Exploration of liquid chromatographic-diode array data for Argentinean wines by extended multivariate curve resolution

Chemometrics and Intelligent Laboratory Systems

Volumn 132, Issue , 2014, Pages 1-7

  Pisano, Pablo L ^a   Silva, María F ^b   Olivieri, Alejandro C ^a  

^a Universidad Nacional de Rosario   (Argentina)

^b Inst de Biologia Agricola de Mendoza Consejo Nacional de Investigaciones Cientificas y Tecnicas   (Argentina)

Author keywords

Direct injection; Liquid chromatography; Multivariate curve resolution; Principal component analysis; Wine data exploration

Indexed keywords

ALGORITHM; ANALYTIC METHOD; ARGENTINA; ARTICLE; CONCENTRATION (PARAMETERS); CONTROLLED STUDY; DATA ANALYSIS; DIODE ARRAY DETECTION; ELUTION; EXTENDED MULTIVARIATE CURVE RESOLUTION; FOOD ANALYSIS; FOOD COMPOSITION; GEOGRAPHIC ORIGIN; GRAPE; HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; RADIATION ABSORPTION; REGRESSION ANALYSIS; STATISTICAL ANALYSIS; ULTRAVIOLET SPECTROSCOPY; VARIETAS; WINE;

EID: 84892666783     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2013.12.010     Document Type: Article

References (40)

