Thesis (MScAgric)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: The WineScan FT120 is widely used in wine laboratories across South Africa. The WineScan FT120 uses Fourier transform infrared (FT-IR) spectroscopy with multivariate data analysis to correlate spectra with chemical compositional data. Ready-to-use, commercially available calibration models for a FT-IR spectroscopy instrument are an advantage for unskilled users and routine analysis. Introducing spirit products to this technology introduced new interferences, which necessitated vastly different calibrations models to compensate for the changes. Accuracy, precision and ruggedness of the reference methods validated during method validation, verified the suitability of the reference methods used to quantify the parameters in question before calibration model building was attempted. Various principal component analysis (PCA) were performed prior to the calibration step with the aim to identify outliers and inspect groupings. PCA models could identify samples with atypical spectra and differentiate between product types. Two tactics regarding data sets for calibration set-up was experimented with, all the products together and calibration models per product. Partial least squares (PLS) regression was used to establish the calibration models for ethanol, density, obscuration and colour. With all the calibration models, the calibration models based on the product specific data sets, achieved better predicting statistics. The best performing ethanol calibration models achieved Residual mean square error of prediction (RMSEP) = 0.038 to 0.106 %v/v and showed significant improvement on previously reported prediction errors by Lachenmeier (2007). The results for the density calibration showed a similar trend, with the product specific calibration models outperforming the calibration model when all samples were included into one calibration model. This study produced novel results for quantification of obscuration (RMSEP = 0.10 and 0.09 in blended brandies and potstill brandies, respectively) and colour (RMSEP < 2.286 gold units) of brandies and whiskies. The correlation coefficients (R²) between true and predicted values, for the four parameters tested, indicated good to excellent precision (0.8 < R² < 1.0). Minimising the variation between the samples of the data set, gave more accurate regression statistics, but this resulted in a lower residual predictive deviation (RPD) value (< 5) that indicated models were not suitable for quantification. Adding more samples per product will add more variability into a data set per product, increase the SD and result in an increase in the RPD. The results pave the way for the development of calibration models for the quantification of other parameters for specific products. Following the groupings of product types, further classifications of brandy brands were investigated. PCA plots showed clear separation between potstill brandies and blended brandies and some degree of clustering between some of the blended brands was observed. Classification of brandies were investigated using the Soft Independent Modeling of Class Analogy (SIMCA) approach resulting in a total correct classification rates between 81.25% and 100% for the various brandy brands. These preliminary results were very promising and highlight the potential of using FT-IR spectroscopy and multivariate classification techniques as a tool for rapid quality control and authentication of brandy brands. Using this work as base for further classification projects, this could be of great benefit to the alcoholic beverage industry of South Africa. Future work will involve the development of a database comprised of more products guaranteed authentic to expand the discriminating options. The results suggest FT-IR spectroscopy could be useful in authentication studies. / AFRIKAANSE OPSOMMING: Die WineScan FT120 is ‘n algemeen gebruikte instrument regoor Suid-Afrika. Die WineScan FT120 gebruik Fourier-transformasie-infrarooi (FT-IR) spektroskopie tesame met multiveranderlike statistiese metodes om spektra te korreleer met chemiese samestellingsdata. Die kommersieël beskikbare kalibrasiemodelle vir die FT-IR spektroskopie-instrument is ‘n voordeel vir onbedrewe gebruikers en roetine ontleding. Blootstelling van spiritusprodukte aan die tegnologie, het nuwe hindernisse bekend gestel en dus is verskillende kalibrasiemodelle genoodsaak om hiervoor te kompenseer. Akkuraatheid, presiesheid en ruheid van die verwysingsmetodes is geëvalueer tydens metodevalidasie. Die verwysingsmetodes is geskik verklaar vir die konstruksie van die kalibrasiemodel met geverifieërde akkurate verwysingsresultate. Verskeie multiveranderlike hoofkomponentanalise (MVK) was uitgevoer voor die kalibrasiestap met die doel om uitskieters te identifiseer en groeperings te inspekteer. MVK modelle kon monsters met atipiese spektra identifiseer en onderskei tussen verskillende produk tipes. Twee taktieke aangaande datastelsamestelling is getoets tydens kalibrasiemodel-opstelling, al die produkte saam en kalibrasiemodelle per produk soos met die MVK aangedui. Parsiële kleinste kwadraat (PKK)- regressie is gebruik vir die opstel van die kalibrasiemodelle vir etanol, digtheid, obskurasie en kleur. Met al die kalibrasiemodelle het die produk spesifieke kalibrasiemodelle beter regressiestatistiek gelewer. Die beste presterende etanol kalibrasiemodelle het ‘n standaardvoorspellingsfout (SVF) = 0.038 tot 0.106 %v/v bereik en het ‘n beduidende verbetering getoon op vorige gerapporteerde studies op spiritusprodukte (Lachenmeier, 2007). Die resultate vir die digtheidskalibrasiemodelle het ‘n eenderse tendens getoon soos die etanol, met die produk spesifieke kalibrasiemodelle wat beter presteer het. Hierdie studie was eerste in sy soort met die kalibrasiemodel vir obskurasie (SVF = 0.10 en 0.09 in gemengde brandewyne en potketel brandewyne, onderskeidelik) en kleur (SVF < 2.286 goud eenhede) van brandewyne en whiskies. Die bepalingskoëffisiënt (R²) vir die vier parameters, dui op goeie tot uitstekende presiesheid (0.8 < R² < 1.0). Vermindering van die variasie tussen die monsters in die datastel, het meer akkurate regressiestatistiek teweeg gebring, maar ‘n laer relatiewe voorspellingsafwyking (RVA) waarde (<5) tot gevolg gehad wat aan dui dat hierdie modelle nie geskik is vir sifting of kwantifisering nie. Die byvoeging van meer monsters per produk sal meer verskeidenheid in die datastel per produk bring, wat dan die standaardafwyking sal laat toeneem en uiteindelik die RVA laat toeneem. Die resultate het die fondasie gelê vir die ontwikkeling van kalibrasiemodelle vir die kwantifisering van ander parameters vir spesifieke produkte. As opvolg tot die groeperings van die produk tipe, waargeneem in die MVK modelle, was klassifikasie van brandewyn handelsmerke ondersoek. MVK modelle het duidelike skeiding gewys tussen potketel en gemengde brandewyne en tot ‘n sekere mate groepering tussen handelsmerke. Klassifikasie van brandewyne was ondersoek met behulp van the Soft Independent Modeling of Class Analogy (SIMCA) met die resultaat van ‘n totale korrekte klassifikasiekoers van tussen 81.25% en 100% vir die verskeie brandewyn handelsmerke. Hierdie voorlopige resultate toon belowend en beklemtoon die potensiaal van FT-IR spektroskopie en chemometrics tegnieke as toerusting vir die vinnige kwaliteitskontrole en egtheid van brandewyn handelsmerke studies. Met hierdie werk as basis vir verdere klassifikasie projekte, kan dit ‘n groot aanwins wees tot die alkoholiese drank industrie van Suid-Afrika. Toekomstige werk sal insluit die ontwikkeling van ‘n databasis saamgestel met meer gewaarborgde egte produkte om die klassifikasie uit te brei.