TY - JOUR
T1 - Direct matrix assisted laser desorption ionization mass spectrometry-based analysis of wine as a powerful tool for classification purposes
AU - Nunes-Miranda, J. D.
AU - Santos, Hugo M.
AU - Reboiro-Jato, Miguel
AU - Fdez-Riverola, Florentino
AU - Igrejas, G.
AU - Lodeiro, Carlos
AU - Capelo, J. L.
N1 - Financial support from the Spanish Ministry of Science and Innovation, from the Instituto da Vinha e do Vinho de Portugal and from the University of Vigo under projects ref. AGL2009-09796, ref. 20/2010/SIA and refs. INOU 11B-01 and CO-110-10 respectively, is acknowledged. J. L Capelo and Carlos Lodeiro, acknowledges the Isidro Parga Pondal research program from the Xunta de Galicia, Spain. H. M. Santos acknowledges the post-doctoral grant SRFH/BPD/73997/2010 provided by the FCT-MCTES (Fundacao para a Ciencia e a Tecnologia-Ministerio da Ciencia, Tecnologia e Ensino Superior, Portugal). M. Reboiro-Jato was supported by a pre-doctoral fellowship from University of Vigo. J.D. Nunes-Miranda thanks Scientific Association PROTEOMASS for financial support given.
PY - 2012/3/15
Y1 - 2012/3/15
N2 - The variables affecting the direct matrix assisted laser desorption ionization mass spectrometry-based analysis of wine for classification purposes have been studied. The type of matrix, the number of bottles of wine, the number of technical replicates and the number of spots used for the sample analysis have been carefully assessed to obtain the best classification possible. Ten different algorithms have been assessed as classification tools using the experimental data collected after the analysis of fourteen types of wine. The best matrix was found to be α-Cyano with a sample to matrix ratio of 1:0.75. To correctly classify the wines, profiling a minimum of five bottles per type of wine is suggested, with a minimum of three MALDI spot replicates for each bottle. The best algorithm to classify the wines was found to be Bayes Net.
AB - The variables affecting the direct matrix assisted laser desorption ionization mass spectrometry-based analysis of wine for classification purposes have been studied. The type of matrix, the number of bottles of wine, the number of technical replicates and the number of spots used for the sample analysis have been carefully assessed to obtain the best classification possible. Ten different algorithms have been assessed as classification tools using the experimental data collected after the analysis of fourteen types of wine. The best matrix was found to be α-Cyano with a sample to matrix ratio of 1:0.75. To correctly classify the wines, profiling a minimum of five bottles per type of wine is suggested, with a minimum of three MALDI spot replicates for each bottle. The best algorithm to classify the wines was found to be Bayes Net.
KW - Classification
KW - Fingerprinting
KW - MALDI
KW - Wine
UR - http://www.scopus.com/inward/record.url?scp=84857788669&partnerID=8YFLogxK
U2 - 10.1016/j.talanta.2012.01.017
DO - 10.1016/j.talanta.2012.01.017
M3 - Article
C2 - 22365682
AN - SCOPUS:84857788669
SN - 0039-9140
VL - 91
SP - 72
EP - 76
JO - Talanta
JF - Talanta
ER -