Commercial craft beers produced in Uruguay: Volatile profile and physicochemical composition.

Even beer being the most consumed alcoholic beverage around the world, there is not enough information generated for craft beers produced in Latin America, for either volatile profiles or physicochemical studies. In this work, the chemical and volatile components of ten commercial Blond Ale and nine Indian Pale Ale (IPA) beers from the Uruguayan market were studied using GC-MS. Principal component analysis applied to the data allowed differentiation among the two groups of samples while the volatile compounds and physicochemical parameters responsible for these differences were identified. The physicochemical properties revealed a great diversity between all beer samples even within the same beer style. The main significant differences were obtained for alcohol, polyphenols, bitterness, colour, and pH. Most Blond Ale beer samples were differentiated from IPA ones by raw fermentation aroma compounds such as 1-pentanol, 1-hexanol, hexanoic and isobutyric acids, 4-vinyl guaiacol, and 5,5-dimethyl-2(5H)-furanone. This is the first work that contributes to the knowledge of Uruguayan craft beers. The study also showed the ability of most of the Uruguayan microbreweries to brew Blond Ale and IPA craft beer styles that meet international standards for physicochemical quality.

Biology and physiology of Hanseniaspora vineae: metabolic diversity and increase flavour complexity for food fermentation.

Apiculate yeasts belonging to the genus Hanseniaspora are predominant on grapes and other fruits. While some species, such as Hanseniaspora uvarum, are well known for their abundant presence in fruits, they are generally characterized by their detrimental effect on fermentation quality because the excessive production of acetic acid. However, the species Hanseniaspora vineae is adapted to fermentation and currently is considered as an enhancer of positive flavour and sensory complexity in foods. Since 2002, we have been isolating strains from this species and conducting winemaking processes with them. In parallel, we also characterized this species from genes to metabolites. In 2013, we sequenced the genomes of two H. vineae strains, being these the first apiculate yeast genomes determined. In the last 10 years, it has become possible to understand its biology, discovering very peculiar features compared to the conventional Saccharomyces yeasts, such as a natural and unique G2 cell cycle arrest or the elucidation of the mandelate pathway for benzenoids synthesis. All these characteristics contribute to phenotypes with proved interest from the biotechnological point of view for winemaking and the production of other foods.

Efficient Face Recognition System for Operating in Unconstrained Environments.

Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. Recently, several deep neural networks algorithms have been developed to achieve state-of-the-art performance on this task. The present work was conceived due to the need for an efficient and low-cost processing system, so a real-time facial recognition system was proposed using a combination of deep learning algorithms like FaceNet and some traditional classifiers like SVM, KNN, and RF using moderate hardware to operate in an unconstrained environment. Generally, a facial recognition system involves two main tasks: face detection and recognition. The proposed scheme uses the YOLO-Face method for the face detection task which is a high-speed real-time detector based on YOLOv3, while, for the recognition stage, a combination of FaceNet with a supervised learning algorithm, such as the support vector machine (SVM), is proposed for classification. Extensive experiments on unconstrained datasets demonstrate that YOLO-Face provides better performance when the face under an analysis presents partial occlusion and pose variations; besides that, it can detect small faces. The face detector was able to achieve an accuracy of over 89.6% using the Honda/UCSD dataset which runs at 26 FPS with darknet-53 to VGA-resolution images for classification tasks. The experimental results have demonstrated that the FaceNet+SVM model was able to achieve an accuracy of 99.7% using the LFW dataset. On the same dataset, FaceNet+KNN and FaceNet+RF achieve 99.5% and 85.1%, respectively; on the other hand, the FaceNet was able to achieve 99.6%. Finally, the proposed system provides a recognition accuracy of 99.1% and 49 ms runtime when both the face detection and classifications stages operate together.

A Teleophthalmology Support System Based on the Visibility of Retinal Elements Using the CNNs.

This paper proposes a teleophthalmology support system in which we use algorithms of object detection and semantic segmentation, such as faster region-based CNN (FR-CNN) and SegNet, based on several CNN architectures such as: Vgg16, MobileNet, AlexNet, etc. These are used to segment and analyze the principal anatomical elements, such as optic disc (OD), region of interest (ROI) composed by the macular region, real retinal region, and vessels. Unlike the conventional retinal image quality assessment system, the proposed system provides some possible reasons about the low-quality image to support the operator of an ophthalmoscope and patient to acquire and transmit a better-quality image to central eye hospital for its diagnosis. The proposed system consists of four steps: OD detection, OD quality analysis, obstruction detection of the region of interest (ROI), and vessel segmentation. For the OD detection, artefacts and vessel segmentation, the FR-CNN and SegNet are used, while for the OD quality analysis, we use transfer learning. The proposed system provides accuracies of 0.93 for the OD detection, 0.86 for OD image quality, 1.0 for artefact detection, and 0.98 for vessel segmentation. As the global performance metric, the kappa-based agreement score between ophthalmologist and the proposed system is calculated, which is higher than the score between ophthalmologist and general practitioner.

National Clinical Practice Guidelines for the management of non-small cell lung cancer in early, locally advanced and metastatic stages. Extended version. / Guía de Práctica Clínica Nacional para el manejo del cáncer de pulmón de células no pequeñas en estadios tempranos, localmente avanzados y metastásicos.

OBJECTIVE: Lung cancer is one the leading causes of mortality worldwide. Symptomatic manifestations of the disease generally occur in the advanced-stage setting, and therefore an important number of patients have advanced or metastatic disease by the time they are diagnosed. This situation contributes to a poor prognosis in the treatment of lung cancer. Evidencebased clinical recommendations are of great value to support decision-making for daily practice, and thus improving health care quality and patient outcomes. MATERIALS AND METHODS: This document was an initiative of the Mexican Society of Oncology (SMEO) in collaboration with Mexican Center of Clinical Excellence (Cenetec) according to Interna- tional Standards. Such standards included those described by the IOM, NICE, SIGN and GI-N. An interdisciplinary Guideline Development Group (GDG) was put together which included medical oncologists, surgical oncologistsc, radiation therapists, and methodologists with expertise in critical appraisal, sys- tematic reviews and clinical practice guidelines development. RESULTS: 62 clinical questions were agreed among members of the GDG. With the evidence identified from systematic reviews, the GDG developed clinical recommendations using a Modified Delphi Panel technique. Patients' representatives validated them. CONCLUSIONS: These Clinical Practice Guideline aims to support the shared decision-making process for patients with different stages of non-small cell lung cancer. Our goal is to improve health-care quality on these patients.

OBJETIVO: El cáncer de pulmón es una de las principales causas de mortalidad alrededor del mundo. Su historia natural, con la manifestación de síntomas en etapas avanzadas y el retraso en su diagnóstico hacen que una gran proporción de pacientes se diagnostiquen en estadios tardíos de la enfermedad, lo que hace muy complicado el tratamiento exitoso de la misma. De esto deriva la importancia de dar origen a recomendaciones basadas en evidencia para soportar la toma de decisiones clínicas por parte de los grupos interdisicplinarios que se encargan del manejo de este padecimiento. MATERIAL Y MÉTODOS: Este documento se desarrolló por parte de la Sociedad Mexicana de Oncología en colaboración con el Centro Nacional de Excelencia Tec- nológica de México (Cenetec) a través de la dirección de integración de Guías de Práctica Clínica en cumplimiento a estándares internacionales como los descritos por el Ins- tituto de Medicina de EUA (IOM, por sus siglas en inglés), el Instituto de Excelencia Clínica de Gran Bretaña (NICE, por sus siglas en inglés), la Red Colegiada para el Desarrollo de Guías de Escocia (SIGN, por sus siglas en inglés), la Red Internacional de Guías (G-I-N, por sus siglas en inglés); entre otros. Se integró en representación de la Sociedad Mexicana de Oncología un Grupo de Desarrollo de la Guía (GDG) de manera interdisciplinaria, considerando oncólogos médicos, cirujanos oncólogos, cirujanos de tórax, radio-oncólogos, y metodólogos con experiencia en revisiones sistemáticas de la literatura y guías de práctica clínica. RESULTADOS: Se consensuaron 62 preguntas cllínicas que abarcaron lo establecido previamente por el GDG en el documento de alcances de la Guía. Se identificó la evidencia científica que responde a cada una de estas preguntas clínicas y se evaluó críticamente la misma, antes de ser incorporada en el cuerpo de evidencia de la Guía. El GDG acordó mediante la técnica de consenso formal de expertos Panel Delphi la redacción final de las recomendaciones clínicas. C. CONCLUSIONES: Esta Guía de Práctica Clínica pretende proveer recomendaciones clínicas para el manejo de los distintos estadios de la enfermedad y que asistan en el proceso de toma de decisiones compartida. El GDG espera que esta guía contribuya a mejorar la calidad de la atención clínica en las pacientes con cáncer de pulmón de células no pequeñas.

Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach.

The counting of vehicles plays an important role in measuring the behavior patterns of traffic flow in cities, as streets and avenues can get crowded easily. To address this problem, some Intelligent Transport Systems (ITSs) have been implemented in order to count vehicles with already established video surveillance infrastructure. With this in mind, in this paper, we present an on-line learning methodology for counting vehicles in video sequences based on Incremental Principal Component Analysis (Incremental PCA). This incremental learning method allows us to identify the maximum variability (i.e., motion detection) between a previous block of frames and the actual one by using only the first projected eigenvector. Once the projected image is obtained, we apply dynamic thresholding to perform image binarization. Then, a series of post-processing steps are applied to enhance the binary image containing the objects in motion. Finally, we count the number of vehicles by implementing a virtual detection line in each of the road lanes. These lines determine the instants where the vehicles pass completely through them. Results show that our proposed methodology is able to count vehicles with 96.6% accuracy at 26 frames per second on average-dealing with both camera jitter and sudden illumination changes caused by the environment and the camera auto exposure.

Using Twitter Data to Monitor Natural Disaster Social Dynamics: A Recurrent Neural Network Approach with Word Embeddings and Kernel Density Estimation.

In recent years, Online Social Networks (OSNs) have received a great deal of attention for their potential use in the spatial and temporal modeling of events owing to the information that can be extracted from these platforms. Within this context, one of the most latent applications is the monitoring of natural disasters. Vital information posted by OSN users can contribute to relief efforts during and after a catastrophe. Although it is possible to retrieve data from OSNs using embedded geographic information provided by GPS systems, this feature is disabled by default in most cases. An alternative solution is to geoparse specific locations using language models based on Named Entity Recognition (NER) techniques. In this work, a sensor that uses Twitter is proposed to monitor natural disasters. The approach is intended to sense data by detecting toponyms (named places written within the text) in tweets with event-related information, e.g., a collapsed building on a specific avenue or the location at which a person was last seen. The proposed approach is carried out by transforming tokenized tweets into word embeddings: a rich linguistic and contextual vector representation of textual corpora. Pre-labeled word embeddings are employed to train a Recurrent Neural Network variant, known as a Bidirectional Long Short-Term Memory (biLSTM) network, that is capable of dealing with sequential data by analyzing information in both directions of a word (past and future entries). Moreover, a Conditional Random Field (CRF) output layer, which aims to maximize the transition from one NER tag to another, is used to increase the classification accuracy. The resulting labeled words are joined to coherently form a toponym, which is geocoded and scored by a Kernel Density Estimation function. At the end of the process, the scored data are presented graphically to depict areas in which the majority of tweets reporting topics related to a natural disaster are concentrated. A case study on Mexico's 2017 Earthquake is presented, and the data extracted during and after the event are reported.

Genomic and Transcriptomic Basis of Hanseniaspora vineae's Impact on Flavor Diversity and Wine Quality.

