Quality grade discrimination of Chinese strong aroma type liquors using mass spectrometry and multivariate analysis

Food Research International

Volumn 54, Issue 2, 2013, Pages 1753-1760

  Cheng, Pingyan ^a   Fan, Wenlai ^a   Xu, Yan ^a  

^a JIANGNAN UNIVERSITY   (China)

Author keywords

Head space solid phase microextraction mass spectrometry; Partial least squares regression; Principal component regression; Quality grade discrimination; Regression coefficients; Support vector machine

Indexed keywords

DISCRIMINATION ACCURACY; FOOD QUALITY CONTROLS; MULTI VARIATE ANALYSIS; PARTIAL LEAST SQUARES REGRESSION; PREDICTION ACCURACY; PRINCIPAL COMPONENT REGRESSION; QUALITY GRADE; REGRESSION COEFFICIENT;

CALIBRATION; EXTRACTION; IONS; LEAST SQUARES APPROXIMATIONS; MASS SPECTROMETRY; ODORS; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES;

LOADING;

EID: 84887175705     PISSN: 09639969     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.foodres.2013.09.002     Document Type: Article

References (29)

1. Balabin R.M., Lomakina E.I. Support vector machine regression (SVR/LS-SVM)-An alternative to neural networks (ANN) for analytical chemistry-Comparison of nonlinear methods on near infrared (NIR) spectroscopy data. Analyst 2011, 136(8):1703-1712.
2. Belousov A., Verzakov S., Von Frese J. Applicational aspects of support vector machines. Journal of Chemometrics 2002, 16(8-10):482-489.
3. Berna A.Z., Trowell S., Clifford D., Cynkar W., Cozzolino D. Geographical origin of Sauvignon Blanc wines predicted by mass spectrometry and metal oxide based electronic nose. Analytica Chimica Acta 2009, 648(2):146-152.
4. Besten M.A., Jasinski V.C., Costa Â.D.G., Nunes D.S., Sens S.L., Wisniewski A., et al. Chemical composition similarity between the essential oils isolated from male and female specimens of each five Baccharis species. Journal of the Brazilian Chemical Society 2012, 23(6):1041-1047.
5. Braga C.M., Zielinski A.A.F., Silva K.M.D., de Souza F.K.F., Pietrowski G.D.A.M., Couto M., et al. Classification of juices and fermented beverages made from unripe, ripe and senescent apples based on the aromatic profile using chemometrics. Food Chemistry 2013, 141(2):967-974.
6. Cadena R.S., Cruz A.G., Faria J.A.F., Bolini H.M.A. Reduced fat and sugar vanilla ice creams: Sensory profiling and external preference mapping. Journal of Dairy Science 2012, 95(9):4842-4850.
7. Cherkassky V., Ma Y. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 2004, 17(1):113-126.
8. Colombani C., Croiseau P., Fritz S., Guillaume F., Legarra A., Ducrocq V., et al. A comparison of partial least squares (PLS) and sparse PLS regressions in genomic selection in French dairy cattle. Journal of Dairy Science 2012, 95(4):2120-2131.
9. Cruz A.G., Cadena R.S., Faria J.A.F., Oliveira C.A.F., Cavalcanti R.N., Bona E., et al. Consumer acceptability and purchase intent of probiotic yoghurt with added glucose oxidase using sensometrics, artificial neural networks and logistic regression. International Journal of Dairy Technology 2011, 64(4):549-556.
10. Cynkar W., Dambergs R., Smith P., Cozzolino D. Classification of Tempranillo wines according to geographic origin: Combination of mass spectrometry based electronic nose and chemometrics. Analytica Chimica Acta 2010, 660(1-2):227-231.
11. Fan W., Qian M.C. Headspace solid phase microextraction and gas chromatography-olfactometry dilution analysis of young and aged Chinese "Yanghe Daqu" liquors. Journal of Agricultural and Food Chemistry 2005, 53(20):7931-7938.
12. Fan W., Qian M.C. Identification of aroma compounds in Chinese 'Yanghe Daqu' liquor by normal phase chromatography fractionation followed by gas chromatography/olfactometry. Flavour and Fragrance Journal 2006, 21(2):333-342.
