Developing an Excitation-Emission Matrix Fluorescence Spectroscopy Method Coupled with Multi-way Classification Algorithms for the Identification of the Adulteration of Shanxi Aged Vinegars

  • Tian-Qin Peng
  • Xiao-Li YinEmail author
  • Weiqing Sun
  • Baomiao Ding
  • Li-An Ma
  • Hui-Wen GuEmail author


In this paper, an excitation-emission matrix (EEM) fluorescence spectroscopy method coupled with two kinds of multi-way classification algorithms was developed for the identification of the adulteration of Shanxi aged vinegars. The first classification model was built from selective information provided by parallel factor analysis (PARAFAC) based on partial least squares-discriminant analysis (PLS-DA) algorithm and the other one was modeled based on strictly multi-way classification algorithm multi-way partial least squares-discriminant analysis (N-PLS-DA). Both classification models showed good clustering tendency for authentic and adulterated Shanxi aged vinegars. Compared with the PARAFAC-PLS-DA method, more accurate and reliable classification results were provided by the N-PLS-DA method because it is a strictly multi-way classification method and can make the utmost use of the EEM fluorescence information of the vinegar samples. The N-PLS-DA model constructed from the EEM spectra enabled the recognition of adulterated samples with accurate rate of 100% both in training and prediction sets and obtained maximum sensitivity and specificity. This study showed that EEM fluorescence spectroscopy combined with multi-way classification algorithm N-PLS-DA could be used as a good method for the identification of the adulteration of Shanxi aged vinegar samples.


Excitation-emission matrix fluorescence Multi-way classification methods Food adulteration Shanxi aged vinegars 


Funding Information

This work received financial supports from the National Natural Science Foundation of China (Grant No. 31701693), the Hubei Provincial Natural Science Foundation of China (Grant No. 2018CFB165), and the Doctoral Scientific Research Startup Foundation of Yangtze University (Grant Nos. 801090010134 and 801100010140).

Compliance with Ethical Standards

Conflict of Interest

Tian-Qin Peng declares that she has no conflict of interest. Xiao-Li Yin declares that she has no conflict of interest. Weiqing Sun declares that she has no conflict of interest. Baomiao Ding declares that he has no conflict of interest. Li-An Ma declares that she has no conflict of interest. Hui-Wen Gu declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human and animal subjects.

Informed Consent

Not applicable.


