Skip to main content
SpringerLink
Log in

Discrimination and Measurements of Three Flavonols with Similar Structure Using Terahertz Spectroscopy and Chemometrics

  • Published:
Journal of Infrared, Millimeter, and Terahertz Waves Aims and scope Submit manuscript

Abstract

Terahertz (THz) technique, a recently developed spectral method, has been researched and used for the rapid discrimination and measurements of food compositions due to its low-energy and non-ionizing characteristics. In this study, THz spectroscopy combined with chemometrics has been utilized for qualitative and quantitative analysis of myricetin, quercetin, and kaempferol with concentrations of 0.025, 0.05, and 0.1 mg/mL. The qualitative discrimination was achieved by KNN, ELM, and RF models with the spectra pre-treatments. An excellent discrimination (100% CCR in the prediction set) could be achieved using the RF model. Furthermore, the quantitative analyses were performed by partial least square regression (PLSR) and least squares support vector machine (LS-SVM). Comparing to the PLSR models, the LS-SVM yielded better results with low RMSEP (0.0044, 0.0039, and 0.0048), higher Rp (0.9601, 0.9688, and 0.9359), and higher RPD (8.6272, 9.6333, and 7.9083) for myricetin, quercetin, and kaempferol, respectively. Our results demonstrate that THz spectroscopy technique is a powerful tool for identification of three flavonols with similar chemical structures and quantitative determination of their concentrations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. M. G. L. Hertzog, P. C. H. Hollman, & M. B. Katan, “Content of potentially anticarcinogenic flavonoids of 28 vegetables and 9 fruits commonly consumed in the Netherlands”. Journal of Agricultural and Food Chemistry, vol. 40, pp. 2379–2383, 1992.

    Article  Google Scholar 

  2. J. D. Kim, L. Liu, W. Guo, & M. Meydani, “Chemical structure of flavonols in relation to modulation of angiogenesis and immune-endothelial cell adhesion”. The Journal of Nutritional Biochemistry, vol. 17(3), pp. 165–176, 2006.

    Article  Google Scholar 

  3. United States Department of Agriculture. USDA database for the flavonoid content of selected foods. Available from: https://www.ars.usda.gov/northeast-area/beltsville-md/beltsville-human-nutrition-research-center/nutrient-data-laboratory/docs/usda-special-interest-databases-on-flavonoids. pdf. Accessed 30.05.17, 2015.

  4. R. Puupponen-Pimiä, L. Nohynek, C. Meier, M. Kähkönen, M. Heinonen, A. Hopia, & K.-M. Oksman-Caldentey. “Antimicrobial properties of phenolic compounds from berries”. Journal of applied microbiology, vol. 90(4), pp. 494–507, 2001.

    Article  Google Scholar 

  5. M. Škerget, P. Kotnik, M. Hadolin, A. R. Hraš, M. Simonič, & Ž. Knez, “Phenols, proanthocyanidins, flavones and flavonols in some plant materials and their antioxidant activities”. Food Chemistry, vol. 89(2), pp. 191–198, 2005.

    Article  Google Scholar 

  6. H. P. Kim, K. H. Son, H. W. Chang, & S. S. Kang, “Anti-inflammatory plant flavonoids and cellular action mechanisms”. Journal of Pharmacological Sciences, vol. 96(3), pp. 229–245, 2004.

    Article  Google Scholar 

  7. J. Lu, L. V. Papp, J. Fang, S. Rodriguez-Nieto, B. Zhivotovsky, & A. Holmgren, “Inhibition of mammalian thioredoxin reductase by some flavonoids: implications for myricetin and quercetin anticancer activity”. Cancer Research, vol. 66(8), pp. 4410–4418, 2006.

    Article  Google Scholar 

  8. S. S. Pekkarinen, I. M. Heinonen, & A. I. Hopia, “Flavonoids quercetin, myricetin, kaemferol and (+)-catechin as antioxidants in methyl linoleate”. Journal of the Science of Food and Agriculture, vol. 79(4), 499–506, 1999.