Hanseniaspora is the main genus of the apiculate yeast group that represents approximately 70% of the grape-associated microflora. Hanseniaspora vineae is emerging as a promising species for quality wine production compared to other non-Saccharomyces species. Wines produced by H. vineae with Saccharomyces cerevisiae consistently exhibit more intense fruity flavors and complexity than wines produced by S. cerevisiae alone. In this work, genome sequencing, assembling, and phylogenetic analysis of two strains of H. vineae showed that it is a member of the Saccharomyces complex and it diverged before the whole-genome duplication (WGD) event from this clade. Specific flavor gene duplications and absences were identified in the H. vineae genome compared to 14 fully sequenced industrial S. cerevisiae genomes. The increased formation of 2-phenylethyl acetate and phenylpropanoids such as 2-phenylethyl and benzyl alcohols might be explained by gene duplications of H. vineae aromatic amino acid aminotransferases (ARO8 and ARO9) and phenylpyruvate decarboxylases (ARO10). Transcriptome and aroma profiles under fermentation conditions confirmed these genes were highly expressed at the beginning of stationary phase coupled to the production of their related compounds. The extremely high level of acetate esters produced by H. vineae compared to that by S. cerevisiae is consistent with the identification of six novel proteins with alcohol acetyltransferase (AATase) domains. The absence of the branched-chain amino acid transaminases (BAT2) and acyl coenzyme A (acyl-CoA)/ethanol O-acyltransferases (EEB1) genes correlates with H. vineae's reduced production of branched-chain higher alcohols, fatty acids, and ethyl esters, respectively. Our study provides sustenance for understanding and potentially utilizing genes that determine fermentation aromas.IMPORTANCE The huge diversity of non-Saccharomyces yeasts in grapes is dominated by the apiculate genus Hanseniaspora Two native strains of Hanseniaspora vineae applied to winemaking because of their high oenological potential in aroma and fermentation performance were selected to obtain high-quality genomes. Here, we present a phylogenetic analysis and the complete transcriptome and aroma metabolome of H. vineae during three fermentation steps. This species produced significantly richer flavor compound diversity than Saccharomyces, including benzenoids, phenylpropanoids, and acetate-derived compounds. The identification of six proteins, different from S. cerevisiae ATF, with diverse acetyltransferase domains in H. vineae offers a relevant source of native genetic variants for this enzymatic activity. The discovery of benzenoid synthesis capacity in H. vineae provides a new eukaryotic model to dilucidate an alternative pathway to that catalyzed by plants' phenylalanine lyases.

Relationship between astringency and phenolic composition of commercial Uruguayan Tannat wines: Application of boosted regression trees.

Phenolic compounds play a major role in the intensity and characteristics of wine astringency. However, studies involving commercial wine samples are still scarce. The aim of the present work was to study the relationship between astringency and phenolic composition of commercial Uruguayan Tannat wines using boosted regression trees (BRT), a novel predictive method. Forty commercial Tannat wines were evaluated by a trained sensory panel (9 members), who assessed their total astringency intensity using time-intensity (TI) and described their astringency sub-qualities using a check-all-that-apply (CATA) question composed of sixteen terms. The polyphenolic profiles of the wines were determined by HPLC-MS and conventional oenological parameters were also obtained. Fifty BRT models with different partitions of the data in training and test sets were built for astringency maximum intensity (Imax) and for the frequency of use of the 16 astringency sub-qualities considered in the CATA question. As predictor variables, 84 phenolic compounds and oenological parameters were considered for all BRT models. Both strong and weak predictive models were obtained for each response variable. Predictive accuracy was much higher for astringency intensity than for the frequency of mention of astringency sub-qualities. Still, the BRT models allowed to point out to some compositional variables most likely involved in wine astringency perception. Total tannin concentration (chemically determined) was the most relevant explanatory variable for sensory astringency, while flavan-3-ols were the individual phenolic compounds with the highest contribution to astringency, particularly some dimers, trimers and the sum of non-galloylated tetramers. However, the effect of these predictors differed according to the astringency sub-quality considered as response. As expected, non-linear relationships between phenolic compounds and astringency were found. These results contribute to the understanding of the influence of phenolic composition on wine astringency and stress the potential of BRT models for identifying the compounds responsible for this complex sensory characteristic.

Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ1 Regularization.

In recent years, online social media information has been the subject of study in several data science fields due to its impact on users as a communication and expression channel. Data gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users’ opinions and make predictions about real events. Cyber-attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber-attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ 1 regularization.

Sensory characterization of the astringency of commercial Uruguayan Tannat wines.

Astringency is one of the most important characteristics that define the quality of red wine, and is of particular relevance for Tannat, Uruguayan emblematic red wine variety. Astringency is a time-dependant and complex sensory characteristic, related to several sensations, or sub-qualities, that can be simultaneously perceived. The aim of the present study was to obtain a sensory characterization of the astringency of commercial Uruguayan Tannat wines. Forty samples with different characteristics in terms of vintage, price segment and aging in oak barrels were assessed by a panel of 9 trained assessors. Total astringency intensity was evaluated using time-intensity (TI), while astringency sub-qualities were described using a check-all-that-apply (CATA) question composed of sixteen terms. TI and the CATA question provided different information on the astringency of Tannat wines. Regarding global astringency, samples mainly differed in intensity-related parameters rather than in the development of astringency over time, although the variability was moderate. A wide range of sub-qualities, from silky and velvety to harsh and aggressive were used to describe the astringency of the evaluated wines. Four groups of samples with different astringency characteristics were identified, but this sorting was not related to vintage, price segment or aging in oak barrels. Further research is necessary to better understand how astringency characteristics are influenced by production variables, and to understand their relationship to consumers' and experts' perceived quality of Tannat wines.

Cross View Gait Recognition Using Joint-Direct Linear Discriminant Analysis.

This paper proposes a view-invariant gait recognition framework that employs a unique view invariant model that profits from the dimensionality reduction provided by Direct Linear Discriminant Analysis (DLDA). The framework, which employs gait energy images (GEIs), creates a single joint model that accurately classifies GEIs captured at different angles. Moreover, the proposed framework also helps to reduce the under-sampling problem (USP) that usually appears when the number of training samples is much smaller than the dimension of the feature space. Evaluation experiments compare the proposed framework's computational complexity and recognition accuracy against those of other view-invariant methods. Results show improvements in both computational complexity and recognition accuracy.

Non-Saccharomyces and Saccharomyces strains co-fermentation increases acetaldehyde accumulation: effect on anthocyanin-derived pigments in Tannat red wines.

During fermentation, Saccharomyces cerevisiae releases into the medium secondary metabolic products, such as acetaldehyde, able to react with anthocyanins, producing more stable derived pigments. However, very limited reports are found about non-Saccharomyces effects on grape fermentation. In this study, six non-Saccharomyces yeast strains, belonging to the genera Metschnikowia and Hanseniaspora, were screened for their effect on red wine colour and wine-making capacity under pure culture conditions and mixed with Saccharomyces. An artificial red grape must was prepared, containing a phenolic extract of Tannat grapes that allows monitoring changes of key phenol parameters during fermentation, but without skin solids in the medium. When fermented in pure cultures, S. cerevisiae produced higher concentrations of acetaldehyde and vitisin B (acetaldehyde reaction-dependent) compared to M. pulcherrima M00/09G, Hanseniaspora guillermondii T06/09G, H. opuntiae T06/01G, H. vineae T02/05F and H. clermontiae (A10/82Fand C10/54F). However, co-fermentation of H. vineae and H. clermontiae with S. cerevisiae resulted in a significantly higher concentration of acetaldehyde compared with the pure S. cerevisiae control. HPLC-DAD-MS analysis confirmed an increased formation of vitisin B in co-fermentation treatments when compared to pure Saccharomyces fermentation, suggesting the key role of acetaldehyde. Copyright © 2016 John Wiley & Sons, Ltd.

How do consumers describe wine astringency?