  1. Chen HY, Chen T, Giudici P, Chen FS (2016a) Vinegar functions on health: constituents, sources, and formation mechanisms. Compr Rev Food Sci 15:1124–1138CrossRefGoogle Scholar
  2. Chen HY, Zhou YX, Shao YC, Chen FS (2016b) Free phenolic acids in shanxi aged vinegar: changes during aging and synergistic antioxidant activities. Int J Food Prop 19:1183–1193CrossRefGoogle Scholar
  3. Xiong C, Zheng YJ, Xing YN, Chen SJ, Zeng YT, Ruan GH (2016) Discrimination of two kinds of geographical origin protected chinese vinegars using the characteristics of aroma compounds and multivariate statistical analysis. Food Anal Methods 9:768–776CrossRefGoogle Scholar
  4. Zhou Z, Liu S, Kong X, Ji Z, Han X, Wu J, Mao J (2017) Elucidation of the aroma compositions of Zhenjiang aromatic vinegar using comprehensive two dimensional gas chromatography coupled to time-of-flight mass spectrometry and gas chromatography-olfactometry. J Chromatogr A 1487:218–226CrossRefGoogle Scholar
  5. Xiong C, Su Z, Zhezng Y, Wang Q, Ling Y, Liu Z, Li Y, Zhang J, Yang G, Zhang X (2017) Characterization of the thermal degradation of vinegar and the construction of an identification model for chinese geographical indication vinegars by the Py-GC-MS Technique. J AOAC Int 100:503–509CrossRefGoogle Scholar
  6. Lu HS, An ZG, Jiang HY, Ying YB (2011) Discrimination between mature vinegars of different geographical origins by NIRS in: computer and computing technologies in agriculture IV. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 729–736Google Scholar
  7. Papotti G, Bertelli D, Graziosi R, Maietti A, Tedeschi P, Marchetti A, Plessi M (2015) Traditional balsamic vinegar and balsamic vinegar of Modena analyzed by nuclear magnetic resonance spectroscopy coupled with multivariate data analysis. LWT Food Sci Technol 60:1017–1024CrossRefGoogle Scholar
  8. Jo Y, Chung N, Park S, Noh BS, Jeong YJ, Kwon JH (2016) Application of E-tongue, E-nose, and MS-E-nose for discriminating aged vinegars based on taste and aroma profiles. Food Sci Biotechnol 25:1313–1318CrossRefGoogle Scholar
  9. Le Moigne M, Dufour E, Bertrand D, Maury C, Seraphin D, Jourjon F (2008) Front face fluorescence spectroscopy and visible spectroscopy coupled with chemometrics have the potential to characterise ripening of Cabernet Franc grapes. Anal Chim Acta 621:8–18CrossRefGoogle Scholar
  10. Christensen J, Nørgaard L, Bro R, Engelsen SB (2006) Multivariate autofluorescence of intact food systems. Chem Rev 106:1979–1994CrossRefGoogle Scholar
  11. Lenhardt Acković L, Zeković I, Dramićanin T, Bro R, Dramićanin MD (2018) Modeling food fluorescence with PARAFAC. In: Geddes CD (ed) Reviews in Fluorescence 2017. Springer International Publishing, Cham, pp 161–197CrossRefGoogle Scholar
  12. Kumar K, Tarai M, Mishra AK (2017) Unconventional steady-state fluorescence spectroscopy as an analytical technique for analyses of complex-multifluorophoric mixtures. TrAC, Trends Anal Chem 97:216–243CrossRefGoogle Scholar
  13. Callejón RM, Amigo JM, Pairo E, Garmón S, Ocaña JA, Morales ML (2012) Classification of Sherry vinegars by combining multidimensional fluorescence, parafac and different classification approaches. Talanta 88:456–462CrossRefGoogle Scholar
  14. Hu LQ, Ma S, Yin CL (2018a) Discrimination of geographical origin and detection of adulteration of kudzu root by fluorescence spectroscopy coupled with multi-way pattern recognition. Spectrochim. Acta A 193:87–94CrossRefGoogle Scholar
  15. Sádecká J, Uríčková V, Májek P, Jakubíková M (2019) Comparison of different fluorescence techniques in brandy classification by region of production. Spectrochim. Acta A 216:125–135CrossRefGoogle Scholar
  16. Hu Y, Wu HL, Yin XL, Gu HW, Liu Z, Xiao R, Xie LX, Fang H, Yu RQ (2018b) A flexible and novel strategy of alternating trilinear decomposition method coupled with two-dimensional linear discriminant analysis for three-way chemical data analysis: characterization and classification. Anal Chim Acta 1021:28–40CrossRefGoogle Scholar
