1. Chemical and fruit skin colour markers for simple quality control of tomato fruitsVesna Mila Meden, Tatjana Unuk, 2015, original scientific article Abstract: The orientation of this research was to evaluate the classic parameters regarding the external and internal quality of tomato fruits cv. ‘Brilliant‘ at different stages of maturity and to define the dynamics of their changes during the ripening in storage at 18 °C. Principal component analysis (PCA) and multivariate canonical discriminant analysis (DA) were used to classify tomato samples according to quality (internal and external) and nutritional value based on fruit mass, fruit skin colour, contents of soluble solids (SS), total titratable acids (TTA), ascorbic acid (AA), and total antioxidant potential (TAP). Several methods are usedfor determining AA content and TAP in plant samples. A simple routine method, direct redox titration with iodate solution and spectrophotometric determination of TAPSP, as described by Singleton and Rossi, also called total phenols, were used respectively. The results show that the stage of maturity (based on fruit skin colour) strongly determines the quality and nutritional value of the tomato fruit. Tomatoes harvested at table maturity (red colour, index a*/b* ≥ 0.85) have a significantly higher nutritional value (in terms of antioxidants – TAPSP and AA content) and overall quality than those harvested at an earlier maturity stage and then ripened in storage. This brings out the importance of short food supply chains and, from the viewpoint of overall fruit quality, it raises doubt about harvesting before reaching table maturity. On the other hand, it is necessary to be extremely attentive when determining optimal maturity, because when the plant becomes over-ripe or when stored, the nutritional value and overall quality decrease drastically. Besides the colour parameters, AA content is the most important chemical marker for a simple quality control. By using a simple and reliable analytical method for determining AA content, such as direct redox titratiation, the monitoring of tomato fruit quality could also be easily performed in situ.
Keywords: chemical markers, quality control, antioxidant, tomato, discriminant analysis
Published in DKUM: 24.10.2017; Views: 1395; Downloads: 199
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2. Multivariate data analysis of natural mineral watersKatja Šnuderl, Marjana Simonič, Jan Mocak, Darinka Brodnjak-Vončina, 2007, original scientific article Abstract: Fifty samples of natural mineral waters from springs in Slovenia, Hungary, Germany, Czech Republic and further countries of former Yugoslavia have been analysed. The mass concentration of cations ($Na^+$, $K^+$, $Ca^{2+}$, $Mg^{2+}$, $Fe^{2+}$, $Mn^{2+}$, $NH^+_4$) and anions ($F^-$, $Cl^-$, $I^-$, $NO^-_3$, $SO_4^{2-}$, $HCO_3^-$), the spring temperature, pH, conductivity and carbon dioxide mass concentration have been measured using standard analytical methods. Appropriate statistical methods and different chemometric tools were used to evaluate the obtained data, namely, (i) descriptive statistics, (ii) principal component analysis (PCA), (iii) cluster analysis, and (iv) linear discriminant analysis (LDA). It was confirmed that Slovenian natural mineral water samples differ most from the German ones but are relatively similar to the Czech and Hungarian ones. Water samples from Hungary are similar to waters from the eastern part of Slovenia.
Keywords: natural mineral water, ion determination, principal component analysis, cluster analysis, linear discriminant analysis
Published in DKUM: 21.12.2015; Views: 1866; Downloads: 103
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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, original scientific article Abstract: 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.
Keywords: multivariate data analysis, principal component analysis, discriminant analysis, feature selection, ANOVA, sensory analysis
Published in DKUM: 31.05.2012; Views: 2308; Downloads: 111
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