From milk to cheese: Evolution of flavor fingerprint of milk, cream, curd, whey, ricotta, scotta, and ripened cheese obtained during summer Alpine pasture

The role of each step of cheese and ricotta making in development of flavor of cheese and other dairy products is not yet well known. The objectives of this study were to characterize volatile organic compounds (VOC) in cheese and ricotta making with bulk milk from cows grazing in a highland area and to evaluate their evolution in the various dairy products and by-products obtained during the production processes. A group of 148 cows was grazed day and night on pasture from June to September. A total of 7 cheese-making sessions were carried out using the bulk milk collected every 2 wk during summer pasturing according to the artisanal procedure used for Malga cheese production. All milks, products, and by-products were sampled, and the VOC content of milk, cream, whey, ricotta, scotta (residual liquid), fresh cheeses, and cheeses ripened for 6 and 12 mo was determined by solid-phase microextraction gas chromatography-mass spectrometry. Forty-nine compounds were identified belonging to the following chemical families: alcohols (13), aldehydes (9), esters (8), free fatty acids (6), ketones (5), lactones (2), sulfurs (2), terpenes (2), phenol (1), and benzene (1). The results showed that the amounts of VOC in the various dairy products differed significantly. Comparisons between the VOC of 4 types of milk (whole evening, skim evening, whole morning, mixed in the vat) showed that the skimming process had the greatest effect, with about half of all the VOC analyzed affected, followed by time of milking (evening milking vs. morning milking) and mixing (skim evening milk mixed with whole morning milk). In general, among fresh products, cream had higher contents of fatty acids, sulfurs, and terpene volatile compounds than fresh cheese and ricotta, whereas ricotta showed a very high VOC amount compared with fresh cheese, probably due to its high processing temperature. The effects of the progressive nutrient depletion in milk during processing were investigated by comparing the amounts of VOC in vat milk, whey, and scotta. Although milk contained greater amounts of nutrients, whey and especially scotta had higher concentrations of VOC, with the exception of esters, sulfurs, terpenes, and phenolic compounds, as a result of physicochemical and microbial modifications during processing. Finally, the effect of ripening was tested by comparing the VOC of fresh and ripened cheeses (6 and 12 mo), revealing that VOC release increased dramatically during the first semester and further with increasing the ripening period to 1 yr. In particular, some alcohols (butan-2-ol), aldehydes (2-methylpropanal, hexanal, and heptanal), esters (ethyl butanoate and ethyl hexanoate), fatty acids (acetic, butanoic, and hexanoic acids), and ketones (butan-2-one, pentan-2-one, and heptan-2-one) showed a very large increase. In conclusion, according to the artisanal milk processing carried out for Malga cheese production, the quantity of VOC was shown to increase about 3 times during cheese making (from milk in vat to fresh cheese plus whey), almost 4 times during ricotta making (from whey to ricotta plus scotta), and about 16 times during 1 yr of ripening of cheese

Evaporation of the soil water in response to the amount of straw and evaporative demand.

Título em português: Evaporação da água do solo em resposta à quantidade de palha e à demanda evaporativa

Volatile fingerprinting of ripened cheese for authentication and characterisation of different dairy systems

Authentication of dairy systems is of growing interest for the dairy industry and we investigated the potentiality of using volatile fingerprinting of ripened cheeses by proton transfer reaction time-of-flight mass spectrometry. A total of 1,075 individual model cheeses made from milk of individual Brown Swiss cows of 72 farms were analysed. Using a linear discriminant analysis, cows and herds were assigned to 3 or 5 dairy systems differing in management, available facilities, and diets. We obtained variable discrimination abilities (up to 77% of correct classification of cheeses and 70% of farms with cross-validation). We found m/z 61,028 (acetic acid), 109,070 (pyrazine), and m/z 137,132 (terpene) characterising model cheeses from traditional dairy systems and m/z 71,086 (3-methyl-butan-1-ol, 3-methyl-3-buten-1-ol, pentan-1-ol), m/z 101,097 (hexan-2-one, hexanal), m/z 123,117 (nonenal), m/z 129,127 (octan-1-one, octanal), and two unidentified peaks m/z 83,071 and m/z 93,090 characterising model cheeses from the modern farms. In conclusion, it seems possible to discriminate between a range of dairy systems using fast volatile fingerprinting of ripened cheeses but a proper validation of results obtained is needed.Highlights Mass spectrometry technique (PTR-ToF-MS) was able to discriminate between dairy systems. We found m/z 61,028 (acetic acid), 109,070 (pyrazine), and m/z 137,132 (terpene) characterising model cheeses from traditional dairy systems. We found m/z 71,086 (3-methyl-butan-1-ol, 3-methyl-3-buten-1-ol, pentan-1-ol), m/z 101,097 (hexan-2-one, hexanal), m/z 123,117 (nonenal), m/z 129,127 (octan-1-one, octanal), and two unidentified peaks m/z 83,071 and m/z 93,090 characterising model cheeses from the modern farms

