Detecció de compostos volàtils, clorofenols, cloroanisoles i 2,4,6-tribromoanisole, relacionats amb el "gust del suro"

D'entre els defectes organolèptics associats al vi, en destaca l'anomenat "gust de suro" habitualment vinculat a la presència de cloroanisoles, els quals són productes de l'activitat microbiana formats a partir dels corresponents clorofenols. La present tesi doctoral recull, en primer lloc, metodologies analítiques adreçades principalment a la determinació dels compostos clorofenòlics (2,4,6-triclorofenol, 2,3,4,6-tetraclorofenol i pentaclorofenol) en el control de qualitat dels taps suro, emprant dissolucions hidroalcohòliques com a medi de maceració o d'extracció i utilitzant les tècniques d'extracció en fase sòlida (SPE) i microextracció en fase sòlida (SPME) acoblades a la cromatografia de gasos (GC). En segon lloc, per tal de dur a terme l'anàlisi de cloroanisoles juntament amb els seus precursors en matrius de suro s'ha avaluat un mètode basat en l'extracció amb dissolvent orgànic, el qual ha estat aplicat per a l'estudi de diferents sistemes d'eliminació d'aquests anàlits en la matriu citada. En darrer lloc, s'han proposat metodologies per l'anàlisi de mostres de vi, en les quals d'una banda s'han determinat els compostos clorofenòlics utilitzant la SPME i de l'altra el 2,4,6-tricloroanisole i el 2,4,6-tribromoanisole mitjançant l'acoblament de la SPE i la injecció de grans volums (LVI) en el sistema cromatogràfic.

Moderating effects of management control systems and innovation on performance. Simple methods for correcting the effects of measurement error for interaction effects in small samples

In the accounting literature, interaction or moderating effects are usually assessed by means of OLS regression and summated rating scales are constructed to reduce measurement error bias. Structural equation models and two-stage least squares regression could be used to completely eliminate this bias, but large samples are needed. Partial Least Squares are appropriate for small samples but do not correct measurement error bias. In this article, disattenuated regression is discussed as a small sample alternative and is illustrated on data of Bisbe and Otley (in press) that examine the interaction effect of innovation and style of use of budgets on performance. Sizeable differences emerge between OLS and disattenuated regression

Inference of distributional parameters from compositional samples containing nondetects

Low concentrations of elements in geochemical analyses have the peculiarity of being compositional data and, for a given level of significance, are likely to be beyond the capabilities of laboratories to distinguish between minute concentrations and complete absence, thus preventing laboratories from reporting extremely low concentrations of the analyte. Instead, what is reported is the detection limit, which is the minimum concentration that conclusively differentiates between presence and absence of the element. A spatially distributed exhaustive sample is employed in this study to generate unbiased sub-samples, which are further censored to observe the effect that different detection limits and sample sizes have on the inference of population distributions starting from geochemical analyses having specimens below detection limit (nondetects). The isometric logratio transformation is used to convert the compositional data in the simplex to samples in real space, thus allowing the practitioner to properly borrow from the large source of statistical techniques valid only in real space. The bootstrap method is used to numerically investigate the reliability of inferring several distributional parameters employing different forms of imputation for the censored data. The case study illustrates that, in general, best results are obtained when imputations are made using the distribution best fitting the readings above detection limit and exposes the problems of other more widely used practices. When the sample is spatially correlated, it is necessary to combine the bootstrap with stochastic simulation

Correcting Measurement Error Bias in Interaction Models with Small Samples

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression