Fast Determination of Amylose Content in Lotus Seeds Based on Hyperspectral Imaging
, Zebin Guo 2 and Baodong Zheng 2,*
Round 1
Reviewer 1 Report
1. Introduction: The importance of using HSI for amylose prediction is less clear. It is also necessary to explain why it is necessary to make an amylose map.
2. Methods: how many seed samples scanned? how many spectra obtained? You used 3 varieties, how many seeds per varieties used?
3. Methods: what is the number of repetitions in the amylose measurement?
4. Results: it is suggested to apply Savitzky Golay first Derivative to plot the spectra (to replace Figure 2). By doing so, the absorbace or reflectance peaks will be more distinct.
5. Figure 8: what a,b, c, refer to?
Author Response
First, we would like to express our sincere gratitude to the reviewer. Your suggestions are all valuable and helpful for revising and improving our manuscript, as well as the important guiding significance to our researchers. We have studied comments carefully and have made correction which we hope meet with approval. The summary of corrections and the responses to the reviewer's comments are listed below.
- Introduction: The importance of using HSI for amylose prediction is less clear. It is also necessary to explain why it is necessary to make an amylose map.
Response: we added the description into the introduction in the new manuscript in red as follows:
“Using lotus seed juice as the example, amylose is easier to retrogradation than amylopectin [5]. Therefore, lotus seeds with low amylose content can be selected for lotus seed juice production to ensure the quality of its product. Studies [6] have shown that the amylose content of lotus seeds varied greatly not only among different lotus cultivar. So it is necessary to detect its content before processing to improve production quality.” in line 37-42.
“Obviously, the traditional amylose detection methods are not appropriate for industrial production and processing.” in line 45-46.
“However, there is still a lack of research on the detection of lotus seed amylose based on HSI technology. And there is potential for HSI or spectroscopy in determining amylose contents in lotus seeds according to former studies.” in line 62-65.
“HIS has the advantage of being informative, but still contains irrelevant information. It usually needs to adopt different preprocessing methods and feature selection algorithms before modeling in order to reduce the complexity of model and improve the accuracy [16]. Besides, human interaction with hyperspectral images is very essential for image interpretation and analysis [17], so the internal ingredient visualization by rendering them as RGB color images could help to discriminate the amylose content directly or further development of online detection equipment.” in line 66-72.
- Methods: how many seed samples scanned? how many spectra obtained? You used 3 varieties, how many seeds per varieties used?
Response: we scanned 120 samples and 120 spectra were obtained. We used 40 lotus seed of each variety. The description was added into the new manuscript in line 84–85 as follows:
“Then the hyperspectral images were obtained by selecting a total of 120 lotus seeds, with 40 samples for each variety.”
- Methods: what is the number of repetitions in the amylose measurement?
Response: we determined the amylose three repetitions in each lotus seed. The description was added into the new manuscript in line 106 as follows:
“Each of the sample was determined by three repetitions.”
- Results: it is suggested to apply Savitzky Golay first Derivative to plot the spectra (to replace Figure 2). By doing so, the absorbace or reflectance peaks will be more distinct.
Response: we added the plot of processed spectra by Savitzky Golay first Derivative as figure 2(b).
Figure 2. Average spectra at ROI. (a) original spectra; (b) processed spectra by Savitzky Golay first Derivative.
- Figure 8: what a,b, c, refer to?
Response: we are sorry for the mistakes. We revised the error number of the figures shown in the new manuscript in line 333 and line 335.
Author Response File: 
Author Response.docx
Reviewer 2 Report
The article concerns the application of hyperspectral imaging for the quantification of amylose in lotus seeds. The approach of the work is correct and the methodology also. There are, however, some points for improvement in the work.
As a general comment, it must be said that the introduction is as deficient as the bibliography. I suggest citing more work on the subject, arguing well in the introduction. Another consideration must be made regarding the discussion. I suggest the authors include an outline with the basic procedure to be followed, as a summary, in order to replicate their method.
Below, specific comments are given line by line.
Line 70. Please correct the spectrograph characteristics: The V10E Specim spectral range is 400-1000 with 2.8 spectral resolution (using a 30µm slit), as Specim datasheet reports. Add please the slit used.
