectrum. How are the slope and curvature calculated for the discrete spectrum? How would the slope and curvature calculated from different numerical change the results presented in Table 1 and the validation of the model in Section 3.2?

 

In multidimensional FTIR methods we work with matrices, resp.  matrix and unit vector (see illustration image). The variables in the matrix are the absorbances at a given wavelength for each oil sample used to construct the calibration model. If the spectral information in the oil changes (e.g. due to fuel penetration, loss of additives, oil oxidation, etc.), which results in a change in spectral band height, spectrum slope or curvature. This reflects in the matrix by change in absorbance at given wavelength. The authors used 190 samples of differently worn engine oils with different mileage to construct the calibration model, i.e. changes in spectral information were included in the model.

 

The authors supplemented the information resulting from the reviewer's question, please see line 308-310.

Line 308-310. The proposed calibration model must be continuously supplemented by other standards – motor oils, the composition of which will correspond to the matrix of worn-out motor oils and the range of their values KV_100 °C.

 

 

1. How good is the model prediction for the verification samples? Can the authors provide all the validation data and list them in a table in Section 3.2? Moreover, what is the predicted value of individual sample, in addition to the RMSEP value for the whole verification sample set?

 

According to the reviewer's request, the authors published a verification data in Table 2. The authors have also added more text, please see lines 329-333 and 342-346.

Line 329 – 333: Table 2 presents the results of testing the predictive ability to determine KV_100°C by the best FTIR-PLS model (using the adjustment of the spectra of the 2nd derivatives) on fifteen external samples of motor oils. This model provided a relative standard error of prediction 2.3%.

Line 342-346: Due to the fact that the quality of the engine oil is affected by the operating conditions and the technical condition of the vehicle, there may be significant changes in its physical and chemical properties. For this reason, the predictive ability of the proposed calibration model should be continuously verified by real samples of operated oils, for which the kinematic viscosity was determined by the standardized method.

 

Thank you very much, authors

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

The authors have addressed my concerns.