    Article  Google Scholar 

  9. D. Labbé, M. Provençal, S. Lamy, D. Boivin, D. Gingras, & R. Béliveau, “The flavonols quercetin, kaempferol, and myricetin inhibit hepatocyte growth factor-induced medulloblastoma cell migration”. The Journal of Nutrition, vol. 139(4), pp. 646–652, 2009.

    Article  Google Scholar 

  10. P. Cos, L. Ying, M. Calomme, J. P. Hu, K. Cimanga, P. B. Van, L. Pieters, A. J. Vlietinck, & B. D. Vanden, “Structure- activity relationship and classification of flavonoids as inhibitors of xanthine oxidase and superoxide scavengers”. Journal of Natural Products, vol. 61(1), pp. 71–76, 1998.

    Article  Google Scholar 

  11. M. Woillez, & J. M. Merillon, “Comparative study of antioxidant properties and total phenolic content of 30 plant extracts of industrial interest using DPPH, ABTS, FRAP, SOD, and ORAC assays”. Journal of Agriculture and Food Chemistry, vol. 57, pp. 1768–1774, 2009.

    Article  Google Scholar 

  12. H. Bae, G. K. Jayaprakasha, J. Jifon, & B. S. Patil, “Extraction efficiency and validation of an HPLC method for flavonoid analysis in peppers”. Food Chemistry, vol. 130(3), pp. 751–758, 2012.

    Article  Google Scholar 

  13. M. R. Sohrabi, & G. Darabi, “The application of continuous wavelet transform and least squares support vector machine for the simultaneous quantitative spectrophotometric determination of Myricetin, Kaempferol and Quercetin as flavonoids in pharmaceutical plants”. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 152, pp. 443–452, 2016.

    Article  Google Scholar 

  14. Y. Sun, N. Fang, D. D. Chen, & K. K. Donkor, “Determination of potentially anti-carcinogenic flavonoids in wines by micellar electrokinetic chromatography”. Food Chemistry, vol. 106(1), pp. 415–420, 2008.

    Article  Google Scholar 

  15. L. Yan, C. Xiong, H. Qu, C. Liu, W. Chen, & L. Zheng, “Non-destructive determination and visualisation of insoluble and soluble dietary fibre contents in fresh-cut celeries during storage periods using hyperspectral imaging technique”. Food Chemistry, vol. 228, pp. 249–256, 2017.

    Article  Google Scholar 

  16. C. Xiong, C. Liu, W. Pan, F. Ma, C. Xiong, L. Qi, F. Chen, X. Lu, J. Yang, L. Zheng, “Non-destructive Determination of Total Polyphenols Content and Classificationof Storage Periods of Iron Buddha Tea Using Multispectral Imaging System”. Food Chemistry, vol. 176, pp. 130–136, 2015.

    Article  Google Scholar 

  17. U. Khulal, J. Zhao, W. Hu, & Q. Chen, “Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms”. Food Chemistry, vol. 197, pp. 1191–1199, 2016.

    Article  Google Scholar 

  18. C. Liu, W. Liu, W. Chen, J. Yang, & L. Zheng, “Feasibility in multispectral imaging for predicting the content of bioactive compounds in intact tomato fruit”. Food Chemistry, vol. 173, pp. 482–488, 2015.

    Article  Google Scholar 

  19. M. C. Beard, G. M. Turner, & C. A. Schmuttenmaer, “Terahertz spectroscopy”. Journal of Physical Chemistry B, vol. 106(29), pp. 7146–7159, 2002.

    Article  Google Scholar 

  20. M. Song, F. Yang, L. Liu, L. Shen, P. Hu, & F. Han, “Chemical Identification of Non-Esterified Catechins by Terahertz Time Domain Spectroscopy”. Journal of Nanoscience and Nanotechnology, vol. 16(12), pp. 12208–12213, 2016.

