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Effective Quantification of Tannin Content in Sorghum Grains Using Near-infrared Spectroscopy

Received: 2 August 2020     Accepted: 22 January 2021     Published: 28 January 2021
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Abstract

This study was conducted to investigate the feasibility of determining tannin content in sorghum grains with near-infrared reflectance spectroscopy (NIRS). A total of 110 sorghum grain samples were collected. The data matrix of the pretreated NIRS was randomly divided into a calibration set (Nc=77 samples) and a prediction set (Np=33 samples). The analysis of tannin content was based on the colorimetric method of GBT 15686-2008. Diffuse reflectance spectra of 110 sorghum samples were generated on a Fourier-transform NIRS with a scanning range of 12800-4000 cm-1 and resolution of 16 cm-1 and 64 scans. Several spectra pretreatment methods were compared to for an optimum spectral pretreatment method. The optimal model was determined according to coefficient of determination for calibration (R2CAL), root mean standard error of calibration (RMSECAL), coefficient of determination for cross-validation (R2CV), root mean standard error of cross-validation (RMSECV) and the residual predictive deviation (RPD). The results showed that the tannin content of the sorghum grains ranged from 0.01% to 2.12% DM with the average of 0.58%, and first derivative was the optimal spectral pretreatment with the lowest RMSECV of 0.14. The absorption peaks of the optimal model mainly located at 9402-7492 cm-1 and 5452-4244 cm-1. The RPD of calibration, cross-validation and external validation were 6.22, 4.22 and 3.0, respectively. The findings suggest that the established model using NIRS is effective to quantify tannin content in sorghum grains rapidly.

Published in International Journal of Animal Science and Technology (Volume 5, Issue 1)
DOI 10.11648/j.ijast.20210501.12
Page(s) 7-12
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Tannin, Near-infrared Spectroscopy, Sorghum

References
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[2] Xiong Y, Zhang P, Warner R D, et al. Sorghum grain: From genotype, nutrition, and phenolic profile to its health benefits and food applications. Compre. Rev. Food Sci. F. 2019; 34: 1541-1550.
[3] Shen S, Rui H, Charlie L, et al. Phenolic compositions and antioxidant activities differ significantly among sorghum grains with different applications. Molecules 2018; 23: 1203-1214.
[4] Zhao J F, Hu G, Tang L. Effects of supplementation with compound enzymes and probiotics on sorghum diets on growth performanc, serum antioxidant indexes and intestinal morphology of Liangfenghua broilers. Chinese J. Anim. Nutr. 2018; 30: 2318-2327.
[5] Paulk C B, Hancock J D, Fahrenholz A C, et al. Effects of sorghum particle size on milling characteristics and growth performance in finishing pigs. Anim. Feed Sci. Tech. 2015; 202: 75-80.
[6] Wang T, Hou M, Shang J. Characteristics of muscle fibers and relative meat quality of fattening sheep fed sweet sorghum silage and corn silage diets. Pratacultural Sci. 2018; 35: 2722-2727.
[7] Ronda V A C V. Sorghum for Animal Feed. In: Breeding Sorghum for Diverse End Uses, UK: Woodhead Publishing, 2019; pp. 229-238.
[8] Dykes L. Tannin Analysis in Sorghum Grains: Methods and Protocols. Sorghum, 2019.
[9] Zhang J, Li S, Lin M, et al. A near-infrared reflectance spectroscopic method for the direct analysis of several fodder-related chemical components in drumstick (Moringa oleifera Lam.) leaves. Biosci. Biotech. Biochem. 2018; 82: 1-8.
[10] Rungpichayapichet P, Mahayothee B, Nagle M, et al. Robust NIRS models for non-destructive prediction of postharvest fruit ripeness and quality in mango. Postharvest Biol. Tech. 2016; 111: 31-40.
[11] Mohamed E S, Saleh A M, Belal A B, et al. Application of near-infrared reflectance for quantitative assessment of soil properties. Egypt J. Remote Sensing Space Sci. 2018; S1168123289.
[12] De Girolamo A, Cervellieri S, Cortese M, et al. Fourier transform near-infrared and mid-infrared spectroscopy as efficient tools for rapid screening of deoxynivalenol contamination in wheat bran. J. Sci. Food Agri. 2019; 99: 1946-1953.
[13] Jaconi A, Vos C D. Near infrared spectroscopy as an easy and precise method to estimate soil texture. Geoderma. 2019; 337: 906-913.
[14] Dykes L, Hoffmann Jr. L, Portillo-Ridriguez O, et al. Prediction of total phenols, condensed tannins, and 3-deoxyanthocyanidins in sorghum grain using near-infrared (NIR) spectroscopy. J. Cereal Sci. 2014; doi: 10.1016/j.jcs.2014.02.002.
[15] GB. Sorghum-Determination of tannin content GB/T 15686-2008. General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China; National standardization management Council, Chinese Standard Publishing. 2008.
[16] Zhang Y, Zhou F, Zhang X, et al. Cluster analysis and determination of protein and tannin content in sorghum germplasms. Tianjin Agri. Sci. 2017; 54: 65-74.
[17] Mu Z, Tian X, Shi Y. Determination method of tannin content in sorghum. J. Shanxi Agri. Sci. 2014; 23: 42-46.
[18] Towett E K, Alex M, Shepherd K D, et al. Applicability of near-nfrared reflectance spectroscopy (NIRS) for determination of crude protein content in cowpea (Vigna unguiculata) leaves. Food Sci. Nutr. 2013; 1: 45-53.
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  • APA Style

