Statistics for Spatial Data.

Mozzarella (M), Grana Padano (GP) and Parmigiano Reggiano (PR) are three of the most important traditional Italian cheeses. In the three cheese manufactures the initial fermentation is carried out by adding natural whey cultures (NWCs) according to a back-slopping procedure. In this study, NWCs and the corresponding curds from M, GP and PR manufactures were analyzed by culture-independent pyrosequencing of the amplified V1–V3 regions of the 16S rRNA gene, in order to provide insights into the microbiota involved in the curd acidification. Moreover, culture-independent high-throughput sequencing of lacS gene amplicons was carried out to evaluate the biodiversity occurring within the S. thermophilus species. Beta diversity analysis showed a species-based differentiation between GP-PR and M manufactures indicating differences between the preparations. Nevertheless, all the samples shared a naturally-selected core microbiome, that is involved in the curd acidification. Type-level variability within S. thermophilus species was also found and twenty-eight lacS gene sequence types were identified. Although lacS gene did not prove variable enough within S. thermophilus species to be used for quantitative biotype monitoring, the possibility of using non rRNA targets for quantitative biotype identification in food was highlighted.

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A selected core microbiome drives the early stages of three popular italian cheese manufactures.

/pdf/spatial-and-spatio-temporal-models-with-r-inla-29eh5lqe7n.pdf

Spatial and spatio-temporal models with R-INLA

In this work, we consider a hierarchical spatio-temporal model for particulate matter (PM) concentration in the North-Italian region Piemonte. The model involves a Gaussian Field (GF), affected by a measurement error, and a state process characterized by a first order autoregressive dynamic model and spatially correlated innovations. This kind of model is well discussed and widely used in the air quality literature thanks to its flexibility in modelling the effect of relevant covariates (i.e. meteorological and geographical variables) as well as time and space dependence. However, Bayesian inference—through Markov chain Monte Carlo (MCMC) techniques—can be a challenge due to convergence problems and heavy computational loads. In particular, the computational issue refers to the infeasibility of linear algebra operations involving the big dense covariance matrices which occur when large spatio-temporal datasets are present. The main goal of this work is to present an effective estimating and spatial prediction strategy for the considered spatio-temporal model. This proposal consists in representing a GF with Matern covariance function as a Gaussian Markov Random Field (GMRF) through the Stochastic Partial Differential Equations (SPDE) approach. The main advantage of moving from a GF to a GMRF stems from the good computational properties that the latter enjoys. In fact, GMRFs are defined by sparse matrices that allow for computationally effective numerical methods. Moreover, when dealing with Bayesian inference for GMRFs, it is possible to adopt the Integrated Nested Laplace Approximation (INLA) algorithm as an alternative to MCMC methods giving rise to additional computational advantages. The implementation of the SPDE approach through the R-library INLA (
 www.r-inla.org
 ) is illustrated with reference to the Piemonte PM data. In particular, providing the step-by-step R-code, we show how it is easy to get prediction and probability of exceedance maps in a reasonable computing time.

/pdf/spatio-temporal-modeling-of-particulate-matter-concentration-y4zaswgved.pdf

Spatio-temporal modeling of particulate matter concentration through the SPDE approach

Safety assessment of dairy microorganisms: The Lactobacillus genus

https://cab.unime.it/mus/384/1/Hierarchical_Space-time_Modelling_of_PM10_Pollution_in_the_Emilia-Romagna_Region.pdf

Hierarchical space-time modelling of PM10 pollution

Lactobacillus helveticus is a homofermentative thermophilic lactic acid bacterium used extensively for manufacturing Swiss type and aged Italian cheese. In this study, the phenotypic and genotypic diversity of strains isolated from different natural dairy starter cultures used for Grana Padano, Parmigiano Reggiano, and Provolone cheeses was investigated by a classification tree technique. A data set was used that consists of 119 L. helveticus strains, each of which was studied for its physiological characters, as well as surface protein profiles and hybridization with a species-specific DNA probe. The methodology employed in this work allowed the strains to be grouped into terminal nodes without difficult and subjective interpretation. In particular, good discrimination was obtained between L. helveticus strains isolated, respectively, from Grana Padano and from Provolone natural whey starter cultures. The method used in this work allowed identification of the main characteristics that permit discrimination of biotypes. In order to understand what kind of genes could code for phenotypes of technological relevance, evidence that specific DNA sequences are present only in particular biotypes may be of great interest.

Carlo Trivisano

Papers

Hierarchical space-time modelling of PM10 pollution

Biodiversity among Lactobacillus helveticus strains isolated from different natural whey starter cultures as revealed by classification trees

Small area estimation of the Gini concentration coefficient

Robust linear mixed models for Small Area Estimation

Hierarchical Bayes multivariate estimation of poverty rates based on increasing thresholds for small domains