1. Prediction of wine sensorial quality by routinely measured chemical propertiesAdriána Bednárová, Roman Kranvogl, Darinka Brodnjak-Vončina, Tjaša Jug, 2014, izvirni znanstveni članek Opis: The determination of the sensorial quality of wines is of great interest for wine consumers and producers since it declares the quality in most of the cases. The sensorial assays carried out by a group of experts are time-consuming and expensive especially when dealing with large batches of wines. Therefore, an attempt was made to assess the possibility of estimating the wine sensorial quality with using routinely measured chemical descriptors as predictors. For this purpose, 131 Slovenian red wine samples of different varieties and years of production were analysed and correlation and principal component analysis were applied to find inter-relations between the studied oenological descriptors. The method of artificial neural networks (ANNs) was utilised as the prediction tool for estimating overall sensorial quality of red wines. Each model was rigorously validated and sensitivity analysis was applied as a method for selecting the most important predictors. Consequently, acceptable results were obtained, when data representing only one year of production were included in the analysis. In this case, the coefficient of determination (R2) associated with training data was 0.95 and that for validation data was 0.90. When estimating sensorial quality in categorical form, 94 % and 85 % of correctly classified samples were achieved for training and validation subset, respectively.
Ključne besede: overall sensorial quality, prediction, Slovenian wine, artificial neural networks, multivariate data analysis
Objavljeno v DKUM: 03.04.2017; Ogledov: 1602; Prenosov: 405
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2. Multivariate analysis and chemometric characterisation of textile wastewater streamsDarja Kavšek, Tina Jerič, Alenka Majcen Le Marechal, Simona Vajnhandl, Adriána Bednárová, Darinka Brodnjak-Vončina, 2013, izvirni znanstveni članek Opis: The aim of this work was to design a quick and reliable method for the evaluation and classification of wastewater streams into treatable and non-treatable effluents for reuse/recycling. Different chemometric methods were used for this purpose handling the enormous amount of data, and additionally to find any hidden information, which would increase our knowledge and improve the classification. The data obtained from the processes description, together with the analytical results of measured parameters' characterising the wastewater of a particular process, enabled us to build a fast-decision model for separating different textile wastewater outlets. Altogether 49 wastewater samples from the textile finishing company were analysed, and 19 different physical chemical measurements were performed for each of them. The resulting classification model was aimed at an automated decision about the choice of treatment technologies or a prediction about the reusability of wastewaters within any textile finishing or other company having similar characteristics of wastewater streams.
Ključne besede: textile finishing wastewater, chemometrics, multivariate data analysis, wastewater treatment
Objavljeno v DKUM: 10.07.2015; Ogledov: 1960; Prenosov: 80
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3. Classification of white varietal wines using chemical analysis and sensorial evaluationsKatja Šnuderl, Jan Mocak, Darinka Brodnjak-Vončina, Bibiana Sedláčkova, 2009, izvirni znanstveni članek Opis: The ways of application of multivariate data analysis and ANOVA to classification of white varietal wines are here demonstrated. Wine classification was performed using the following classification criteria: winevariety, year of production, wine producer, and wine quality, as found by sensorial testing (bouquet, colour, and taste). Subjective wine evaluation, made by wine experts, is combined with commonly used chemical and physico-chemical properties, measured in analytical laboratory. Importance of the measured variables was determined by principal component analysis and confirmed by analysis of variance. Linear discriminant analysis enabled not only a very successful wine classification but also prediction of the wine category for unknown samples. The wine categories were set up either by three wine varieties, or two vintages, wine producers; two or three wine categories established by wine quality reflected either total points obtained in sensorial evaluation or the points obtained for a particular quality descriptor like colour, taste and bouquet.
Ključne besede: multivariate data analysis, principal component analysis, discriminant analysis, feature selection, ANOVA, sensory analysis
Objavljeno v DKUM: 31.05.2012; Ogledov: 2308; Prenosov: 108
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