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Journal of Food Measurement and Characterization

, Volume 13, Issue 4, pp 2713–2721 | Cite as

Determination of total sugar content in Siraitia grosvenorii by near infrared diffuse reflectance spectroscopy with wavelength selection techniques

  • Jun YanEmail author
  • Xiao-Ping Huang
  • Ye-Yu Wu
  • Fang-Kai Du
  • Xue-Cai Tan
  • Qi Wang
  • Wei-Wei ZhuEmail author
Original Paper

Abstract

Near-infrared (NIR) diffuse reflectance spectroscopy has become an attractive technique for quality assurance of fruits, drugs, vegetables, and foods. In this work, the information on chemical composition of Siraitia grosvenorii was acquired by NIR, and then a calibration model for the prediction of total sugar content (TSC) was developed using chemometric techniques. One hundred and fifty samples were used as calibration set to develop model, while 40 unknown samples were used for prediction, using phenol-concentrated sulphuric acid assay as reference value. Several spectra preprocessing methods and wavelength selection techniques combined with partial least squares regression were compared, and then standard normal variate coupled with Savitzky–Golay derivative was selected for spectra processing and competitive adaptive reweighted sampling method was applied to extract the informative wavelengths related to the prediction of TSC. The performance of the obtained model has been evaluated by external validation, the square of correlation coefficient (R2) are 0.9097 for a separate blind set consists of 40 unknown samples. Additionally, the result of paired t test (p = 0.3608) illustrated that there is no significant differences between NIR and wet chemistry analysis. These results demonstrated that NIR spectroscopy has the potential to be a time saving and cost-effective method for the determination of the TSC in S. grosvenorii.

Graphic abstract

Keywords

Total sugar Siraitia grosvenorii Chemometrics Near infrared spectroscopy Wavelength selection 

Notes

Acknowledgements

The work was financially supported by the National Nature Foundation Committee of P.R. China (Grant No. 21565006), Guangxi Education Department (KY2016YB132), Innovation Project of Guangxi university graduate (gxun-chxzs2017124), start-up funds of Guangxi University for Nationalities (2015MDQD012), Xiangsihu Young Scholars Innovative Research Team of Guangxi University for Nationalities. Specific research project of Guangxi for research bases and talents (AD18126005).

Compliance with ethical standards

Conflict of interest

There are no conflicts to declare.

