Principal component analysis applied to Fourier transform infrared spectroscopy for the design of calibration sets for glycerol prediction models in wine and for the detection and classification of outlier samples

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dc.contributor.authorNieuwoudt, Heleneen_ZA
dc.contributor.authorPrior, B. A.en_ZA
dc.contributor.authorPretorius, I. S.en_ZA
dc.contributor.authorManley, M.en_ZA
dc.contributor.authorBauer, Florianen_ZA
dc.date.accessioned2011-05-15T15:56:52Z
dc.date.available2011-05-15T15:56:52Z
dc.date.issued2004
dc.description.abstractPrincipal component analysis (PCA) was used to identify the main sources of variation in the Fourier transform infrared (FT-IR) spectra of 329 wines of various styles. The FT-IR spectra were gathered using a specialized WineScan instrument. The main sources of variation included the reducing sugar and alcohol content of the samples, as well as the stage of fermentation and the maturation period of the wines. The implications of the variation between the different wine styles for the design of calibration models with accurate predictive abilities were investigated using glycerol calibration in wine as a model system. PCA enabled the identification and interpretation of samples that were poorly predicted by the calibration models, as well as the detection of individual samples in the sample set that had atypical spectra (i.e., outlier samples). The Soft Independent Modeling of Class Analogy (SIMCA) approach was used to establish a model for the classification of the outlier samples. A glycerol calibration for wine was developed (reducing sugar content < 30 g/L, alcohol > 8% v/v) with satisfactory predictive ability (SEP = 0.40 g/L). The RPD value (ratio of the standard deviation of the data to the standard error of prediction) was 5.6, indicating that the calibration is suitable for quantification purposes. A calibration for glycerol in special late harvest and noble late harvest wines (RS 31-147 g/L, alcohol > 11.6% v/v) with a prediction error SECV = 0.65 g/L, was also established. This study yielded an analytical strategy that combined the careful design of calibration sets with measures that facilitated the early detection and interpretation of poorly predicted samples and outlier samples in a sample set. The strategy provided a powerful means of quality control, which is necessary for the generation of accurate prediction data and therefore for the successful implementation of FT-IR in the routine analytical laboratory.
dc.description.versionArticle
dc.identifier.citationJournal of Agricultural and Food Chemistry
dc.identifier.citation52
dc.identifier.citation12
dc.identifier.issn218561
dc.identifier.other10.1021/jf035431q
dc.identifier.urihttp://hdl.handle.net/10019.1/10085
dc.subjectalcohol
dc.subjectglycerol
dc.subjectaccuracy
dc.subjectanalytic method
dc.subjectanalytical error
dc.subjectarticle
dc.subjectcalibration
dc.subjectfermentation
dc.subjectinfrared spectroscopy
dc.subjectlaboratory test
dc.subjectmodel
dc.subjectprincipal component analysis
dc.subjectwine
dc.subjectCalibration
dc.subjectGlycerol
dc.subjectPrincipal Component Analysis
dc.subjectSpectroscopy, Fourier Transform Infrared
dc.subjectWine
dc.titlePrincipal component analysis applied to Fourier transform infrared spectroscopy for the design of calibration sets for glycerol prediction models in wine and for the detection and classification of outlier samples
dc.typeArticle
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