Uncertainty in data integration systems: automatic generation of probabilistic relationships

This paper proposes a method for the automatic discovery of probabilistic relationships in the environment of data integration systems. Dynamic data integration systems extend the architecture of current data integration systems by modeling uncertainty at their core. Our method is based on probabilistic word sense disambiguation (PWSD), which allows to automatically lexically annotate (i.e. to perform annotation w.r.t. a thesaurus/lexical resource) the schemata of a given set of data sources to be integrated. From the annotated schemata and the relathionships defined in the thesaurus, we derived the probabilistic lexical relationships among schema elements. Lexical relationships are collected in the Probabilistic Common Thesaurus (PCT), as well as structural relationships

Schema-agnostic progressive entity resolution

Entity Resolution (ER) is the task of finding entity profiles that correspond to the same real-world entity. Progressive ER aims to efficiently resolve large datasets when limited time and/or computational resources are available. In practice, its goal is to provide the best possible partial solution by approximating the optimal comparison order of the entity profiles. So far, Progressive ER has only been examined in the context of structured (relational) data sources, as the existing methods rely on schema knowledge to save unnecessary comparisons: they restrict their search space to similar entities with the help of schema-based blocking keys (i.e., signatures that represent the entity profiles). As a result, these solutions are not applicable in Big Data integration applications, which involve large and heterogeneous datasets, such as relational and RDF databases, JSON files, Web corpus etc. To cover this gap, we propose a family of schema-agnostic Progressive ER methods, which do not require schema information, thus applying to heterogeneous data sources of any schema variety. First, we introduce two na\uefve schema-agnostic methods, showing that straightforward solutions exhibit a poor performance that does not scale well to large volumes of data. Then, we propose four different advanced methods. Through an extensive experimental evaluation over 7 real-world, established datasets, we show that all the advanced methods outperform to a significant extent both the na\uefve and the state-of-the-art schema-based ones. We also investigate the relative performance of the advanced methods, providing guidelines on the method selection

Instrumental neutron activation analysis of an enriched 28Si single-crystal

The determination of the Avogadro constant plays a key role in the redefinition of the kilogram in terms of a fundamental constant. The present experiment makes use of a silicon single-crystal highly enriched in 28Si that must have a total impurity mass fraction smaller than a few parts in 109. To verify this requirement, we previously developed a relative analytical method based on neutron activation for the elemental characterization of a sample of the precursor natural silicon crystal WASO 04. The method is now extended to fifty-nine elements and applied to a monoisotopic 28Si single-crystal that was grown to test the achievable enrichment. Since this crystal was likely contaminated, this measurement tested also the detection capabilities of the analysis. The results quantified contaminations by Ge, Ga, As, Tm, Lu, Ta, W and Ir and, for a number of the detectable elements, demonstrated that we can already reach the targeted 1 ng/g detection limit.Comment: 9 pages, 1 figure, 1 tabl

Parallel Matrix-free polynomial preconditioners with application to flow simulations in discrete fracture networks

We develop a robust matrix-free, communication avoiding parallel, high-degree polynomial preconditioner for the Conjugate Gradient method for large and sparse symmetric positive definite linear systems. We discuss the selection of a scaling parameter aimed at avoiding unwanted clustering of eigenvalues of the preconditioned matrices at the extrema of the spectrum. We use this preconditioned framework to solve a $3 \times 3$ block system arising in the simulation of fluid flow in large-size discrete fractured networks. We apply our polynomial preconditioner to a suitable Schur complement related with this system, which can not be explicitly computed because of its size and density. Numerical results confirm the excellent properties of the proposed preconditioner up to very high polynomial degrees. The parallel implementation achieves satisfactory scalability by taking advantage from the reduced number of scalar products and hence of global communications

Prognostic factors in node-negative colorectal cancer: a retrospective study from a prospective database

PURPOSE: There is a need to identify a subgroup of high-risk patients with node-negative colorectal cancer who have a poor long-term prognosis and may benefit from adjuvant therapies. The aim of this study was to evaluate the prognostic impact of clinical and pathological parameters in a retrospective study from a prospective, continuous database of homogenously treated patients. METHODS: This study included 362 patients operated in a single institution for Dukes A and B (node-negative) colorectal cancer. The median follow-up was 140 months. The prognostic value of 13 clinical and pathological parameters was investigated. RESULTS: Multivariate analysis identified six independent prognostic factors: age at time of diagnosis (hazard ratio (HR) = 1.076), number of lymph nodes removed (HR = 0.948), perineural invasion (HR = 2.173), venous invasion (HR = 1.959), lymphatic vessel invasion (HR = 2.126), and T4 stage (HR = 5.876). CONCLUSION: These parameters could be useful in identifying patients with high-risk node-negative colorectal cancer who should be presented to adjuvant therapy