rudnitskaya

Analysis of port wines using the electronic tongue: 

Analysis of port wines using the electronic tongue Alisa Rudnitskaya1, Ivonne Delgadillo2, Andrey Legin1, Silvia Rocha2, Anne-Marie Da Costa2, Tomás Simoes3 1Chemistry Department, St. Petersburg University, Russia; www.elecronictongue.com 2Chemistry Department, University of Aveiro, Portugal 3Instituto do Vinho do Porto, Porto, Portugal

Port wine making procedure: 

Port wine making procedure

Port wine producing region: 

Port wine producing region

Port wine producing region: 

Port wine producing region

Port wine styles: 

Port wine styles Ruby Bottle aged Ruby, Ruby reserve (2-3 years in the cask) Tawny, Tawny reserve (min 6 years in the cask) LBV (4-6 years in the cask) Tawny with an Indication of Age (10, 20, 30 or 40 years in the cask) Vintage (2-3 years in the cask) Colheita (min 7 years in the cask) Tawny Cask aged

Purpose of the study: 

Purpose of the study Development of the rapid analytical methodology for the assessment of the port wine age Evaluation of the electronic tongue multisensor system (ET) for the determination of the port wine age Comparison between ET and conventional chemical analysis data for the determination of the port wine age Evaluation of the orthogonal signal correction for the data filtering

Experimental: 

Experimental Samples 146 samples of port wine, in particular, wines aged in oak casks for 10, 20, 30 and 40 years, Vintage, LBV and Colheita (harvest) wines of age varying from 2 to 70 years. All port wine samples together with chemical analysis results were obtained from Instituto do Vinho Do Porto Measurements Electronic tongue Sensor array of 28 potentiometric chemical sensors with both chalcogenide glass and polymeric membranes Direct measurements without sample preparation Chemical analysis using conventional analytical techniques (provided by Instituto de Vinho de Porto) 32 parameters including content of sugar (ºBé), ashes, reducible sugar, total SO2 and sulphates, tartaric and malic acids, alcohols (ethanol, methanol, glycerol, 1 and 2-butanol, 1-propanol, isopropanol, amyl and allyl alcohols), ethanal, ethyl acetate, volatile and total acidity, Foline index, density, dry extract, etc.

ExperimentalData processing: 

Experimental Data processing PCA Recognition of samples and data exploration PLS regression Calibration models for prediction of port wine age ET and chemical analysis data Raw and OSC filtered data Test set validation, 1/3 of the samples were used as tests OSC Applied for filtering of ET and chemical analysis data Software used Unscrambler v. 7.8 by CAMO AS SIMCA-P v.11.0 by Umetrics

Orthogonal Signal Correction : 

Orthogonal Signal Correction Wold S, Antti H, Lindgren F, Öhman J, Chemometrics Intell Lab. Syst. 44 (1998) 175-185 Aim – removal of variation in X that is not correlated with Y prior to modeling to = Xwo, which is orthogonal to Y AND provides good modeling and prediction of X po' = to‘X XOSC = X – Σto*po‘

PCAChemical analysis data: 

PCA Chemical analysis data Good correlation between chemical analysis data and port wine age Clustering according to port wine type – good separation between blended tawnies and LBV and vintage wines

PCAET data: 

PCA ET data No good separation of port wines according to their age Clustering according to port wine type Significant temporary drift in the data

Prediction of the port wine agePLS regression on the raw data: 

Prediction of the port wine age PLS regression on the raw data ET Chemical analysis PCs in the model - 2 RMSEC 5.3 RMSEP 5.4

OSC filtering of the data: 

OSC filtering of the data

OSC filtering of the dataRMSEP: 

OSC filtering of the data RMSEP ET data Chemical analysis data

Effect of OSC filtering of ET data: 

Effect of OSC filtering of ET data

Effect of OSC filtering on ET data: 

Effect of OSC filtering on ET data

Conclusions: 

Conclusions Port wine age can be predicted using both electronic tongue and conventional chemical analysis data with the same precision of about 5 years. Electronic tongue response has shown a temporary drift in port wines, especially pronounced during first days of measuring session Data pretreatment using OSC was favorable for ET data successfully removing time dependence and producing improved calibration models Port wine sample can be separated according to their types using both ET and conventional chemical analysis data.