Line 73. Please check the value of light source. 15W x2 halogen lamp is not sufficient to excite the sample and to have a good response of the sensor.
Line 74. “The whole system was set in a dark chamber”. this sentence can be deleted. The hyperspectral system does not need the darkroom as it must be calibrated to 0 and 100 per cent brightness before analysis, in any case. Calibration cancels out any external interference, as long as it does not change.
Line 76. Please, insert the reference for eq. 1:
ElMasry,G., Wang, N., ElSayed, A., Ngadi, M. 2007. Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. Journal of Food Engineering, 81(1), 98-107.
https://doi.org/10.1016/j.jfoodeng.2006.10.016.
Line 93. Indicate please the total number of samples. Then, specify how many samples for calibration and validation.
Line 97. equation 3. Please improve syntax. The summation index is incomplete (indicate k=1) and the internal syntax also. Restate the equation as expressed in the original work cited. Check also the other equations.
Line 133. Since you indicate the software indicated for pre-processing, please explain what the pre-processing operations were.
Line 234 – table 3. Indicate please the equation to obtain the RPD value.
Line 254. Please insert the description of subfigures 4a, 4b and 4c in the caption.
Author Response
First, we would like to express our sincere gratitude to the reviewer. Your suggestions are all valuable and helpful for revising and improving our manuscript, as well as the important guiding significance to our researchers. We have studied comments carefully and have made correction which we hope meet with approval. The summary of corrections and the responses to the reviewer's comments are listed below.
- The article concerns the application of hyperspectral imaging for the quantification of amylose in lotus seeds. The approach of the work is correct and the methodology also. There are, however, some points for improvement in the work.
As a general comment, it must be said that the introduction is as deficient as the bibliography. I suggest citing more work on the subject, arguing well in the introduction. Another consideration must be made regarding the discussion.
Response: We added more bibliographies into the new manuscript as follows:
“5. Zheng, M.; Su, H.; You, Q.; Zeng, S.; Zheng, B.; Zhang, Y.; Zeng, H. An Insight into the Retrogradation Behaviors and Molecular Structures of Lotus Seed Starch-Hydrocolloid Blends. Food Chemistry 2019, 295, 548–555, doi:10.1016/j.foodchem.2019.05.166.
- Sun, H.; Li, J.; Song, H.; Yang, D.; Deng, X.; Liu, J.; Wang, Y.; Ma, J.; Xiong, Y.; Liu, Y.; et al. Comprehensive Analysis of AGPase Genes Uncovers Their Potential Roles in Starch Biosynthesis in Lotus Seed. BMC Plant Biology 2020, 20, 457, doi:10.1186/s12870-020-02666-z.
- Duan, D.X.; Donner, E.; Liu, Q.; Smith, D.C.; Ravenelle, F. Potentiometric Titration for Determination of Amylose Content of Starch – A Comparison with Colorimetric Method. Food Chemistry 2012, 130, 1142–1145, doi:10.1016/j.foodchem.2011.07.138.
Li, S.; Song, Q.; Liu, Y.; Zeng, T.; Liu, S.; Jie, D.; Wei, X. Hyperspectral Imaging-Based Detection of Soluble Solids Content of Loquat from a Small Sample. Postharvest Biology and Technology 2023, 204, 112454, doi:10.1016/j.postharvbio.2023.112454.
- Coliban, R.-M.; MarincaÅŸ, M.; Hatfaludi, C.; Ivanovici, M. Linear and Non-Linear Models for Remotely-Sensed Hyperspectral Image Visualization. Remote Sensing 2020, 12, 2479, doi:10.3390/rs12152479.
- ElMasry, G.; Wang, N.; ElSayed, A.; Ngadi, M. Hyperspectral Imaging for Nondestructive Determination of Some Quality Attributes for Strawberry. J. Food Eng. 2007, 81, 98–107, doi:10.1016/j.jfoodeng.2006.10.016.
- Zhang, C.; Liu, F.; Kong, W.W.; Cui, P.; He, Y.; Zhou, W.J. Estimation and Visualization of Soluble Sugar Content in Oilseed Rape Leaves Using Hyperspectral Imaging. Trans. ASABE 2016, 59, 1499–1505, doi:10.13031/trans.59.10485.
