Maurizio Rinaldi

The assessment of airborne bacterial contamination in three composting plants revealed site-related biological hazard and seasonal variations.

Aims:  The purpose of this study was to evaluate the degree of bacterial contamination generated by three Italian composting plants (1, 2 and 3) in two different seasons and to assess the health risk for the employees.

Inhibition of Candida albicans adhesion on medical‐grade silicone by a Lactobacillus‐derived biosurfactant

The study aimed at investigating the ability of biosurfactant (BS) produced by a Lactobacillus brevis isolate (CV8LAC) to inhibit adhesion and biofilm formation of Candida albicans on medical‐grade silicone elastomeric disks (SEDs).

Pattern recognition and genetic algorithms for discrimination of orange juices and reduction of significant components from headspace solid‐phase microextraction

INTRODUCTION Orange (Citrus sinensis L.) juice comprises a complex mixture of volatile components that are difficult to identify and quantify. Classification and discrimination of the varieties on the basis of the volatile composition could help to guarantee the quality of a juice and to detect possible adulteration of the product. OBJECTIVE To provide information on the amounts of volatile constituents in fresh-squeezed juices from four orange cultivars and to establish suitable discrimination rules to differentiate orange juices using new chemometric approaches. METHODOLOGY Fresh juices of four orange cultivars were analysed by headspace solid-phase microextraction (HS-SPME) coupled with GC-MS. Principal component analysis, linear discriminant analysis and heuristic methods, such as neural networks, allowed clustering of the data from HS-SPME analysis while genetic algorithms addressed the problem of data reduction. To check the quality of the results the chemometric techniques were also evaluated on a sample. RESULTS Thirty volatile compounds were identified by HS-SPME and GC-MS analyses and their relative amounts calculated. Differences in composition of orange juice volatile components were observed. The chosen orange cultivars could be discriminated using neural networks, genetic relocation algorithms and linear discriminant analysis. Genetic algorithms applied to the data were also able to detect the most significant compounds. CONCLUSIONS SPME is a useful technique to investigate orange juice volatile composition and a flexible chemometric approach is able to correctly separate the juices.

Bayesian Inference from Count Data Using Discrete Uniform Priors

We consider a set of sample counts obtained by sampling arbitrary fractions of a finite volume containing an homogeneously dispersed population of identical objects. We report a Bayesian derivation of the posterior probability distribution of the population size using a binomial likelihood and non-conjugate, discrete uniform priors under sampling with or without replacement. Our derivation yields a computationally feasible formula that can prove useful in a variety of statistical problems involving absolute quantification under uncertainty. We implemented our algorithm in the R package dupiR and compared it with a previously proposed Bayesian method based on a Gamma prior. As a showcase, we demonstrate that our inference framework can be used to estimate bacterial survival curves from measurements characterized by extremely low or zero counts and rather high sampling fractions. All in all, we provide a versatile, general purpose algorithm to infer population sizes from count data, which can find application in a broad spectrum of biological and physical problems.