Astringency is one of the most important sensory characteristics of red wine. Although a hierarchically structured vocabulary to describe the mouthfeel sensations of red wine has been proposed, research on consumers' astringency vocabulary is lacking. In this context, the aim of this work was to gain an insight on the vocabulary used by wine consumers to describe the astringency of red wine and to evaluate the influence of wine involvement on consumers' vocabulary. One hundred and twenty-five wine consumers completed and on-line survey with five tasks: an open-ended question about the definition of wine astringency, free listing the sensations perceived when drinking an astringent wine, free listing the words they would use to describe the astringency of a red wine, a CATA question with 44 terms used in the literature to describe astringency, and a wine involvement questionnaire. When thinking about wine astringency consumers freely elicited terms included in the Mouth-feel Wheel, such as dryness and harsh. The majority of the specific sub-qualities of the Mouth-feel Wheel were not included in consumer responses. Also, terms not classified as astringency descriptors were elicited (e.g. acid and bitter). Only 17 out of the 31 terms from the Mouth-feel Wheel were used by more than 10% of participants when answering the CATA question. There were no large differences in the responses of consumer segments with different wine involvement. Results from the present work suggest that most of the terms of the Mouth-feel Wheel might not be adequate to communicate the astringency characteristics of red wine to consumers.

Genome Sequence of the Native Apiculate Wine Yeast Hanseniaspora vineae T02/19AF.

The use of novel yeast strains for winemaking improves quality and provides variety including subtle characteristic differences in fine wines. Here we report the first genome of a yeast strain native to Uruguay, Hanseniaspora vineae T02/19AF, which has been shown to positively contribute to aroma and wine quality.

Redox effect on volatile compound formation in wine during fermentation by Saccharomyces cerevisiae.

Although redox state is a well-known key process parameter in microbial activity, its impact on wine volatile aroma compounds produced during fermentation has not been studied in detail. In this study we report the effect of reductive and microaerobic conditions on wine aroma compound production using different initial amounts of yeast assimilable nitrogen (YAN: 180 and 400 mg N/l) in a simil grape must defined medium and two S. cerevisiae strains commonly used in wine-making. In batch fermentation culture conditions, reductive conditions were obtained using flasks plugged with Muller valves filled with sulphuric acid; while microaerobic conditions were attained with defined cotton plugs. It was found that significant differences in redox potential were obtained using the different plugs, and with variation of over 100 mV during the main fermentation period. Significant differences in the final concentration of higher alcohols, esters and fatty acids were attributed to differences in the redox state in the medium in both strains. A consistent increase in esters and medium chain fatty acids, as well as a decrease of higher alcohols and isoacids, was seen under reductive fermentation conditions. Interestingly, 1-propanol, δ-butyrolactone and ethyl lactate concentrations, showed no significant variation under the different redox conditions. A better understanding of the influence of redox state of the fermentation medium on the composition of volatile compounds in wine could enable improvement of vinification management. From a microbiological standpoint results presented here will contribute to the standardization of data models for the application of metabolic footprinting methods for wine yeast strain phenotyping and characterization.

An early fire detection algorithm using IP cameras.

The presence of smoke is the first symptom of fire; therefore to achieve early fire detection, accurate and quick estimation of the presence of smoke is very important. In this paper we propose an algorithm to detect the presence of smoke using video sequences captured by Internet Protocol (IP) cameras, in which important features of smoke, such as color, motion and growth properties are employed. For an efficient smoke detection in the IP camera platform, a detection algorithm must operate directly in the Discrete Cosine Transform (DCT) domain to reduce computational cost, avoiding a complete decoding process required for algorithms that operate in spatial domain. In the proposed algorithm the DCT Inter-transformation technique is used to increase the detection accuracy without inverse DCT operation. In the proposed scheme, firstly the candidate smoke regions are estimated using motion and color smoke properties; next using morphological operations the noise is reduced. Finally the growth properties of the candidate smoke regions are furthermore analyzed through time using the connected component labeling technique. Evaluation results show that a feasible smoke detection method with false negative and false positive error rates approximately equal to 4% and 2%, respectively, is obtained.

Growth of non-Saccharomyces yeasts affects nutrient availability for Saccharomyces cerevisiae during wine fermentation.