    Article  Google Scholar 

  21. A. Pohl, N. Deßmann, K. Dutzi, & H. W. Hübers, “Identification of unknown substances by terahertz spectroscopy and multivariate data analysis”. Journal of Infrared Millimeter & Terahertz Waves, vol. 37(2), pp. 1–14, 2016.

    Article  Google Scholar 

  22. Y. Ueno, R. Rungsawang, I. Tomita, & K. Ajito, “Quantitative measurements of amino acids by terahertz time-domain transmission spectroscopy”. Analytical Chemistry, vol. 78(15), pp. 5424–5428, 2006.

    Article  Google Scholar 

  23. S. H. Baek, H. K. Ju, Y. H. Hwang, M. O. Kang, K. Kwak, & H. S. Chun, Detection of methomyl, a carbamate insecticide, in food matrices using terahertz time-domain spectroscopy. Journal of Infrared Millimeter & Terahertz Waves, vol. 37(5), pp. 486–497, 2016.

    Article  Google Scholar 

  24. W. Liu, C. Liu, X. Hu, J. Yang, & L. Zheng, “Application of terahertz spectroscopy imaging for discrimination of transgenic rice seeds with chemometrics”. Food Chemistry, vol. 210, pp. 415–421, 2016.

    Article  Google Scholar 

  25. J. Liu, Z. Li, F. Hu, T. Chen, Y. Du, & H. Xin, “Identification of transgenic organisms based on terahertz spectroscopy and hyper sausage neuron”. Journal of Applied Spectroscopy, vol. 82, pp. 104–110, 2015.

    Article  Google Scholar 

  26. L. Duvillaret, F. Garet, & J. L. Coutaz, “Highly precise determination of optical constants and sample thickness in terahertz time-domain spectroscopy”. Applied Optics, vol. 38(2), pp. 409–415, 1999.

    Article  Google Scholar 

  27. A. Candolfi, R. De Maesschalck, D. Jouan-Rimbaud, P. A. Hailey, & D. L. Massart, “The influence of data pre-processing in the pattern recognition of excipients near-infrared spectra”. Journal of Pharmaceutical and Biomedical Analysis, vol. 21(1), pp. 115–132, 1999.

    Article  Google Scholar 

  28. Q. Chen, J. Zhao, Z. Chen, H. Lin, & D.A. Zhao,"Discrimination of green tea quality using the electronic nose technique and the human panel test, comparison of linear and nonlinear classification tools". Sensors and Actuators B: Chemical, vol. 159(1), 294–300, 2011.

    Article  Google Scholar 

  29. P. J. García-Laencina, J. L. Sancho-Gómez, A. R. Figueiras-Vidal, & M. Verleysen, “K-nearest neighbours with mutual information for simultaneous classification and missing data imputation”. Neurocomputing, vol. 72(7), pp. 1483–1493, 2009.

    Article  Google Scholar 

  30. Q. Chen, J. Ding, J. Cai, & J. Zhao, "Rapid measurement of total acid content (TAC) in vinegar using near infrared spectroscopy based on efficient variables selection algorithm and nonlinear regression tools". Food Chemistry, vol. 135(2), 590–595, 2012.

    Article  Google Scholar 

  31. B. Ayerdi, & M. Graña, “Hyperspectral image nonlinear unmixing and reconstruction by ELM regression ensemble”. Neurocomputing, vol. 174, pp. 299–309, 2016.

    Article  Google Scholar 

  32. H. Martens, & M. Martens, “Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR)”. Food Quality and Preference, vol. 11(1), pp. 5–16, 2000.

    Article  Google Scholar 

  33. M. M. Adankon, & M. Cheriet, “Model selection for the LS-SVM Application to handwriting recognition”. Pattern Recognition, vol. 42(12), pp. 3264–3270, 2009.

    Article  MATH  Google Scholar 

  34. Q. Chen, P. Jiang, & J. Zhao, “Measurement of total flavone content in snow lotus (Saussurea involucrate) using near infrared spectroscopy combined with interval PLS and genetic algorithm”. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 76(1), pp. 50–55, 2010.