- Jie, D.; Xie, L.; Rao, X.; Ying, Y. Using Visible and near Infrared Diffuse Transmittance Technique to Predict Soluble Solids Content of Watermelon in an On-Line Detection System. Postharvest Biol. Technol. 2014, 90, 1–6, doi:10.1016/j.postharvbio.2013.11.009.”
- I suggest the authors include an outline with the basic procedure to be followed, as a summary, in order to replicate their method.
Response：we draw the whole procedure as graphic abstract in Figure S1. We considered that it could replicate according to the procedure.
- Below, specific comments are given line by line.
Response：Thanks for your comment. We carefully revised our manuscript according to your comments line by line.
- Line 70. Please correct the spectrograph characteristics: The V10E Specim spectral range is 400-1000 with 2.8 spectral resolution (using a 30µm slit), as Specim datasheet reports. Add please the slit used.
Response: we added the slit into the new manuscript in line 90.
- Line 73. Please check the value of light source. 15W x2 halogen lamp is not sufficient to excite the sample and to have a good response of the sensor.
Response: we are sorry for the mistake. We changed the 15W into 150W.
- Line 74. “The whole system was set in a dark chamber”. this sentence can be deleted. The hyperspectral system does not need the darkroom as it must be calibrated to 0 and 100 per cent brightness before analysis, in any case. Calibration cancels out any external interference, as long as it does not change.
Response: thanks for your carefully explanation, we deleted the whole sentence.
- Line 76. Please, insert the reference for eq. 1:
ElMasry,G., Wang, N., ElSayed, A., Ngadi, M. 2007. Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. Journal of Food Engineering, 81(1), 98-107. https://doi.org/10.1016/j.jfoodeng.2006.10.016.
Response: we added the reference into the new manuscript as the 18th reference.
- Line 93. Indicate please the total number of samples. Then, specify how many samples for calibration and validation.
Response: we added the description “The 120 sample was divided into calibration and prediction set by sample set partitioning based on joint x-y distances(SPXY)” in line 111-112 and “In this study, the ratio of calibration and prediction was set as 2:1.” into the new manuscript in line 114-115. We have shown the specific number of samples for calibration and prediction in table 1.
- Line 97. equation 3. Please improve syntax. The summation index is incomplete (indicate k=1) and the internal syntax also. Restate the equation as expressed in the original work cited. Check also the other equations.
Response: we checked all the equations, and the wrong equations were revised.
- Line 133. Since you indicate the software indicated for pre-processing, please explain what the pre-processing operations were.
Response: we added the description of the pre-processing method into the new manuscript in line 125-130 as follows:
“2.5 Spectral pre-processing
Savitzky-Golay smoothing (S-G smoothing), standard normal variate (SNV), first-order derivative (1st-D) and multiple scatter correction (MSC) method were used to the spectra pretreatment. They have been proved useful in eliminating random noise, removing the multiplicative signal effects, eliminating the baseline drift or correcting the scattering, respectively[20].”
- Line 234 – table 3. Indicate please the equation to obtain the RPD value.
Response：we added the equation of RPD into the new manuscript in line 165-169.
“2.9 Evaluation of model performance
The evaluation of regression model performance primarily include the correlation coefficient (R), root mean square error (RMSE), and residual predictive deviation (RPD). RPD was calculated as the ratio of standard deviation (SD) of the reference values to the root-mean-square error of prediction(RPD = SD/RMSEP)[24,25].”
- Line 254. Please insert the description of subfigures 4a, 4b and 4c in the caption.
Response: we revised the description of subfigures 4a, 4b, 4c and 4d as follows:
“Feature wavelengths selection by SPA. (a)RMSE trend chart of SG-SPA and the mini-mum RMSE denoted by red square; (b) feature bands selected by SPA with SG-input; (c) RMSE trend chart of SG-UVE-SPA and the minimum RMSE denoted by red square; (d) feature bands selected by SPA with SG-UVE input.”
Author Response File: Author Response.docx
Round 2
Reviewer 2 Report
Dear authors,
the paper was correctly improved following the suggestions.