Yeast produces numerous secondary metabolites during fermentation that impact final wine quality. Although it is widely recognized that growth of diverse non-Saccharomyces (NS) yeast can positively affect flavor complexity during Saccharomyces cerevisiae wine fermentation, the inability to control spontaneous or co-fermentation processes by NS yeast has restricted their use in winemaking. We selected two NS yeasts from our Uruguayan native collection to study NS-S. cerevisiae interactions during wine fermentation. The selected strains of Hanseniaspora vineae and Metschnikowia pulcherrima had different yeast assimilable nitrogen consumption profiles and had different effects on S. cerevisiae fermentation and growth kinetics. Studies in which we varied inoculum size and using either simultaneous or sequential inoculation of NS yeast and S. cerevisiae suggested that competition for nutrients had a significant effect on fermentation kinetics. Sluggish fermentations were more pronounced when S. cerevisiae was inoculated 24h after the initial stage of fermentation with a NS strain compared to co-inoculation. Monitoring strain populations using differential WL nutrient agar medium and fermentation kinetics of mixed cultures allowed for a better understanding of strain interactions and nutrient addition effects. Limitation of nutrient availability for S. cerevisiae was shown to result in stuck fermentations as well as to reduce sensory desirability of the resulting wine. Addition of diammonium phosphate (DAP) and a vitamin mix to a defined medium allowed for a comparison of nutrient competition between strains. Addition of DAP and the vitamin mix was most effective in preventing stuck fermentations.

Tandem repeat-tRNA (TRtRNA) PCR method for the molecular typing of non-Saccharomyces subspecies.

There is a worldwide trend to understand the impact of non-Saccharomyces yeast species on the process of winemaking. Although the predominant species at the end of the fermentation is Saccharomyces cerevisiae, several non-Saccharomyces species present during the first days of the process can produce and/or release aromas that improve the bouquet and complexity of the final wine. Since no genomic sequences are available for the predominant non-Saccharomyces species selected from grapes or musts (Hanseniaspora uvarum, Hanseniaspora vineae, Hanseniaspora opuntiae, Metschnikowia pulcherrima, Candida zemplinina), a reproducible PCR method was devised to discriminate strains at the subspecies level. The method combines different oligonucleotides based on tandem repeats with a second oligonucleotide based on a conserved tRNA region, specific for ascomycetes. Tandem repeats are randomly dispersed in all eukaryotic genomes and tRNA genes are conserved and present in several copies in different chromosomes. As an example, the method was applied to discriminate native M. pulcherrima strains but it could be extended to differentiate strains from other non-Saccharomyces species. The biodiversity of species and strains found in the grape ecosystem is a potential source of new enzymes, fungicides and/or novel sustainable methods for biological control of phytopathogens.

Effect of Saccharomyces cerevisiae inoculum size on wine fermentation aroma compounds and its relation with assimilable nitrogen content.

Different commercial Saccharomyces cerevisiae strains have been applied at the winemaking level, trying to establish a dominant population of selected strains from the start of fermentation and ensuring the complete consumption of sugars. Although a large population of active yeast cells can be introduced in the inoculated wines, resulting in a complete fermentation, this does not necessarily mean an improvement of the sensory characteristics of the wines. The impact of the size of the inocula in wine quality parameters has been very little studied, and in no case the nutrient balance of the grape must utilized was taken into account. In this work we present results obtained for wine aroma compounds at three inoculum levels (10(4), 10(5) and 10(6)cells/mL), and two different yeast assimilable nitrogen (YAN) in a white grape must, using two S. cerevisiae strains commonly used for winemaking. A significant effect in the final concentrations of higher alcohols, esters, fatty acids, free monoterpenes and lactones was attributed to the size of inoculum in both strains but not in an easily predictable way. However, a consistent increase of desired aroma compounds (esters, lactones and free monoterpenes), and a decrease of less desired compounds for white wine (higher alcohols and medium chain fatty acids), was shown at inoculum sizes of 10(5)cells/mL for both strains in real winemaking conditions. In a discriminant analysis six aroma compounds discriminate the three inoculum sizes for all wine samples: 1,8-terpine, hodiol I (trans-3,7-dimethyl-1,5-octadiene-3,7-diol), isobutyl alcohol, iso C4 acid, ethyl C6 ester and C8 acid.