Alejandra Urtubia

Prediction of Abnormal Wine Fermentations Using Computational Intelligent Techniques

Symbolic bisimulation avoids the infinite branching problem caused by instantiating input names with all names in the standard definition of bisimulation in π-calculus. However, it does not automatically lead to an efficient algorithm, because symbolic bisimulation is indexed by conditions on names, and directly manipulating such conditions can be computationally costly. In this paper a new notion of bisimulation is introduced, in which the manipulation of maximally consistent conditions is replaced with a systematic employment of schematic names. It is shown that the new notion captures symbolic bisimulation in a precise sense. Based on the new definition an efficient algorithm, which instantiates input names “on-the-fly”, is presented to check bisimulations for finite-control π-calculus.Symbolic bisimulation avoids the infinite branching problem caused by instantiating input names with all names in the standard definition of bisimulation in π-calculus. However, it does not automatically lead to an efficient algorithm, because symbolic bisimulation is indexed by conditions on names, and directly manipulating such conditions can be computationally costly. In this paper a new notion of bisimulation is introduced, in which the manipulation of maximally consistent conditions is replaced with a systematic employment of schematic names. It is shown that the new notion captures symbolic bisimulation in a precise sense. Based on the new definition an efficient algorithm, which instantiates input names “on-the-fly”, is presented to check bisimulations for finite-control π-calculus.

Detection of abnormal fermentations in wine process by multivariate statistics and pattern recognition techniques.

Three multivariate statistical techniques (Multiway Principal Component Analysis, Multiway Partial Least Squares, and Stepwise Linear Discriminant Analysis) and one artificial intelligence method (Artificial Neural Networks) were evaluated to detect and predict early abnormal behaviors of wine fermentations. The techniques were tested with data of thirty-two variables at different stages of fermentation from industrial wine fermentations of Cabernet Sauvignon. All the techniques studied considered a pre-treatment to obtain a homogeneous space and reduce the overfitting. The results were encouraging; it was possible to classify at 72h 100% of the fermentation correctly with three variables using Multiway Partial Least Squares and Artificial Neural Networks. Additional and complementary results were obtained with Stepwise Linear Discriminant Analysis, which found that ethanol, sugars and density measurements are able to discriminate abnormal behavior.

Effect of dissolved oxygen and temperature on macromolecular composition and PHB storage of activated sludge

The macromolecular composition of activated sludge (lipids, intracellular proteins and intracellular polysaccharides) was studied together with its capacity to store macromolecules such as polyhydroxybutyrate (PHB) in a conventional activated sludge system fed with synthetic sewage water at an organic load rate of 1.0 kg COD/(m3·d), varying the dissolved oxygen (DO) and temperature. Six DO concentrations (0.8, 1.0, 1.5, 2.0, 2.5 and 8 mg/L) were studied at 20°C with a sludge retention time (SRT) of 6 days. In addition, four temperatures (10ºC, 15ºC, 20ºC and 30ºC) were assessed at constant DO (2 mg/L) with 2 days SRT in a second experimental run. The highest lipid content in the activated sludge was 95.6 mg/g VSS, obtained at 30°C, 2 mg/L of DO and a SRT of 2 days. The highest content of intracellular proteins in the activated sludge was 87.8 mg/g VSS, obtained at 20°C, 8 mg/L of DO and a SRT of 6 days. The highest content of intracellular polysaccharides in the activated sludge was 76.6 mg/g VSS, which was achieved at 20°C, a SRT of 6 days and a wide range of DO. The activated sludge PHB storage was very low for all the conditions studied.

Predictive power of LDA to discriminate abnormal wine fermentations

Wine fermentation is a critical step of winemaking. Unfavorable conditions can seriously affect the quality of the final product; however, it is difficult to anticipate these abnormal behaviors. In this study, the predictive power of stepwise linear discriminant analysis (SLDA) was evaluated to discriminate the behavior of wine fermentation. Information on different chemical concentrations from 18 industrial wine fermentations of Cabernet Sauvignon was used in this study. The statistical procedure consisted of curve fitting with exponential curve, and Stepwise LDA applied to the parameters of the curve. This methodology was applied to different times between the beginning and the end of fermentation (72, 95, 100, 150, 200 and 400 h). The results revealed that between seven and eight, of the 28 variables studied, minimized the Standard Error of Cross‐Validation (SECV) for the different times. In almost all times studied, correlation coefficient of alcoholic degree, initial concentration of glucose, initial density and correlation coefficient of tartaric acid were the variables more discriminant, and they indicated some differences between a normal and an abnormal fermentation, which need to be corroborated with more information. In this work, before 95 h, it was not possible to minimize the prediction error and find the most discriminant variables. Copyright

Technical Feasibility of Glucose Oxidase as a Prefermentation Treatment for Lowering the Alcoholic Degree of Red Wine

In the present work, the use of the glucose oxidase/catalase enzymatic system was evaluated as an alternative to decrease glucose concentration and eventually produce a reduced-alcohol wine. The effects of glucose oxidase, catalase, and aeration on glucose concentration were evaluated after 24 and 48 hr of treatment of 27°Brix Carmenere must. The results showed that the effect of aeration and glucose oxidase was not significant compared with the effect produced by glucose oxidase itself. In addition, the use of catalase combined with glucose oxidase provided the best result, decreasing the glucose concentration by 51 and 78% after 24 and 48 hr, respectively, when 200 U/mL of both enzymes was used. The alcoholic degree obtained after three and five days under this treatment and subsequent fermentations were 15% (v/v) ± 0.8 and 14% (v/v) ± 0.8, respectively. A major drawback of this treatment was the color change of Carmenere must because H2O2 was produced during the glucose oxidase treatment, despite the presence of catalase. The technical feasibility of using this prefermentative process led to a divided conclusion; obtaining a lower alcoholic degree using the glucose oxidase/catalase system was possible, but if the goal is the industrial application of this technique, the color change should be investigated further. An evaluation of the glucose oxidase/catalase ratio was projected to show an improvement of the H2O2 elimination and, subsequently, decrease the effect on color change.

Application of Computational Intelligence Techniques for Forecasting Problematic Wine Fermentations Using Data from Classical Chemical Measurements

The early forecasting of normal and problematic wine fermentations is one of the main problems of winemaking processes, due to its significant impacts in wine quality and utility. In Chile this is a critical problem because it is one of the top ten wine-producing countries. In this chapter, we review the computational intelligence methods that have been applied to solve this problem. Both methods studied, support vector machines and artificial neural networks, show excellent results with respect to the overall prediction error for different training/testing/validation percentages, different time cutoffs, and several parameter configurations. These results are of great importance for wine production because they are based only on measurement of classical chemical variables and they confirm that computational intelligence methods are a useful tool to the winemakers in order to correct in time a potential problem in the fermentation process.