References

  1. 1.
    S. Rahul Pawar, J.K. Alexander, J.I. Rader, Sweeteners from plants-with emphasis on Stevia rebaudiana (Bertoni) and Siraitia grosvenorii (Swingle). Anal. Bioanal. Chem. 405, 4397–4407 (2013)CrossRefGoogle Scholar
  2. 2.
    R. Di, M.T. Huang, C.T. He, Anti-inflammatory activities of mogrosides from Momordica grosvenori in murine macrophages and a murine ear edema model. J. Agric. Food. Chem. 59, 7474–7481 (2011)CrossRefGoogle Scholar
  3. 3.
    Y.A. Suzuki, M. Tomoda, Y. Murata, Antidiabetic effect of long -term supplementation with Siraitia grosvenorii on the spontaneously diabetic Goto-Kakizaki rat. Br. J. Nutr. 97, 770–775 (2007)CrossRefGoogle Scholar
  4. 4.
    C. Li, L.M. Lin, F. Sui, Z.M. Wang, H.R. Huo, L. Dai, T.L. Jiang, Chemistry and pharmacology of Siraitia grosvenorii: a review. Chin. J. Nat. Med. 2, 89–102 (2014)Google Scholar
  5. 5.
    G.S. Zhou, M.Y. Wang, Y. Li, Y. Peng, X.B. Li, Rapid and sensitive analysis of 27 underivatized free amino acids, dipeptides, and tripeptides in fruits of Siraitia grosvenorii Swingle using HILIC-UHPLC-QTRAP/MS combined with chemometrics methods. Amino Acids 47, 1589–1603 (2015)CrossRefGoogle Scholar
  6. 6.
    G.S. Zhou, M.Y. Wang, R.J. Xu, X.B. Li, Chemometrics for comprehensive analysis of nucleobases, nucleosides, and nucleotides in Siraitiae Fructus by hydrophilic interaction ultra high performance liquid chromatography coupled with triple-quadrupole linear ion-trap tandem mass spectrometry. J. Sep. Sci. 38, 3508–3515 (2015)CrossRefGoogle Scholar
  7. 7.
    Z.X. Qing, H. Zhao, Q. Tang, C.M. Mo, P. Huang, P. Cheng, P. Yang, X.Y. Yang, X.B. Liu, Y.J. Zheng, J.G. Zeng, Systematic identification of flavonols, flavonol glycosides, triterpene and siraitic acid glycosides from Siraitia grosvenorii using high-performance liquid chromatography quadrupole-time-of-flight mass spectrometry combined with a screening strategy. J. Pharm. Biomed. Anal. 138, 240–248 (2017)CrossRefGoogle Scholar
  8. 8.
    H.Y. Wang, X.J. Ma, C.M. Mo, Determination of sugar components and contents in fruit flesh of Siraitia grosvenorii. Guihaia 35, 775–781 (2015)Google Scholar
  9. 9.
    C.H. Zhang, Y.H. Yun, Z.M. Zhang, Y.Z. Liang, Simultaneous determination of neutral and uronic sugars based on UV-Vis spectrometry combined with PLS. Int. J. Biol. Macromol. 87, 290–294 (2016)CrossRefGoogle Scholar
  10. 10.
    M. Dubois, K.A. Gilles, J.K. Hamiton, P.A. Rebers, F. Smith, Colorimetric method for determination of sugars and related substances. Anal. Chem. 28, 350–356 (1956)CrossRefGoogle Scholar
  11. 11.
    H.W. Zheng, Q. Zhang, J.P. Quan, Q. Zheng, W.P. Xi, Determination of sugars, organic acids, aroma components, and carotenoids in grapefruit pulps. Food Chem. 205, 112–121 (2016)CrossRefGoogle Scholar
  12. 12.
    R. Redaelli, M. Alfieri, G. Cabassi, Development of a NIRS calibration for total antioxidant capacity in maize germplasm. Talanta 154, 164–168 (2016)CrossRefGoogle Scholar
  13. 13.
    V. Muresan, S. Danthine, A.E. Muresan, E. Racolta, C. Blecker, S. Muste, C. Socaciu, V. Baeten, In situ analysis of lipid oxidation in oilseed-based food products using near-infrared spectroscopy and chemometrics: the sunflower kernel paste (tahini) example. Talanta 155, 336–346 (2016)CrossRefGoogle Scholar
  14. 14.
    X.X. Ding, Y. Guo, Y.N. Ni, S. Kokot, A novel NIR spectroscopic method for rapid analyses of lycopene, total acid, sugar, phenols and antioxidant activity in dehydrated tomato samples. Vib. Spectrosc. 82, 1–9 (2016)CrossRefGoogle Scholar
  15. 15.
    G.A. Oliveira, S. Bureau, C.M.C. Renard, A.B. Pereira-Netto, F. Castilhos, Comparison of NIRS approach for prediction of internal quality traits in three fruit species. Food Chem. 143, 223–230 (2014)CrossRefGoogle Scholar
  16. 16.
    M. Mehrübeoĝlu, G.L. Coté, Determination of total reducing sugars in potato samples using near-infrared spectroscopy. Cereal Foods Worlds. 42, 409–413 (1997)Google Scholar
  17. 17.
    A.M. Rady, D.E. Guyer, Evaluation of sugar content in potatoes using NIR reflectance and wavelength selection techniques. Postharvest Biol. Technol. 103, 17–26 (2015)CrossRefGoogle Scholar
  18. 18.
    S. Minaei, H. Bagherpour, M. Abdollahian Noghabi, M.E. Khorasani-Fardvani, F. Forughimanesh, A comparative study concerning linear and nonlinear models to determine sugar content in sugar beet by near infrared spectroscopy (NIR). J. Food Biosci. Technol. 6, 13–22 (2016)Google Scholar
  19. 19.
    R. Leardi, A.L. Gonzalez, Genetic algorithms applied to feature selection in PLS regression: how and when to use them. Chemom. Intell. Lab. Syst. 41, 195–207 (1998)CrossRefGoogle Scholar
  20. 20.
    R. Leardi, M.B. Seasholtz, R.J. Pell, Variable selection for multivariate calibration using a genetic algorithm: prediction of addictive concentrations in polymer films from Fourier transform-infrared spectral data. Anal. Chim. Acta 461, 189–200 (2002)CrossRefGoogle Scholar
  21. 21.
    H.D. Li, Y.Z. Liang, Q.S. Xu, D.S. Cao, Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Anal. Chim. Acta 648, 77–84 (2009)CrossRefGoogle Scholar
  22. 22.
    B.C. Deng, Y.H. Yun, Y.Z. Liang, A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling. Analyst 139, 4836–4845 (2014)CrossRefGoogle Scholar
  23. 23.
    B.M. Nicolai, K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K.I. Theron, J. Lammertyn, Non-destructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol. Technol. 46, 99–118 (2007)CrossRefGoogle Scholar
  24. 24.
    Q. Li, X.Z. Yu, J.M. Gao, A novel method to determine total sugar of Goji berry using FT-NIR spectroscopy with effective wavelength selection. Int. J. Food Prop. 20, 1–11 (2017)CrossRefGoogle Scholar
  25. 25.
    R. Lu, predicting firmness and sugar content of sweet cherries using near-infrared diffuse reflectance spectroscopy. Am. Soc. Agric. Eng. 44, 1265–1271 (2001)Google Scholar
  26. 26.
    Y.H. Qin, H.L. Gong, NIR models for predicting total sugar in tobacco for samples with different physical states. Infrared Phys. Technol. 77, 239–243 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Guangxi College and University for Food Safety and Pharmaceutical Chemistry, Guangxi Key Laboratory of Chemistry and Engineering of Forest Products, School of Chemistry and Chemical EngineeringGuangxi University for NationalitiesNanningPeople’s Republic of China
  2. 2.Research Centre for Analysis and MeasurementKunming University of Science and TechnologyKunmingPeople’s Republic of China

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