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Alessandro Magrini

Summary

Linear regression with temporally delayed covariates (distributed-lag linear regression) is a standard approach to lag exposure assessment, but it is limited to a single biomarker of interest and cannot provide insights on the relationships holding among the pathogen exposures, thus precluding the assessment of causal effects in a general context. In this paper, to overcome these limitations, distributed-lag linear regression is applied to Markovian structural causal models. Dynamic causal effects are defined as a function of regression coefficients at different time lags. The proposed methodology is illustrated using a simple lag exposure assessment problem.

Open access

Alessandro Magrini, Ottorino L. Pantani, Alessandra Biondi Bartolini and Federico M. Stefanini

Summary

The analysis of wine sensory descriptors is a fundamental step in the improvement of wine making, because the procedures are judged just before bottled wine is ready for consumption. Despite several contributions in the literature, traditional analysis of variance methods are not adequate to analyse sensory descriptors, because they are defined on ordinal scales. In this paper, we exploit cumulative link mixed models in a three-way full factorial design to assess the effect of prefermentative maceration, temperature and saignée on wine sensory descriptors. Using cumulative link mixed models, the bias introduced by assessors’ judgement and the ordinal scale of sensory descriptors are taken into account. The results were the following: the application of prefermentative maceration techniques did not lead to an improvement in the sensory profile of wines after a year from bottling; wines treated with saignée showed greater intensity in olfactive descriptors; and higher fermentation temperatures resulted in wines that were generally more concentrated.

Open access

Alessandro Magrini, Stefano Di Blasi and Federico Mattia Stefanini

Summary

In this paper, a Conditional Linear Gaussian Network (CLGN) model is built for a two-year experiment on Tuscan Sangiovese grapes involving canopy management techniques (number of buds, defoliation and bunch thinning) and harvest time (technological and late harvest). We found that the impact of the considered treatments on the color of wine can be predicted still in the vegetative season of the grapevine; the best treatments to obtain wines with good structure are those with a low number of buds; the best treatments to obtain fresh wines suitable for young consumers are those with technological rather than late harvest, preferably with a high number of buds, and anyway with both defoliation and bunch thinning not performed.