Prediction Models to Control Aging Time in Red Wine

Machine learning-based predictive modelling for the enhancement of wine quality

The certification of wine quality is essential to the wine industry. The main goal of this work is to develop a machine learning model to forecast wine quality using the dataset. We utilised samples from the red wine dataset (RWD) with eleven distinct physiochemical properties. With the initial RWD, five machine learning (ML) models were trained and put to the test. The most accurate algorithms are Random Forest (RF) and Extreme Gradient Boosting (XGBoost). Using these two ML approaches, the top three features from a total of eleven features are chosen, and ML analysis is performed on the remaining features. Several graphs are employed to demonstrate the feature importance based on the XGBoost model and RF. Wine quality was predicted using relevant characteristics, often referred to as fundamental elements, that were shown to be essential during the feature selection procedure. When trained and tested without feature selection, with feature selection (RF), and with key attributes, the XGBoost classifier displayed 100% accuracy. In the presence of essential variables, the RF classifier performed better. Finally, to assess the precision of their predictions, the authors trained an RF classifier, validated it, and changed its hyperparameters. To address collinearity and decrease the quantity of predictors without sacrificing model accuracy, we have also used cluster analysis.

Artificial Neural Networks for Predicting Food Antiradical Potential

Using an artificial neural network (ANN), the values of the antiradical potential of 1315 items of food and agricultural raw materials were calculated. We used an ANN with the structure of a “multilayer perceptron” (MLP) and with the hyberbolic tangent (Tanh) as an activation function. Values reported in the United States Food and Nutrient Database for Dietary Studies (FNDDS) were taken as input to the analysis. When training the ANN, 60 parameters were used, such as the content of plastic substances, food calories, the amount of mineral components, vitamins, the composition of fatty acids and additional substances presented in this database. The analysis revealed correlations, namely, a direct relationship between the value of the antiradical potential (ARP) of food and the concentration of dietary fiber (r = 0.539) and a negative correlation between the value of ARP and the total calorie content of food (r = −0.432) at a significance level of p < 0.001 for both values. The average ARP value for 10 product groups within the 95% CI (confidence interval) was ≈23–28 equivalents (in terms of ascorbic acid) per 1 g of dry matter. The study also evaluated the range of average values of the daily recommended intake of food components (according to Food and Agriculture Organization—FAO, World Health Organization—WHO, Russia and the USA), which within the 95% CI, amounted to 23.41–28.98 equivalents per 1 g of dry weight. Based on the results of the study, it was found that the predicted ARP values depend not only on the type of raw materials and the method of their processing, but also on a number of other environmental and technological factors that make it difficult to obtain accurate values.

Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies

Aroma and other physicochemical parameters are important attributes influencing consumer perception and acceptance of rice. However, current methods using multiple instruments and laboratory analysis make these assessments costly and time-consuming. Therefore, this study aimed to assess rice quality traits of 17 commercial rice types using a low-cost electronic nose and portable near-infrared spectrometer coupled with machine learning (ML). Specifically, artificial neural networks (ANN) were used to classify the type of rice and predict rice quality traits (aromas, color, texture, and pH of cooked rice) as targets. The ML models developed showed that the chemometrics obtained from both sensor technologies successfully classified the rice (Model 1: 98.7%; Model 2: 98.6%) and predicted the peak area of aromas obtained by gas chromatography-mass spectroscopy found in raw (Model 3: R = 0.95; Model 6: R = 0.95) and cooked rice (Model 4: R = 0.98; Model 7: R = 0.96). Furthermore, a high R = 0.98 was obtained for Model 5 to estimate the color, texture, and pH of cooked rice. The proposed method is rapid, low-cost, reliable, and may help the rice industry increase high-quality rice production and accelerate the adoption of digital technologies and artificial intelligence to support the rice value chain.

Terroir and vintage discrimination of Malbec wines based on phenolic composition across multiple sites in Mendoza, Argentina

This study evaluated the phenolic profiles of Malbec wines made from grapes of 23 parcels distributed in 12 geographical indications (GIs) from Mendoza, Argentina. Wines were elaborated under standardized winemaking conditions over three consecutive vintages (2016–2018). Data discriminated wines from different GIs and parcels, based on an integrative data analysis by chemometric tools. Vintage effect and specific phenolic compounds were associated with some GIs or parcels. As well, regional climate conditions allowed partial discrimination of the GIs (and also some parcels). A random forest analysis correctly identified 11 out of 23 individual parcels across the different vintages. The most notorious compounds associated with such classification were p-coumaric acid, delphinidin-3-O-glucoside, caffeic acid, quercetin and peonidin-3-O-glucoside. The presented research allows to individualize, through phenolic profiles, parcels with unique characteristics over years. This is the first report characterizing Malbec wines coming from several GIs (and individual parcels) in different vintages. These results are strongly related to terroir features of wines, contributing to a better communication to consumers and to position Argentinean wines.

Bottle Aging and Storage of Wines: A Review

Wine is perhaps the most ancient and popular alcoholic beverage worldwide. Winemaking practices involve careful vineyard management alongside controlled alcoholic fermentation and potential aging of the wine in barrels. Afterwards, the wine is placed in bottles and stored or distributed in retail. Yet, it is considered that wine achieves its optimum properties after a certain storage time in the bottle. The main outcome of bottle storage is a decrease of astringency and bitterness, improvement of aroma and a lighter and more stable color. This is due to a series of complex chemical changes of its components revolving around the minimized and controlled passage of oxygen into the bottle. For this matter, antioxidants like sulfur oxide are added to avoid excessive oxidation and consequent degradation of the wine. In the same sense, bottles must be closed with appropriate stoppers and stored in adequate, stable conditions, as the wine may develop unappealing color, aromas and flavors otherwise. In this review, features of bottle aging, relevance of stoppers, involved chemical reactions and storage conditions affecting wine quality will be addressed.

Quality characteristics of white wine: The short- and long-term impact of high power ultrasound processing

This research aimed to analyze the effects of ultrasound on the quality characteristics of white wine when processed by two different systems, i.e., ultrasonic bath and ultrasonic probe. In this regard, the multivariate statistical analysis and artificial neural network (ANN) techniques were used. Additionally, the efficiency of high power ultrasound (HPU) combined with sulfite and glutathione (GSH) treatments was explored during 18 months of bottle storage. Regarding ultrasonic bath experiment, the higher bath temperature caused the degradation of volatile compounds, precisely esters and higher alcohols, while the ultrasound effect on phenolic composition was much less pronounced. Interestingly, a combination of larger probe diameter and higher ultrasound amplitude showed a milder effect on phenolic and volatile composition in ultrasonic probe experiment. Both, ultrasonic bath and probe experiments did not cause great changes in the color properties. Moreover, implemented ANN models for flavan-3-ols, higher alcohols and esters resulted in the highest prediction values. HPU processing after 18 months of storage did not affect wine color. However, it modified phenolic and volatile composition, with greater effect in wines with lower concentration of antioxidants. In addition, there was no significant difference in the phenolic and volatile composition among sonicated low-sulfite-GSH wine and the one with standard-sulfite content. Therefore, a combined HPU and low-sulfite-GSH treatment might be a promising method for production of low-sulfite wines.

Variation in secondary metabolites in a unique set of tomato accessions collected in Turkey

In this study, 50 tomato landraces grown in Turkey were investigated in terms of their secondary metabolite profiles. Each accession was planted in 2016 and 2017 in 3 replicates in an open field. In this study, color, pH and brix of the fruit samples were measured and an unbiased LCMS-based metabolomics approach was applied. Based on Principal Components Analysis (PCA) and Hierarchical Cluster Analysis (HCA) of the relative abundance levels of >250 metabolites, it could be concluded that fruit size was the most influential to the biochemical composition, rather than the geographical origin of accessions. Results indicated substantial biodiversity in various metabolites generally regarded as key to fruit quality aspects, including sugars; phenolic compounds like phenylpropanoids and flavonoids; alkaloids and glycosides of flavour-related volatile compounds. The phytochemical data provides insight into which Turkish accessions might be most promising as starting materials for the tomato processing and breeding industries.