In this work, the application of an electronic tongue (ET) based on a specifi c ion-selective sensor array for discrimination of different white wine types is presented. The electronic tongue equipped with specifi c sensor array containing seven IFSET sensors was used to analyze wine samples. The obtained ET responses were evaluated using different pattern recognition methods. Principal component analysis (PCA) provides the possibility to identify some initial patterns. Linear discriminant analysis (LDA) was used to build models to separate white wine samples based on wine regions and grape cultivars. The results showed that every group was distinguished from each other with no misclassifi cation error. Furthermore, the sequence of the wine sample groups was similar to the increasing total acidity content. Partial least square (PLS) regression was used to build models for the prediction of the main chemical compositions of the wine samples based on the electronic tongue results. The closest correlation (R 2 =0.93) was found in case of 'total acidity', and the prediction error (RMSEP) was 6.9%. The pH of the wine samples was predicted with good correlation (R 2 =0.89) but higher prediction error (RMSEP=10.71%) from the electronic tongue results. The ET combining these statistical methods can be applied to determine the origin and variety of the wine samples in easy and quick way.
An appropriate rheological model can be used in production of good quality gum candy required by consumers. For this purpose Creep-Recovery Test (CRT) curves were recorded with a Stable Micro System TA.XT-2 precision texture analyser with 75 mm diameter cylinder probe on gum candies purchased from the local market. The deformation speed was 0.2 mm s -1 , the creeping-and recovering time was 60 s, while the loading force was set to 1 N, 2 N, 5 N, 7 N, and 10 N. The two-element Kelvin-Voigt-model, a three-element model, and the four-element Burgers-model were fi tted on the recorded creep data, and then the parameters of the models were evaluated. The best fi tting from the used models was given by the Burgers model. Keywords: Burgers model, gum candy, rheological modelling, texture analyserThe quality of foods can be described by both sensory and physical properties (SZCZESNIAK, 2001). The texture and its change can be characterized by rheological parameters: LAMBERT-MERETEI and co-workers (2010) evaluated the changes of hardness, chewiness, gumminess, cohesiveness, and springiness of bread crumb after addition of bread improver. co-workers (2008, 2011) determined the deformation work and stiffness of carrot texture during non-ideal storage. From consumer's view the main quality properties of gum candies are also textural properties. From the producer's view, the quality and the texture of gum candy has to be described by such rheological test that is objective and models the chewing process. The gum candy is sucrose based, combined semisolid gel, which contains approximately 10% gelatine. The sugar content (sucrose, glucose syrup, and dextrose in certain proportion) ensures the required texture profi le, while the gelatine secures the typical viscoelastic rheological behaviour (MOHOS, 1993). The origin, the quality, and the quantity of applied gelatine determine the main quality and sensory properties of candy (MOHOS, 2010).According to MITCHELL'S (1980) comprehensive study of gel rheology, the majority of food material gels show linear viscoelastic behaviour up to strain of 0.1 range. If the strain is higher than 0.1, the creep and the stress relaxation of gels would suggest the move and the brake of non-covalent cross links under stress.The gelatine is a biopolymer protein, obtained by hydrolytic degradation of collagen. Native conformation of collagen is a triple helix held together by inter-chain hydrogen bonding. Above 37 °C in aqueous solutions the gelatine molecules exist as separate, disordered chains (coils). When a solution containing around 1% w/w gelatine is cooled to room temperature, the gelatine molecules form an infi nite network cross-linked by hydrogen bonding (MARFIL et al., 2012). The role of the coil-helix transition in this mechanism has
Time consuming and expensive methods have been applied for detection of coffee adulteration based on the literature. In the present work, an optical method (vision system) and the application of an electronic tongue is proposed to reveal the addition of barley in different proportion to coffee in ground and brewed forms. In a range of 1 to 80% (w/w) Robusta coffee was blended with roasted barley. Principal Component Analysis (PCA) accomplished on vision system image data showed a good discrimination of the adulterated samples. The results of Polar Qualifi cation System (PQS) data reduction method revealed even small differences in the right barley content order by point method approach. With Partial Least Squares (PLS) regression the amount of barley in Robusta was predicted with high R 2 (0.996) and relatively low RMSEP (~2%) values in case of vision system data processing. Considering electronic tongue measurements, PCA results showed a good discrimination of samples with higher barley concentration. Misclassifi cation was found in the low concentrated area by Lienar Discriminant Analgsis (LDA). To obtain an accurate model for barley content prediction in coffee, the most sensitive sensor signals were used to apply PLS regression successfully (R 2 =0.97, RMSEP=3.99% (w/w)).
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