Slow denaturation of wine proteins is thought to lead to protein aggregation, flocculation into a hazy suspension and formation of precipitates. The majority of wine proteins responsible for haze are grape‐derived, have low isoelectric points and molecular weight. They are grape pathogenesis‐related (PR) proteins that are expressed throughout the ripening period post véraison, and are highly resistant to low pH and enzymatic or non‐enzymatic proteolysis. Protein levels in un‐fined white wine differ by variety and range up to 300 mg/L. Infection with some common grapevine pathogens or skin contact, such as occurs during transport of mechanically harvested fruit, results in enhanced concentrations of some PR proteins in juice and wine. Oenological control of protein instability is achieved through adsorption of wine proteins onto bentonite. The adsorption of proteins onto bentonite occurs within several minutes, suggesting that a continuous contacting process could be developed. The addition of proteolytic enzyme during short term heat exposure, to induce PR protein denaturation, showed promise as an alternative to bentonite fining. The addition of haze‐protective factors, yeast mannoproteins, to wines results in decreased particle size of haze, probably by competition with wine proteins for other non‐proteinaceous wine components required for the formation of large insoluble aggregations of protein. Other wine components likely to influence haze formation are ethanol concentration, pH, metal ions and phenolic compounds.

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Analyses of complex decision-making problems, involving tradeoffs among multiple criteria, is often undertaken using the PROMETHEE multi-criteria decision analysis (MCDA) outranking technique. Various sources of uncertainty exist in the application of MCDA methods including the definition of criteria weights and the assignment of criteria performance values. Generalized criterion functions were incorporated in PROMETHEE to take the uncertainty in the criteria performance values into account; however, actors find it extremely difficult to select the generalized criterion functions and their associated thresholds for each criterion, which therefore results in an additional source of uncertainty. Furthermore, the generalized criterion functions do not address the subjectivity and uncertainty in the criteria weights, therefore, this form of uncertainty is usually assessed by sensitivity analysis methods. In this paper, a reliability-based approach is proposed which enables the decision maker to examine the robustness of the solution obtained from PROMETHEE. The proposed approach therefore allows a decision to be made with confidence that the alternative chosen is the best performing alternative under the range of probable circumstances, without being required to define the generalized criterion functions. The proposed stochastic method involves defining the uncertainty in the input values using probability distributions, performing a reliability analysis by Monte Carlo simulation and undertaking a significance analysis using the Spearman rank correlation coefficient. The outcomes of the approach include a distribution of the total flows of each alternative based upon the expected range of input parameter values. The benefits of the approach are illustrated by applying it to a renewable energy case study.

Visible (vis) and near-infrared (NIR) spectroscopy combined with multivariate analysis was used to classify the geographical origin of commercial Tempranillo wines from Australia and Spain. Wines (n = 63) were scanned in the vis and NIR regions (400-2500 nm) in a monochromator instrument in transmission. Principal component analysis (PCA), discriminant partial least-squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) based on PCA scores were used to classify Tempranillo wines according to their geographical origin. Full cross-validation (leave-one-out) was used as validation method when PCA and LDA classification models were developed. PLS-DA models correctly classified 100% and 84.7% of the Australian and Spanish Tempranillo wine samples, respectively. LDA calibration models correctly classified 72% of the Australian wines and 85% of the Spanish wines. These results demonstrate the potential use of vis and NIR spectroscopy, combined with chemometrics as a rapid method to classify Tempranillo wines accordingly to their geographical origin.