Open Access
Issue
Ciência Téc. Vitiv.
Volume 29, Number 1, 2014
Page(s) 35 - 43
DOI https://doi.org/10.1051/ctv/20142901035
Published online 08 August 2014
  • Abdi H., Williams L.J., 2010. Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat., 2, 433–459. [CrossRef] [Google Scholar]
  • Alamprese C., Casale M., Sinelli N., Lanteri S., Casiraghi E., 2013. Detection of minced beef adulteration with turkey meat by UV-vis, NIR and MIR spectroscopy. Lwt-Food Sci. Technol., 53, 225–232. [CrossRef] [Google Scholar]
  • Astray G., Castillo J.X., Ferreiro-Lage J.A., Galvez J.F., Mejuto J.C., 2010. Artificial neural networks: A promising tool to evaluate the authenticity of wine redes neuronales: Una herramienta prometedora para evaluar la autenticidad del vino. Cyta-J. Food, 8, 79–86. [CrossRef] [Google Scholar]
  • Atanacković M., Petrović A., Jović S., Bukarica L.G., Bursać M., Cvejić J., 2012. Influence of winemaking techniques on the resveratrol content, total phenolic content and antioxidant potential of red wines. Food Chem., 131, 513–518. [CrossRef] [Google Scholar]
  • Callejón R.M., Amigo J.M., Pairo E., Garmón S., Ocaña J.A., Morales M.L., 2012. Classification of sherry vinegars by combining multidimensional fluorescence, parafac and different classification approaches. Talanta, 88, 456–462. [CrossRef] [PubMed] [Google Scholar]
  • Canaza-Cayo A., Cozzolino D., Alomar D., Quispe E., 2012. A feasibility study of the classification of alpaca (< i> lama pacos) wool samples from different ages, sex and color by means of visible and near infrared reflectance spectroscopy. Comput. Electron. Agr., 88, 141–147. [CrossRef] [Google Scholar]
  • Caruso M., Galgano F., Morelli M.A.C., Viggiani L., Lencioni L., Giussani B., Favati F., 2012. Chemical profile of white wines produced from ‘greco bianco’ grape variety in different italian areas by nuclear magnetic resonance (NMR) and conventional physicochemical analyses. J. Agric. Food Chem., 60, 7–15. [CrossRef] [PubMed] [Google Scholar]
  • Cetó X., Gutiérrez-Capitán M., Calvo D., Del Valle M., 2013. Beer classification by means of a potentiometric electronic tongue. Food Chem., 141, 2533–2540. [CrossRef] [PubMed] [Google Scholar]
  • Chauchard F., Cogdill R., Roussel S., Roger J., Bellon-Maurel V., 2004. Application of LS-SVM to non-linear phenomena in NIR spectroscopy: Development of a robust and portable sensor for acidity prediction in grapes. Chemometr. Intell. Lab., 71, 141–150. [CrossRef] [Google Scholar]
  • Chouchouli V., Kalogeropoulos N., Konteles S.J., Karvela E., Makris D.P., Karathanos V.T., 2013. Fortification of yoghurts with grape (Vitis vinifera) seed extracts. Lwt-Food Sci. Technol., 53, 522–529. [CrossRef] [Google Scholar]
  • Cozzolino D., Cynkar W.U., Shah N., Smith P., 2011. Multivariate data analysis applied to spectroscopy: Potential application to juice and fruit quality. Food Res. Int., 44, 1888–1896. [CrossRef] [Google Scholar]
  • Cozzolino D., Kwiatkowski M.J., Parker M., Cynkar W.U., Dambergs R.G., Gishen M., Herderich M.J., 2004. Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy. Anal. Chim. Acta, 513, 73–80. [CrossRef] [Google Scholar]
  • Cozzolino D., McCarthy J., Bartowsky E., 2012. Comparison of near infrared and mid infrared spectroscopy to discriminate between wines produced by different Oenococcus oeni strains after malolactic fermentation: A feasibility study. Food Control, 26, 81–87. [CrossRef] [Google Scholar]
  • Cozzolino D., Smyth H., Gishen M., 2003. 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., 51, 7703–7708. [CrossRef] [PubMed] [Google Scholar]
  • Ferrer-Gallego R., Hernández-Hierro J.M., Rivas-Gonzalo J.C., Escribano-Bailón M.T., 2012. A comparative study to distinguish the vineyard of origin by NIRS using entire grapes, skins and seeds. J. Sci. Food Agr., 93, 967–972. [CrossRef] [Google Scholar]
  • Ferrer-Gallego R., Hernandez-Hierro J.M., Rivas-Gonzalo J.C., Escribano-Bailon M.T., 2011. Multivariate analysis of sensory data of Vitis vinifera L. cv. graciano during ripening. correlation with the phenolic composition of the grape skins. Cyta-J. Food, 9, 290–294. [CrossRef] [Google Scholar]
  • Figueiredo-González M., Simal-Gándara J., Boso S., Martínez M.C., Santiago J.L., Cancho-Grande B., 2012. Anthocyanins and flavonols berries from Vitis vinifera L. cv. brancellao separately collected from two different positions within the cluster. Food Chem., 135, 47–56. [CrossRef] [Google Scholar]
  • Fudge A.L., Wilkinson K.L., Ristic R., Cozzolino D., 2011. Classification of smoke tainted wines using mid-infrared spectroscopy and chemometrics. J. Agric. Food Chem., 60, 52–59. [CrossRef] [PubMed] [Google Scholar]
