Skip to main content
Log in

Discrimination of Chinese traditional soy sauces based on their physico-chemical properties

  • Articles
  • Published:
Science China Chemistry Aims and scope Submit manuscript

Abstract

This work aimed to classify the categories (produced by different processes) and brands (obtained from different geographical origins) of Chinese soy sauces. Nine variables of physico-chemical properties (density, pH, dry matter, ashes, electric conductivity, amino nitrogen, salt, viscosity and total acidity) of 53 soy sauce samples were measured. The measured data was submitted to such pattern recognition as cluster analysis (CA), principal component analysis (PCA), discrimination partial least squares (DPLS), linear discrimination analysis (LDA) and K-nearest neighbor (KNN) to evaluate the data patterns and the possibility of differentiating Chinese soy sauces between different categories and brands. Two clusters corresponding to the two categories were obtained, and each cluster was divided into three subsets corresponding to three brands by the CA method. The variables for LDA and KNN were selected by the Fisher F-ratio approach. The prediction ability of all classifiers was evaluated by cross-validation. For the three supervised discrimination analyses, LDA and KNN gave 100% predications according to the sample category and brand.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Pizarro C, Esteban-Diez I, Saenz-Gonzalez C, Gonzalez-Saiz JM. Vinegar classification based on feature extraction and selection from headspace solid-phase microextraction/gas chromatography volatile analyses: a feasibility study. Anal Chim Acta, 2008, 608(1): 38–47

    Article  CAS  Google Scholar 

  2. Yamaguchi N, Fujimaki M. Studies on browning reaction products from reducing sugars and amino acid: part 14. Antioxidative activities of purified melanoidin and their comparison with those of legal antioxidants. J Food Sci Technol, 1974, 21: 6–12

    Google Scholar 

  3. Kataoka S. Functional effects of Japanese style fermented soy sauce (shoyu) and its components. J Biosci Bioeng, 2005, 100(3): 227–234

    Article  CAS  Google Scholar 

  4. Masino F, Chinnici F, Franchini GC, Ulrici A, Antonelli A. A study of the relationships among acidity, sugar, and furanic compound concentrations in set of casks for Aceto Balsamico Tradizionale of Reggio Emilia by multivariate techniques. Food Chem, 2005, 92(4): 673–679

    Article  CAS  Google Scholar 

  5. Lachman J, Kolihova D, Miholova D, Kosata J, Titera D, Kult K. Analysis of minority honey component: possible use for the evaluation of honey quality. Food Chem, 2007, 101(3): 973–979

    Article  CAS  Google Scholar 

  6. Angeles Herrador M, Gustavo Gonzalez A. Pattern recognition procedures for differentiation of Green, Black and Oolong teas according to their metal content from inductively coupled plasma atomic emission spectrometry. Talanta, 2001, 53(6): 1249–1257

    Article  Google Scholar 

  7. Casale M, Armanino C, Casolino C, Forina M. Combining information from headspace mass spectrometry and visible spectroscopy in the classification of the Ligurian olive oils. Anal Chim Acta, 2007, 589(1): 89–95

    Article  CAS  Google Scholar 

  8. Inon FA, Garrigues S, Guardia M. Combination of mid- and near-infrared spectroscopy for the determination of the quality properties of beers. Anal Chim Acta, 2006, 571(2): 167–174

    Article  CAS  Google Scholar 

  9. Baeten V, Dardenne P, Aparicio R. Interpretation of Fourier transform Raman spectra of the unsaponifiable matter in a selection of edible oils. J Agric Food Chem, 2001, 49(11): 5098–5107

    Article  CAS  Google Scholar 

  10. Zhang GW, Ni YN, Churchill J, Kokot S, Authentication of vegetable oils on the basis of their physico-chemical properties with the aid of chemometrics. Talanta, 2006, 70(2): 293–300

    Article  CAS  Google Scholar 

  11. Masino F, Chinnici F, Bendini A, Montevecchi G, Antonelli A. A study on relationship among chemical, physical, and qualitative assessment in traditional balsamic vinegar. Food Chem, 2008, 106(1): 90–95

    Article  CAS  Google Scholar 

  12. Zhang L, Lu ZY, Li SQ, Yue Y. Fermented vinegar. Beijing: Chinese Standards Press, 2000

    Google Scholar 

  13. Ding XY, Hu KQ, Zhong GS, Zhu H, Bai XG. Method for analysis of hygienic standard of soybean sauce. Beijing: Chinese Standards Press, 2003

    Google Scholar 

  14. Gao XW, Yang HR, Wu YX, Lv GF. Determination of ash in foods. Beijing: Chinese Standards Press, 2003

    Google Scholar 

  15. Geladi P. Kowalski BR. Partial least-squares regression: a tutorial. Anal Chim Acta, 1986, 185: 1–17

    Article  CAS  Google Scholar 

  16. Hartigan JA. Clustering algorithms. New York: Wiley, 1975

    Google Scholar 

  17. Malinowski ER. Factor Analysis In Chemistry. 3rd ed. New York: John Wiley & Sons, 2002

    Google Scholar 

  18. Jolliffe IT. Principal Component Analysis. New York: Springer-Verlag, 1986

    Google Scholar 

  19. Coomans D, Jonckheer M, Massart DL, Broeckaert I, Blockx P. The application of linear discriminant analysis in the diagnosis of thyroid disease. Anal Chim Acta, 1978, 103: 409–415

    Article  CAS  Google Scholar 

  20. Massart DL, Vandeginste BGM, Deming SN, Michotte Y, Kaufman L. Chemometrics: A Textbook. Amsterdam: Elsevier Science Publishers, 1988. 395–397

    Google Scholar 

  21. Goutte C. Note on free lunches and cross-validation. Neural Comput, 1997, 9(6): 1245–1249

    Article  Google Scholar 

  22. Berrueta LA, Alonso-Salces RM, Heberger K. Supervised pattern recognition in food analysis. J Chromatogr A, 2007, 1158(1–2): 196–214

    Article  CAS  Google Scholar 

  23. Sharaf MA, Illman DL, Kowalski BR. Chemometrics. New York: John wiley & Sons, 1986

    Google Scholar 

  24. Fernandez-Torres R, Perez-Bernal JL, Bello-Lopez MA, Callejon-Mochon M, Jimenez-Sanchez JC, Guiraum-Perez A. Mineral content and botanical origin of Spanish honeys. Talanta, 2005, 65(3): 686–691

    Article  CAS  Google Scholar 

  25. Marini F, Magri AL, Balestrieri F, Fabretti F, Marini D. Supervised pattern recognition applied to the discrimination of the floral origin of six types of Italian honey samples. Anal Chim Acta, 2004, 515: 117–125

    Article  CAS  Google Scholar 

  26. Varmuza K. Pattern Recognition In Chemistry. Heideberg. Springer Verlag, 1980

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to YongNian Ni.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, Y., Ni, Y. & Kokot, S. Discrimination of Chinese traditional soy sauces based on their physico-chemical properties. Sci. China Chem. 53, 1406–1413 (2010). https://doi.org/10.1007/s11426-010-3163-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11426-010-3163-4

Keywords

Navigation