  17. Lenhardt L, Bro R, Zeković I, Dramićanin T, Dramićanin MD (2015) Fluorescence spectroscopy coupled with PARAFAC and PLS DA for characterization and classification of honey. Food Chem 175:284–291CrossRefGoogle Scholar
  18. Ríos-Reina R, Elcoroaristizabal S, Ocaña-González JA, García-González DL, Amigo JM, Callejón RM (2017) Characterization and authentication of Spanish PDO wine vinegars using multidimensional fluorescence and chemometrics. Food Chem 230:108–116CrossRefGoogle Scholar
  19. Ren MM, Wang XY, Tian CR, Li XJ, Zhang BS, Song XZ, Zhang J (2017) Characterization of organic acids and phenolic compounds of cereal vinegars and fruit vinegars in China. J Food Process Preserv 41:e12937CrossRefGoogle Scholar
  20. Bro R (1997) PARAFAC. Tutorial and applications. Chemom Intell Lab Syst 38:149–171CrossRefGoogle Scholar
  21. Nocairi H, Mostafa Qannari E, Vigneau E, Bertrand D (2005) Discrimination on latent components with respect to patterns. Application to multicollinear data. Comput Statist Data Anal 48:139–147CrossRefGoogle Scholar
  22. Lenhardt L, Zeković I, Dramićanin T, Milićević B, Burojević J, Dramićanin MD (2017) Characterization of cereal flours by fluorescence spectroscopy coupled with PARAFAC. Food Chem 229:165–171CrossRefGoogle Scholar
  23. Hu LQ, Yin CL (2017) Development of a new three-dimensional fluorescence spectroscopy method coupling with multilinear pattern recognition to discriminate the variety and grade of green tea. Food Anal Methods 10:2281–2292CrossRefGoogle Scholar
  24. Silva AC, Soares SFC, Insausti M, Galvão RKH, Band BSF, Araújo MCU (2016) Two-dimensional linear discriminant analysis for classification of three-way chemical data. Anal Chim Acta 938:53–62CrossRefGoogle Scholar
  25. Arancibia JA, Boschetti CE, Olivieri AC, Escandar GM (2008) Screening of oil samples on the basis of excitation−emission room-temperature phosphorescence data and multiway chemometric techniques. Introducing the second-order advantage in a classification study. Anal Chem 80:2789–2798CrossRefGoogle Scholar
  26. Azcarate SM, de Araújo GA, Muñoz de la Peña A, Goicoechea HC (2018) Modeling second-order data for classification issues: data characteristics, algorithms, processing procedures and applications. TrAC, Trends Anal. Chem. 107:151–168Google Scholar
  27. Brasca R, Goicoechea HC, Culzoni MJ (2018) Chapter 6 - multiway calibration approaches for quality control of food samples. In: Grumezescu AM, Holban AM (eds) Food safety and preservation. Academic Press, New York, pp 143–165CrossRefGoogle Scholar
  28. Ouertani SS, Mazerolles G, Boccard J, Rudaz S, Hanafi M (2014) Multi-way PLS for discrimination: compact form equivalent to the tri-linear PLS2 procedure and its monotony convergence. Chemom Intell Lab Syst 133:25–32CrossRefGoogle Scholar
  29. Andersson CA, Bro R (2000) The N-way toolbox for MATLAB. Chemom Intell Lab Syst 52:1–4CrossRefGoogle Scholar
  30. Durante C, Bro R, Cocchi M (2011) A classification tool for N-way array based on SIMCA methodology. Chemom Intell Lab Syst 106:73–85CrossRefGoogle Scholar
  31. Li M, Yuan BZ (2005) 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recogn Lett 26:527–532CrossRefGoogle Scholar
  32. Ballabio D, Consonni V (2013) Classification tools in chemistry. Part 1: linear models. PLS-DA. Anal. Methods 5:3790–3798Google Scholar
  33. Bahram M, Bro R, Stedmon C, Afkhami A (2006) Handling of rayleigh and raman scatter for PARAFAC modeling of fluorescence data using interpolation. J Chemom 20:99–105CrossRefGoogle Scholar
  34. Bro R, Kiers HAL (2003) A new efficient method for determining the number of components in PARAFAC models. J Chemom 17:274–286CrossRefGoogle Scholar
  35. Mazina J, Vaher M, Kuhtinskaja M, Poryvkina L, Kaljurand M (2015) Fluorescence, electrophoretic and chromatographic fingerprints of herbal medicines and their comparative chemometric analysis. Talanta 139:233–246CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.College of Life SciencesYangtze UniversityJingzhouChina
  2. 2.Jingchu Food Research and Development CenterYangtze UniversityJingzhouChina
  3. 3.College of Chemistry and Environmental EngineeringYangtze UniversityJingzhouChina

Personalised recommendations