No abstract

This study investigated the yeast community present in the traditional Portuguese cheese, Serpa, by culture-dependent and -independent methods. Sixteen batches of Serpa cheeses from various regional industries registered with the Protected Designation of Origin (PDO) versus non-PDO registered, during spring and winter, were used. Irrespective of the producer, the yeast counts were around 5log CFU/g in winter and, overall, were lower in spring. The yeast species identified at the end of ripening (30days), using PCR-RFLP analysis and sequencing of the 26S rRNA, mainly corresponded to Debaryomyces hansenii and Kluyveromyces marxianus, with Candida spp. and Pichia spp. present to a lesser extent. The culture-independent results, obtained using high-throughput sequencing analysis, confirmed the prevalence of Debaryomyces spp. and Kluyveromyces spp. but, also, that Galactomyces spp. was relevant for three of the five producers, which indicates its importance during the early stages of the cheese ripening process, considering it was not found among the dominant viable yeast species. In addition, differences between the identified yeast isolated from cheeses obtained from PDO and non-PDO registered industries, showed that the lack of regulation of the cheese-making practice, may unfavourably influence the final yeast microbiota. The new knowledge provided by this study of the yeast diversity in Serpa cheese, could be used to modify the cheese ripening conditions, to favour desirable yeast species. Additionally, the prevalent yeast isolates identified, Debaryomyces hansenii and Kluyveromyces spp., may have an important role during cheese ripening and in the final sensorial characteristics. Thus, the study of their technological and functional properties could be relevant, in the development of an autochthonous starter culture, to ensure final quality and safety of the cheese.

Raw ewes' milk semi-soft cheeses (RESS-cheeses) are important products in Portugal and in several European regions. Creamy texture is an essential attribute of these cheeses, which results from structural properties that are not always well characterized. Here, the structural changes occurring during the ripening period of a traditional RESS-cheese, known as Serpa cheese, were analysed through small amplitude oscillatory shear (SAOS). Rheological data was complemented with other physical and chemical parameters, that were monitored during ripening, in order to estimate Serpa cheese ripening time using multiple linear regression (MLR). Mechanical spectra indicated a relatively strong structure, comparable to a gel, with a low dependence on frequency at the beginning of ripening and a weak structure, comparable to a concentrated suspension, with a crossing point (G''=G') at the left of the graphic and with both moduli highly dependent on frequency, at the end of ripening. Good correlations (P<0.05) were obtained between structural (hardness and storage modulus) and proteolysis indicators. Using a combination of chemical, colour and rheological parameters we were able to obtain a multiple linear regression (MLR) which allows the estimation of Serpa cheese ripening time with an estimation error of 1.7 d (adjusted R2=0.98, P<0.0001).

Roasted coffee samples of the two major trade species (Coffea arabica and C. canephora) were studied to identify sensory descriptors that might be used to determine blends production and evaluation, following the expectations of consumers. Coffee beans were roasted at 220 + 10 °C, for 7, 9, and 11 min, and the sensory profiles of the beverages were assessed. From descriptive analysis the eigenvalues allowed the identification of two principal components (PCs), being the variance between samples 68.9% and 21.1%. In the first PC the characteristic odor, astringency, body, bitter flavor, burned aroma, and residual, typical, and burned tastes prevailed. The correlation coefficient between the second PC and citric acid flavor and aroma reached 0.96 and 0.78, respectively. It was concluded that in beverages of these species, the descriptors of both components can be separated according to bean roasting time. Considering roasting time, the overall quality was also rated.