Résultat de la recherche
Geographic origin of European Emmental. Use of discriminant analysis and artificial neural network for classification purposes
The goal of this work was to classify European Emmental cheeses according to their geographic origin using analytical approaches. Twenty-five analytical parameters (factors) were measured in 183 samples. Results were combined by multivariate statistical analysis. Discriminant analysis (DA) and an artificial neural network (ANN) delivered similar results when all regions and factors were included; 95% and 91%, respectively, of the samples were correctly classified in the validation procedure. To reduce the analytical costs and the risk of overfitting, a DA based on a selection of only 11 factors was calculated. In this case, the Jackknifed validation delivered 95% correct assignments. Finally, a system was optimised to discriminate between the Swiss samples and cheeses from other regions. Building a new model for each of the six pairs, Switzerland vs. another region, 100% correct classification could be achieved for the Swiss samples.
Data transferability between two MS-based electronic noses using processed cheeses and evaporated milk as reference materials
Electronic noses produce data which are difficult to transfer between identical instruments because of differences in the characteristics of "identical" instruments and because of sensor drift. The transportability of data is, however, essential for the setting up of databanks. Results of the present investigation, using processed cheeses and evaporated milk as reference materials, show that data transfer between two electronic noses based on mass spectrometry is possible and simple.
Geographic origin of European Emmental cheese: Characterisation and descriptive statistics
To survey the authenticity of Emmental cheese from some of the main European countries of origin, samples of cheeses manufactured during winter (110 samples) and summer (73 samples) were collected. From a preliminary study, a series of promising analytical methods were selected and applied: total nitrogen, water soluble nitrogen (WSN), 12% TCA soluble nitrogen (TCA-SN), pH-value, volatile short-chain acids, chloride, organic acids, enterococci, obligate heterofermentative lactobacilli (OHL), Lactobacillus helveticus, sodium, copper, zinc, magnesium and stable isotope ratios (δ2H, δ13C, δ15N, δ34S). The data were analysed by univariate statistical methods according to the geographic origin and the season of production. Significant differences between the regions of origin were found for all parameters investigated (P≤0.001). Cheeses from some regions showed very specific properties. Seasonal differences were observed in certain regions for acetate, propionate, caproate, WSN, TCA-SN, pyruvate, OHL, zinc and δ13C levels.
Analytical methods for the determination of the geographic origin of Emmentaler cheese. Main framework of the project; chemical, biochemical, microbiological, colour and sensory analyses
Emmentaler is a hard cheese produced and consumed in many countries. The authenticity of this cheese variety is becoming a matter of national importance with the imminent opening of the cheese market in Switzerland and the introduction of the PDO label for the Emmentaler Switzerland™. This paper is the first in a series reporting the search for parameters that make possible a geographic discrimination of Emmentalers. Twenty Emmentaler cheese samples from six regions in Europe (one from Switzerland, two from France, one from Austria, one from Germany and one from Finland) were considered. In this paper, chemical, biochemical, microbiological, colour and sensory parameters were investigated leading to the following preliminary results. The presence of Lb. helveticus seemed to be a good indicator of an origin other than Swiss. The concentration of enterococci, facultative and obligate heterofermentative lactobacilli and salt tolerant bacteria was significantly lower in Emmentaler Switzerland™ than in the foreign cheeses. Chemical parameters such as fat content and pH value as well as biochemical parameters such as L- and D-lactate and pyruvate allowed us to partially discriminate between the regions when these data were combined by principal component analysis. The colour of the body also showed significant differences.