The technology of soft Camembert cheese with the usage of different bacterial preparations

The aim of the work was to develop a technology of soft Camembert cheese from pasteurized cow's milk using various bacterial preparations and to investigate its basic physicochemical and organoleptic properties. Two experimental samples of cheese were made: sample 1 – culture of direct application of mesothermophilic type RSF-742 + culture of white mold (Penicillium candidum + Geotrichum candidum) + rennet enzyme + calcium chloride; sample 2 – culture of direct application of mesophilic type CHN-19 + culture of white mold (Penicillium candidum + Geotrichum candidum) + rennet enzyme + calcium chloride. The finished product analyzed by the organoleptic and physicochemical parameters. Syneretic properties of rennet clots were studied after the process of fermentation and coagulation at a temperature of 32 ºC. The sample with the use of CNH-19 was characterized by the best consistency and pronounced mushroom and creamy aroma. Sample № 1, made with the bacterial preparation RSF-742, was characterized by a less pronounced aroma and structure, typical of Camembert cheese. It was found that the sample №1 (RSF-742) was characterized by higher syneretic properties. According to syneretic properties, lower moisture content characterized sample 1 (62 %) versus sample 2 (64 %). The volume of serum released in 1 hour was 65 % for sample 1 and 62 % for sample 2. The highest values for the fat content was sample 1 – 43 %, and sample 2 – 42 %. The content of salt did not differ, in sample № 1 – 1.8 % and in sample № 2 – 1.75 %. There were changes in the active acidity of the finished product when using different bacterial preparation. Lower pH values in sample 1 – 6.2, and slightly higher in sample 2 – 6.5 were observed. The highest number of points according to the results of the score was given to sample № 2 – 86 points, sample № 1 – 77 points out of possible 100.

Operationalizing Impaired Performance in Neuropsychological Assessment: A Comparison of the Use of Published Versus Sample-Based Normative Data for the Prediction of Dementia

Objectives:To compare the sensitivity, specificity, and predictive value of published versus sample-based norms to detect early dementia in the Uniform Data Set (UDS).Methods:The UDS was administered to 526 nondemented participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Baseline scores were standardized using published norms and healthy control data from ADNI corrected for age, education, and sex. Subjects obtaining two scores < −1 SD (determined separately using published and sample norms) were labeled “at risk for dementia.” Both methods were compared on sensitivity, specificity, and positive/negative predictive value (PPV/NPV) for dementia at follow-up.Results:Risk scores derived from published data had 86.1% sensitivity, 62.0% specificity, 68.6% accuracy, 46.1% PPV, and 92.2% NPV. Those from sample norms were more sensitive (91.0%), less specific (52.9%), and less accurate (63.3%), with worse PPV (42.1%) and similar NPV (94.0%). Sample norms were better at identifying incident dementia cases with relatively lower education than those with higher education. Discrepancies between both methods were more common in women.Conclusions:Sample norms are marginally more sensitive than published norms for predicting dementia, while published norms are slightly more accurate. Accuracy of risk estimates for women and those with lower education may be increased using locally generated norms.

A Comparison of Mine Seismic Discriminators Based on Features of Source Parameters to Waveform Characteristics

To find efficient methods for classifying mine seismic events, two features extraction approaches were proposed. Features of source parameters including the seismic moment, the seismic energy, the energy ratio of S- to P-wave, the static stress drop, time of occurrence, and the number of triggers were selected, counted, and analyzed in approach I. Waveform characteristics consisting of two slope values and the coordinates of the first peak and the maximum peak were extracted as the discriminating parameters in approach II. The discriminating performance of the two approaches was compared and discussed by applying the Bayes discriminant analysis to the characteristic parameters extracted. Classification results show that 83.5% of the original grouped cases are correctly classified by approach I, and 97.1% of original grouped cases are correctly classified by approach II. The advantages and limitations pertaining to each classifier were discussed by plotting the event magnitude versus sample number. Comparative analysis shows that the proposed method of approach II not only has a low misjudgment rate but also displays relative constancy when the testing samples fluctuate with seismic magnitude and energy.

Real Versus Sample-Based Differences in Comparative Family Business Research

This article analyzes the impact of not controlling for “demographic sample” differences on research results in the area of comparative family/nonfamily business research. Using different statistical methods with and without control for “demographic sample” differences, the results show that controlling for these firm demographics in a bivariate as well as a multivariate framework is very important to discover “real” differences between family and nonfamily firms. We found “real” differences for export, budgeting, variable reward systems, profitability and gender, educational degree, and tenure of the CEO. Strategy, networking, long-term planning and control systems, perceived environmental uncertainty, growth, and management training, classified by prior empirical research as different between family and nonfamily firms, do not differ.