  • Fudge A.L., Wilkinson K.L., Ristic R., Cozzolino D., 2013. Synchronous two-dimensional MIR correlation spectroscopy (2DCOS) as a novel method for screening smoke tainted wine. Food Chem., 139, 115–119. [CrossRef] [PubMed] [Google Scholar]
  • Garde-Cerdán T., Lorenzo C., Alonso G.L., Salinas M.R., 2012a. Review of the use of near infrared spectroscopy to determine different wine parameters: Discrimination between wines. Curr Bioact Compd., 8, 353–369. [CrossRef] [Google Scholar]
  • Garde-Cerdán T., Lorenzo C., Zalacain A., Alonso G.L., Salinas M.R., 2012b. Using near infrared spectroscopy to determine haloanisoles and halophenols in barrel aged red wines. Lwt-Food Sci. Technol., 46, 401–405. [CrossRef] [EDP Sciences] [Google Scholar]
  • Kečkeš S., Gašić U., Veličković T.Ć., Milojković-Opsenica D., Natić M., Tešić Ž., 2013. The determination of phenolic profiles of serbian unifloral honeys using ultra-high-performance liquid chromatography/high resolution accurate mass spectrometry. Food Chem., 138, 32–40. [CrossRef] [PubMed] [Google Scholar]
  • Luna A.S., da Silva A.P., Ferre J., Boque R., 2013. Classification of edible oils and modeling of their physico-chemical properties by chemometric methods using mid-IR spectroscopy. Spectrochim. Acta A, 100, 109–114. [CrossRef] [Google Scholar]
  • Martelo-Vidal M., Domínguez-Agis F., Vázquez M., 2013. Ultraviolet/visible/near-infrared spectral analysis and chemometric tools for the discrimination of wines between subzones inside a controlled designation of origin: A case study of rías baixas. Aust. J. Grape Wine Res., 19, 62–67. [CrossRef] [Google Scholar]
  • Martelo-Vidal M., Vázquez M., 2014a. Determination of polyphenolic compounds of red wines by UV–VIS–NIR spectroscopy and chemometrics tools. Food Chem., 158, 28–34. [CrossRef] [PubMed] [Google Scholar]
  • Martelo-Vidal M., Vázquez M., 2014b. Application of artificial neural networks coupled to UV–VIS–NIR spectroscopy for the rapid quantification of wine compounds in aqueous mixtures. Cyta-J. Food (In press. http://dx.doi.org/10.1080/19476337.2014.908955.. [Google Scholar]
  • Martelo-Vidal M., Vázquez M., 2014c. Evaluation of ultraviolet, visible and near infrared spectroscopy for the analysis of wine compounds. Czech J. Food Sci., 32, 37–47. [Google Scholar]
  • Perez Trujillo J.P., Perez Pont M.L., Conde Gonzalez J.E., 2011. Content of mineral ions in wines from canary islands (spain). Cyta-J. Food, 9, 135–140. [CrossRef] [Google Scholar]
  • Pizarro C., Rodríguez-Tecedor S., Pérez-del-Notario N., Esteban-Díez I., González-Sáiz J.M., 2013. Classification of spanish extra virgin olive oils by data fusion of visible spectroscopic fingerprints and chemical descriptors. Food Chem., 138, 915–922. [CrossRef] [PubMed] [Google Scholar]
  • Rios-Corripio M.A., Rojas Lopez M., Delgado Macuil R., 2012. Analysis of adulteration in honey with standard sugar solutions and syrups using attenuated total reflectance-Fourier Transform Infrared spectroscopy and multivariate methods. Cyta-J. Food, 10, 119–122. [CrossRef] [Google Scholar]
  • Rohman A., Man Y.B.C., 2011. Analysis of chicken fat as adulterant in cod liver oil using Fourier Transform Infrared (FTIR) spectroscopy and chemometrics. Cyta-J. Food, 9, 187–191. [CrossRef] [Google Scholar]
  • Serranti S., Cesare D., Marini F., Bonifazi G., 2013. Classification of oat and groat kernels using NIR hyperspectral imaging. Talanta, 103, 276–284. [CrossRef] [PubMed] [Google Scholar]
  • Shen F., Li F., Liu D., Xu H., Ying Y., Li B., 2012a. Ageing status characterization of chinese rice wines using chemical descriptors combined with multivariate data analysis. Food Control, 25, 458–463. [CrossRef] [Google Scholar]
  • Shen F., Yang D., Ying Y., Li B., Zheng Y., Jiang T., 2012b. Discrimination between shaoxing wines and other Chinese rice wines by near-infrared spectroscopy and chemometrics. Food and Bioprocess Tech., 5, 786–795. [CrossRef] [Google Scholar]
  • Tarantilis P.A., Troianou V.E., Pappas C.S., Kotseridis Y.S., Polissiou M.G., 2008. Differentiation of Greek red wines on the basis of grape variety using attenuated total reflectance Fourier Transform Infrared Spectroscopy. Food Chem., 111, 192–196. [CrossRef] [Google Scholar]
  • Toaldo I.M., Fogolari O., Pimentel G.C., de Gois J.S., Borges D.L.G., Caliari V., Bordignon-Luiz M., 2013. Effect of grape seeds on the polyphenol bioactive content and elemental composition by ICP-MS of grape juices from Vitis labrusca L. Lwt-Food Sci. Technol., 53, 1–8. [CrossRef] [Google Scholar]

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