Wine protein instability depends on several factors, but wine grape proteins are the main haze factors, being mainly caused by pathogenesis-related proteins (thaumatin-like proteins and chitinases) with a molecular weight between 10~40 kDa and an isoelectric point below six. Wine protein stability tests are needed for the routine control of this wine instability, and to select the best technological approach to remove the unstable proteins. The heat test is the most used, with good correlation with the natural proteins’ precipitations and because high temperatures are the main protein instability factor after wine bottling. Many products and technological solutions have been studied in recent years; however, sodium bentonite is still the most efficient and used treatment to remove unstable proteins from white wines. This overview resumes and discusses the different aspects involved in wine protein instability, from the wine protein instability mechanisms, the protein stability tests used, and technological alternatives available to stabilise wines with protein instability problems.

No abstract

a b s t r a c tBrazilian rum (also known as cachaça) is the third most commonly consumed distilled alcoholic drink in the world, with approximately 2.5 billion liters produced each year. It is a traditional drink with refined features and a delicate aroma that is produced mainly in Brazil but consumed in many countries. It can be aged in various types of wood for 1-3 years, which adds aroma and a distinctive flavor with different characteristics that affect the price. A research challenge is to develop a cheap automatic recognition system that inspects the finished product for the wood type and the aging time of its production. Some classical methods use chemical analysis, but this approach requires relatively expensive laboratory equipment. By contrast, the system proposed in this paper captures image signals from samples and uses an intelligent classification technique to recognize the wood type and the aging time. The classification system uses an ensemble of classifiers obtained from different wavelet decompositions. Each classifier is obtained with different wavelet transform settings. We compared the proposed approach with classical methods based on chemical features. We analyzed 105 samples that had been aged for 3 years and we showed that the proposed solution could automatically recognize wood types and the aging time with an accuracy up to 100.00% and 85.71% respectively, and our method is also cheaper.

No abstract

The mam objective of this study is to present an alternative methodology based on the Probability Neural Network (PNN) fonnalism to estimate and further predict the penneability values in any location of a 3D grid of a reservoir model. An experimental data set containing measurements of log porosity, true vertical depth, transitive time and lithologies at well locations, was used for training the PNN. Afterwards, the trained PNN is applied to the wells where does not exist core infonnation, but logs. The penneability values estimated by PNN in the wells are used to simulate the penneability at all spatial locations of the reservoir. This methodology was applied to experimental data from a reservoir located at West Africa with encouraging results.