1. Jackson R.S. 6- Chemical constituents of grapes and wine. Wine Science 2008, 270-331. Academic Press, San Diego. Third ed.
2. Moreno-Arribas M.V., Polo M.C. 9- Phenolic compounds. Wine Chemistry and Biochemistry 2009, 437-527. Springer, New York. M.V. Moreno-Arribas, M.C. Polo (Eds.).
3. Moreno-Arribas M.V., Polo M.C. 9D- Influence of phenolics on wine organoleptic properties. Wine Chemistry and Biochemistry 2009, 529-570. Springer, New York. M.V. Moreno-Arribas, M.C. Polo (Eds.).
4. Saurina J. Characterization of wines using compositional profiles and chemometrics. Trends Anal. Chem. 2010, 29:234-245.
5. Tredoux A., de Villiers A., Májek P., Lynen F.d.r., Crouch A., Sandra P. Stir bar sorptive extraction combined with GC-MS analysis and chemometric methods for the classification of South African wines according to the volatile composition. J. Agric. Food Chem. 2008, 56:4286-4296.
6. Ballabio D., Skov T., Leardi R., Bro R. Classification of GC-MS measurements of wines by combining data dimension reduction and variable selection techniques. J. Chemom. 2008, 22:457-463.
7. Alves R.F., Nascimento A.M.D., Nogueira J.M.F. Characterization of the aroma profile of Madeira wine by sorptive extraction techniques. Anal. Chim. Acta. 2005, 546:11-21.
8. Villiers A.d., Vanhoenacker G., Majek P., Sandra P. Determination of anthocyanins in wine by direct injection liquid chromatography-diode array detection-mass spectrometry and classification of wines using discriminant analysis. J. Chromatogr. A 2004, 1054:195-204.
9. Fanzone M., Peña-Neira Á., Jofré V., Assof M., Zamora F. Phenolic characterization of Malbec wines from mendoza province (Argentina). J. Agric. Food Chem. 2010, 58:2388-2397.
10. Vaclavik L., Lacina O., Hajslova J., Zweigenbaum J. The use of high performance liquid chromatography-quadrupole time-of-flight mass spectrometry coupled to advanced data mining and chemometric tools for discrimination and classification of red wines according to their variety. Anal. Chim. Acta. 2011, 685:45-51.
11. Cuadros-Inostroza A., Giavalisco P., Hummel J., Eckardt A., Willmitzer L., Peña-Cortés H. Discrimination of wine attributes by metabolome analysis. Anal. Chem. 2010, 82:3573-3580.
12. Jaitz L., Siegl K., Eder R., Rak G., Abranko L., Koellensperger G., Hann S. LC-MS/MS analysis of phenols for classification of red wine according to geographic origin, grape variety and vintage. Food Chem. 2010, 122:366-372.
13. 1H NMR-based metabonomics for the classification of Greek wines according to variety, region, and vintage. Comparison with HPLC data. J. Agric. Food Chem. 2009, 57:11067-11074.
14. 1H NMR-based metabolomic study. Anal. Chim. Acta. 2009, 648:71-76.
15. Cozzolino D., Smyth H.E., Gishen M. Feasibility study on the use of visible and near-infrared spectroscopy together with chemometrics to discriminate between commercial white wines of different varietal origins. J. Agric. Food Chem. 2003, 51:7703-7708.
16. Liu L., Cozzolino D., Cynkar W.U., Dambergs R.G., Janik L., O'Neill B.K., Colby C.B., Gishen M. Preliminary study on the application of visible-near infrared spectroscopy and chemometrics to classify Riesling wines from different countries. Food Chem. 2008, 106:781-786.
17. Garrido-Delgado R., López-Vidal S., Arce L., Valcárcel M. Differentiation and identification of white wine varieties by using electropherogram fingerprints obtained with CE. J. Sep. Sci. 2009, 32:3809-3816.
18. Pazourek J., Gajdošová D., Spanilá M., Farková M., Novotná K., Havel J. Analysis of polyphenols in wines: correlation between total polyphenolic content and antioxidant potential from photometric measurements: Prediction of cultivars and vintage from capillary zone electrophoresis fingerprints using artificial neural network. J. Chromatogr. A 2005, 1081:48-54.
19. Fabani M.P., Arrúa R.C., Vázquez F., Diaz M.P., Baroni M.V., Wunderlin D.A. Evaluation of elemental profile coupled to chemometrics to assess the geographical origin of Argentinean wines. Food Chem. 2010, 119:372-379.
20. Pohl P. What do metals tell us about wine?. Trends Anal. Chem. 2007, 26:941-949.
21. Makris D.P., Kallithraka S., Mamalos A. Differentiation of young red wines based on cultivar and geographical origin with application of chemometrics of principal polyphenolic constituents. Talanta 2006, 70:1143-1152.
22. Beltrán N.H., Duarte-Mermoud M.a., Bustos M.a., Salah S.a., Loyola E.a., Peña-Neira A., Jalocha J.W. Feature extraction and classification of Chilean wines. J. Food Eng. 2006, 75:1-10.
23. Bellomarino S.a., Conlan X.a., Parker R.M., Barnett N.W., Adams M.J. Geographical classification of some Australian wines by discriminant analysis using HPLC with UV and chemiluminescence detection. Talanta 2009, 80:833-838.
24. Fanzone M., Peña-Neira Á., Gil M., Jofré V., Assof M., Zamora F. Impact of phenolic and polysaccharidic composition on commercial value of Argentinean Malbec and Cabernet Sauvignon wines. Food Res. Int. 2012, 45:402-414.
25. Serrano-Lourido D., Saurina J., Hernández-Cassou S., Checa A. Classification and characterisation of Spanish red wines according to their appellation of origin based on chromatographic profiles and chemometric data analysis. Food Chem. 2012, 135:1425-1431.
26. Salvatore E., Cocchi M., Marchetti A., Marini F., de Juan A. Determination of phenolic compounds and authentication of PDO Lambrusco wines by HPLC-DAD and chemometric techniques. Anal. Chim. Acta. 2013, 761:34-45.
27. Mancha de Llanos A., Zan M.M., Culzoni M.J., Espinosa-Mansilla A., Cañada-Cañada F., Peña A.M., Goicoechea H.C. Determination of marker pteridines in urine by HPLC with fluorimetric detection and second-order multivariate calibration using MCR-ALS. Anal. Bioanal. Chem. 2011, 399:2123-2135.
28. Culzoni M.J., Mancha de Llanos A., De Zan M.M., Espinosa-Mansilla A., Cañada-Cañada F., Muñoz de la Peña A., Goicoechea H.C. Enhanced MCR-ALS modeling of HPLC with fast scan fluorimetric detection second-order data for quantitation of metabolic disorder marker pteridines in urine. Talanta 2011, 85:2368-2374.
29. Bloemberg T.G., Gerretzen J., Lunshof A., Wehrens R., Buydens L.M.C. Warping methods for spectroscopic and chromatographic signal alignment: a tutorial. Anal. Chim. Acta. 2013, 781:14-32.
30. van Mispelaar V.G., Tas A.C., Smilde A.K., Schoenmakers P.J., van Asten A.C. Quantitative analysis of target components by comprehensive two-dimensional gas chromatography. J. Chromatogr. A 2003, 1019:15-29.
31. Tomasi G., van den Berg F., Andersson C. Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data. J. Chemom. 2004, 18:231-241.
32. Jaumot J., Gargallo R., de Juan A., Tauler R. A graphical user-friendly interface for MCR-ALS: a new tool for multivariate curve resolution in MATLAB. Chemom. Intell. Lab. Syst. 2005, 76:101-110.
33. Maeder M., Zilian A. Evolving factor analysis, a new multivariate technique in chromatography. Chemom. Intell. Lab. Syst. 1988, 3:205-213.
34. Maeder M. Evolving factor analysis for the resolution of overlapping chromatographic peaks. Anal. Chem. 1987, 59:527-530.
35. Windig W., Guilment J. Interactive self-modeling mixture analysis. Anal. Chem. 1991, 63:1425-1432.
36. Tauler R., Smilde A., Kowalski B. Selectivity, local rank, three-way data analysis and ambiguity in multivariate curve resolution. J. Chemom. 1995, 9:31-58.
37. Tauler R., Maeder M., de Juan A. 2.24- Multiset data analysis: extended multivariate curve resolution. Comprehensive chemometrics 2009, 473-505. Elsevier, Oxford. D.B. Stephen, T. Romà, W. Beata (Eds.).
38. Jolliffe I.T. Principal Component Analysis 2002, Springer, New York. 2nd ed.
39. Tomasi G., Savorani F., Engelsen S.B. icoshift: an effective tool for the alignment of chromatographic data. J. Chromatogr. A 2011, 1218:7832-7840.
40. Nielsen N.-P.V., Carstensen J.M., Smedsgaard J. Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping. J. Chromatogr. A 1998, 805:17-35.