    Yongsheng Wang, Jie Li, Bo Wang, Yuting Zhang, Junling Geng, et al. (2021). Effective Quantification of Tannin Content in Sorghum Grains Using Near-infrared Spectroscopy. International Journal of Animal Science and Technology, 5(1), 7-12. https://doi.org/10.11648/j.ijast.20210501.12

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    ACS Style

    Yongsheng Wang; Jie Li; Bo Wang; Yuting Zhang; Junling Geng, et al. Effective Quantification of Tannin Content in Sorghum Grains Using Near-infrared Spectroscopy. Int. J. Anim. Sci. Technol. 2021, 5(1), 7-12. doi: 10.11648/j.ijast.20210501.12

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    AMA Style

    Yongsheng Wang, Jie Li, Bo Wang, Yuting Zhang, Junling Geng, et al. Effective Quantification of Tannin Content in Sorghum Grains Using Near-infrared Spectroscopy. Int J Anim Sci Technol. 2021;5(1):7-12. doi: 10.11648/j.ijast.20210501.12

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  • @article{10.11648/j.ijast.20210501.12,
      author = {Yongsheng Wang and Jie Li and Bo Wang and Yuting Zhang and Junling Geng and Li Xin Wen and Aike Li},
      title = {Effective Quantification of Tannin Content in Sorghum Grains Using Near-infrared Spectroscopy},
      journal = {International Journal of Animal Science and Technology},
      volume = {5},
      number = {1},
      pages = {7-12},
      doi = {10.11648/j.ijast.20210501.12},
      url = {https://doi.org/10.11648/j.ijast.20210501.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijast.20210501.12},
      abstract = {This study was conducted to investigate the feasibility of determining tannin content in sorghum grains with near-infrared reflectance spectroscopy (NIRS). A total of 110 sorghum grain samples were collected. The data matrix of the pretreated NIRS was randomly divided into a calibration set (Nc=77 samples) and a prediction set (Np=33 samples). The analysis of tannin content was based on the colorimetric method of GBT 15686-2008. Diffuse reflectance spectra of 110 sorghum samples were generated on a Fourier-transform NIRS with a scanning range of 12800-4000 cm-1 and resolution of 16 cm-1 and 64 scans. Several spectra pretreatment methods were compared to for an optimum spectral pretreatment method. The optimal model was determined according to coefficient of determination for calibration (R2CAL), root mean standard error of calibration (RMSECAL), coefficient of determination for cross-validation (R2CV), root mean standard error of cross-validation (RMSECV) and the residual predictive deviation (RPD). The results showed that the tannin content of the sorghum grains ranged from 0.01% to 2.12% DM with the average of 0.58%, and first derivative was the optimal spectral pretreatment with the lowest RMSECV of 0.14. The absorption peaks of the optimal model mainly located at 9402-7492 cm-1 and 5452-4244 cm-1. The RPD of calibration, cross-validation and external validation were 6.22, 4.22 and 3.0, respectively. The findings suggest that the established model using NIRS is effective to quantify tannin content in sorghum grains rapidly.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Effective Quantification of Tannin Content in Sorghum Grains Using Near-infrared Spectroscopy