13. Fang Y., Park J.I., Jeong Y.-S., Jeong M.K., Baek S.H., Cho H.W. Enhanced predictions of wood properties using hybrid models of PCR and PLS with high-dimensional NIR spectral data. Annals of Operations Research 2011, 190(1):3-15.
14. Granato D., Uchida Katayama F.C., de Castro I.A. Characterization of red wines from South America based on sensory properties and antioxidant activity. Journal of the Science of Food and Agriculture 2012, 92(3):526-533.
15. Höskuldsson A. Variable and subset selection in PLS regression. Chemometrics and Intelligent Laboratory Systems 2001, 55(1):23-38.
16. Jelen H.H., Ziolkowska A., Kaczmarek A. Identification of the botanical origin of raw spirits produced from rye, potato, and corn based on volatile compounds analysis using a SPME-MS method. Journal of Agricultural and Food Chemistry 2010, 58(24):12585-12591.
17. Liu L., Cozzolino D., Cynkar W., Gishen M., Colby C.B. Geographic classification of Spanish and Australian Tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis. Journal of Agricultural and Food Chemistry 2006, 54(18):6754-6759.
18. Masuda S., Ozaki K., Kuriyama H., Sugimoto T., Shoji H., Tanabe M., et al. Classification of barley shochu samples produced using submerged culture and solid-state culture of koji mold by solid-phase microextraction and gas chromatography-mass spectrometry. Journal of the Institute of Brewing 2010, 116:170-176.
19. Møller J.K.S., Catharino R.R., Eberlin M.N. Electrospray ionization mass spectrometry fingerprinting of whisky: Immediate proof of origin and authenticity. Analyst 2005, 130(6):890-897.
20. Ochi H., Sumi M., Nakata I., Saito H., Uozumi M., Iwatsuki K. Sensometric calibration of sensory characteristics of commercially available milk products with instrumental data. Journal of Dairy Science 2010, 93(5):1794-1806.
21. Pereira A.C., Reis M.S., Saraiva P.M., Marques J.C. Analysis and assessment of Madeira wine ageing over an extended time period through GC-MS and chemometric analysis. Analytica Chimica Acta 2010, 660(1-2):8-21.
22. Pontes M., Santos S., Araújo M., Almeida L., Lima R., Gaiao E., et al. Classification of distilled alcoholic beverages and verification of adulteration by near infrared spectrometry. Food Research International 2006, 39(2):182-189.
23. Qin H., Huo D., Zhang L., Yang L., Zhang S., Yang M., et al. Colorimetric artificial nose for identification of Chinese liquor with different geographic origins. Food Research International 2012, 45:45-51.
24. Sádecká J., Tóthová J., Májek P. Classification of brandies and wine distillates using front face fluorescence spectroscopy. Food Chemistry 2009, 117(3):491-498.
25. Sampaio O.M., Reche R.V., Franco D.W. Chemical profile of rums as a function of their origin. The use of chemometric techniques for their identification. Journal of Agricultural and Food Chemistry 2008, 56(5):1661-1668.
26. Tóthová J., Žiak Ľ., Sádecká J. Characterization and classification of distilled drinks using total luminescence and synchronous fluorescence spectroscopy. Acta Chimica Slovenica 2008, 1(1):265-275.
27. Venkatesan P., Dharuman C., Gunasekaran S. A comparative study of principal component regression and partial least squares regression with application to FTIR diabetes data. Indian Journal of Science and Technology 2011, 740-746.
28. Xu Y., Zomer S., Brereton R.G. Support vector machines: A recent method for classification in chemometrics. Critical Reviews in Analytical Chemistry 2006, 36(3-4):177-188.
29. Yu H., Zhou Y., Fu X., Xie L., Ying Y. Discrimination between Chinese rice wines of different geographical origins by NIRS and AAS. European Food Research and Technology 2006, 225(3-4):313-320.