    Article  MathSciNet  Google Scholar 

  35. C. Liu, G. Hao, M. Su, Y. Chen, & L. Zheng, "Potential of multispectral imaging combined with chemometric methods for rapid detection of sucrose adulteration in tomato paste". Journal of Food Engineering, vol. 215, 78–83, 2017.

    Article  Google Scholar 

  36. M. Walther, B. M. Fischer, & P. U. Jepsen, “Noncovalent intermolecular forces in polycrystalline and amorphous saccharides in the far infrared”. Chemical Physics, vol. 288(2), pp. 261–268, 2003.

    Article  Google Scholar 

  37. L. Liu, R. Pathak, L. J. Cheng, & T. Wang, “Real-time frequency-domain terahertz sensing and imaging of isopropyl alcohol-water mixtures on a microfluidic chip”. Sensors and Actuators B: Chemical, vol. 184, 228–234, 2013

    Article  Google Scholar 

  38. C. Rønne, & S. R. Keiding, “Low frequency spectroscopy of liquid water using THz-time domain spectroscopy”. Journal of Molecular Liquids, vol. 101 (1–3), 199–218, 2002.

    Article  Google Scholar 

  39. M. L. T. Asaki, A. Redondo, T. A. Zawodzinski, & A. J. Taylor, “Dielectric relaxation and underlying dynamics of acetonitrile and 1-ethyl-3-methylimidazolium triflate mixtures using THz transmission spectroscopy”. The Journal of Chemical Physics, vol. 116(23), 10377–10385, 2002.

    Article  Google Scholar 

  40. A. Escarpa, & M. C. Gonzalez, "High-performance liquid chromatography with diode-array detection for the determination of phenolic compounds in peel and pulp from different apple varieties". Journal of chromatography A, vol. 823(1), 331–337, 1998.

    Article  Google Scholar 

  41. X. Hu, W. Lang, W. Liu, X. Xu, J. Yang, & L. Zheng, “A non-destructive terahertz spectroscopy-based method for transgenic rice seed discrimination via sparse representation”. Journal of Infrared Millimeter & Terahertz Waves, vol. 38, pp. 1–12, 2017.

    Article  Google Scholar 

Download references

Acknowledgements

This study is supported by the National Key Research and Development Plan of China (2016YFD0401104), the National Natural Science Foundation of China (31401544), the Funds for Huangshan Professorship of Hefei University of Technology (407-037019), the Key Science and Technology Specific Projects of Anhui Province (16030701078), and the Fundamental Research Funds for the Central Universities (JZ2016HGTB0712, JZ2017HGTB0195).

Author information

Author notes

  1. Ling Yan and Changhong Liu contributed equally to this work.

Authors and Affiliations

  1. School of Food Science and Engineering, Hefei University of Technology, Hefei, 230009, China

    Ling Yan, Changhong Liu & Lei Zheng

  2. School of Biological and Medical Engineering, Hefei University of Technology, Hefei, 230009, China

    Hao Qu

  3. Intelligent Control and Compute Vision Lab, Hefei University, Hefei, 230601, China

    Wei Liu

  4. Hebei Food Inspection and Research Institute and Hebei Key Laboratory of Food Safety, Shijiazhuang, 050051, China

    Yan Zhang

  5. Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei, 230031, China

    Jianbo Yang

Authors
  1. Ling Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Changhong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hao Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jianbo Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lei Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lei Zheng.

Electronic Supplementary Material

ESM 1

(DOCX 224 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, L., Liu, C., Qu, H. et al. Discrimination and Measurements of Three Flavonols with Similar Structure Using Terahertz Spectroscopy and Chemometrics. J Infrared Milli Terahz Waves 39, 492–504 (2018). https://doi.org/10.1007/s10762-018-0474-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10762-018-0474-6

Keywords

Search

Navigation