    AU  - Yongsheng Wang
    AU  - Jie Li
    AU  - Bo Wang
    AU  - Yuting Zhang
    AU  - Junling Geng
    AU  - Li Xin Wen
    AU  - Aike Li
    Y1  - 2021/01/28
    PY  - 2021
    N1  - https://doi.org/10.11648/j.ijast.20210501.12
    DO  - 10.11648/j.ijast.20210501.12
    T2  - International Journal of Animal Science and Technology
    JF  - International Journal of Animal Science and Technology
    JO  - International Journal of Animal Science and Technology
    SP  - 7
    EP  - 12
    PB  - Science Publishing Group
    SN  - 2640-1312
    UR  - https://doi.org/10.11648/j.ijast.20210501.12
    AB  - This study was conducted to investigate the feasibility of determining tannin content in sorghum grains with near-infrared reflectance spectroscopy (NIRS). A total of 110 sorghum grain samples were collected. The data matrix of the pretreated NIRS was randomly divided into a calibration set (Nc=77 samples) and a prediction set (Np=33 samples). The analysis of tannin content was based on the colorimetric method of GBT 15686-2008. Diffuse reflectance spectra of 110 sorghum samples were generated on a Fourier-transform NIRS with a scanning range of 12800-4000 cm-1 and resolution of 16 cm-1 and 64 scans. Several spectra pretreatment methods were compared to for an optimum spectral pretreatment method. The optimal model was determined according to coefficient of determination for calibration (R2CAL), root mean standard error of calibration (RMSECAL), coefficient of determination for cross-validation (R2CV), root mean standard error of cross-validation (RMSECV) and the residual predictive deviation (RPD). The results showed that the tannin content of the sorghum grains ranged from 0.01% to 2.12% DM with the average of 0.58%, and first derivative was the optimal spectral pretreatment with the lowest RMSECV of 0.14. The absorption peaks of the optimal model mainly located at 9402-7492 cm-1 and 5452-4244 cm-1. The RPD of calibration, cross-validation and external validation were 6.22, 4.22 and 3.0, respectively. The findings suggest that the established model using NIRS is effective to quantify tannin content in sorghum grains rapidly.
    VL  - 5
    IS  - 1
    ER  - 

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Author Information
  • Animal Nutrition Center of Nutrition & Health Research Institute, China Oil & Foodstuffs Corporation, Beijing, China

  • Animal Nutrition Center of Nutrition & Health Research Institute, China Oil & Foodstuffs Corporation, Beijing, China

  • Animal Nutrition Center of Nutrition & Health Research Institute, China Oil & Foodstuffs Corporation, Beijing, China

  • Beijing Engineering Research Center of Livestock Products Quality and Safety Source Control, Beijing, China

  • Beijing Engineering Research Center of Livestock Products Quality and Safety Source Control, Beijing, China

  • College of Veterinary Medicine, Hunan Agricultural University, Changsha, China

  • Institute of Grain Quality and Nutrition, Academy of National Food and Strategic Reserves Administration, Beijing, China

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