Discrimination of the geographic origins and varieties of wine grapes using high-throughput sequencing assisted by a random forest model

Improved Classification Performance of Bacteria in Interference Using Raman and Fourier-Transform Infrared Spectroscopy Combined with Machine Learning

The rapid and sensitive detection of pathogenic and suspicious bioaerosols are essential for public health protection. The impact of pollen on the identification of bacterial species by Raman and Fourier-Transform Infrared (FTIR) spectra cannot be overlooked. The spectral features of the fourteen class samples were preprocessed and extracted by machine learning algorithms to serve as input data for training purposes. The two types of spectral data were classified using classification models. The partial least squares discriminant analysis (PLS-DA) model achieved classification accuracies of 78.57% and 92.85%, respectively. The Raman spectral data were accurately classified by the support vector machine (SVM) algorithm, with a 100% accuracy rate. The two spectra and their fusion data were correctly classified with 100% accuracy by the random forest (RF) algorithm. The spectral processed algorithms investigated provide an efficient method for eliminating the impact of pollen interference.

Influence of climatic variation on microbial communities during organic Pinot noir wine production

To assess the possible impact of climatic variation on microbial community composition in organic winemaking, we employed a metabarcoding approach to scrutinize the microbiome in a commercial, organic, Pinot noir wine production system that utilizes autochthonous fermentation. We assessed microbial composition across two vintages (2018 and 2021) using biological replicates co-located at the same winery. Microbial dynamics were monitored over four important fermentation time points and correlated with contemporaneous climate data. Bacterial (RANOSIM = 0.4743, p = 0.0001) and fungal (RANOSIM = 0.4738, p = 0.0001) compositions were different in both vintages. For bacteria, Lactococcus dominated the diversity associated with the 2018 vintage, while Tatumella dominated the 2021 vintage. For fungal populations, while Saccharomyces were abundant in both vintages, key differences included Starmerella, copious in the 2018 vintage; and Metschnikowia, substantive in the 2021 vintage. Ordination plots correlated the climatic variables with microbial population differences, indicating temperature as a particularly important influence; humidity values also differed significantly between these vintages. Our data illustrates how climatic conditions may influence microbial diversity during winemaking, and further highlights the effect climate change could have on wine production.

Microbial diversity on grape epidermis and wine volatile aroma in spontaneous fermentation comprehensively driven by geography, subregion, and variety

On their journey from the wine grape to the resulting wine, microbiota from grape surfaces controlled by multiple factors is transferred to wine spontaneous fermentation process with indisputable consequences for wine quality parameters. The associated microbiota was regionally distinct (defined to microbial terroir) but how these microbial patterns with significantly regional distinctiveness quantitatively drive the wine regional characteristics are not definite within a complete grape ecosystem at different geographical (> 300 km), subregional (< 10 km), and varietal scales. Here, we collected 24 samples (containing two grape varieties) from four subregions of two regions in Xinjiang wine production area to investigate fungal distribution patterns and the association with wine chemical composition at different evaluation scales. Meanwhile, the relationships were established between geographical, subregional, varietal community of fungi, and wine volatile aroma using partial least squares regression (PLSR) and structural equation modeling (SEM). Results show that microbial and volatile samples present the significantly regional difference inside the complete ecosystem. Microbiota showed a stronger heterogeneity at geography scales, which drove the distributions of subregional and varietal microbiota thereby influencing the volatile composition of finished wines. Moreover, geographical microbiota seems to weaken the effects of varietal community on wine aroma compounds. Microbial communities respond to environmental changes within a completely set grape-related ecosystem at different scales, and these responses resulted in the wine regional distinctiveness based on the volatile profiles. Our findings further confirmed the important role of microbial terroir in shaping wine styles and provided the new cerebration for the terroir drivers of microbiota.

Exploring the ecological characteristics of natural microbial communities along the continuum from grape berries to winemaking

Under natural conditions, a complex and dynamic microbial ecosystem exists on the grape epidermis, which plays an important role in safeguarding grape health and facilitating the conversion of grapes into wine. However, current viticulture and vinification are flooded with excessive chemical additives and commercial ferments, leading to a reduction in microbial diversity, affecting the ecological balance of the natural microbiota and masking the wine terroir. This experiment comprehensively explored the continuous changes in the natural microbiota from the Ecolly (Vitis vinifera L.) grape epidermis to spontaneous fermentation over two years. The results suggested that microbial community structure and composition were significantly influenced by vintage and growing stage, with fungal genera being more stable than bacterial genera during the growing season. The fungal genera Alternaria, Ascochyta, Gibberella and Dissoconium and the bacterial genera Pantoea, Sediminibacterium, Ralstonia and Sphingomonas were mainly present on the grape epidermis in both years. The natural microbial diversity decreased from grape growth to spontaneous fermentation, and the fermentation environment reshapes the community structure, composition and diversity of the wine microbial consortium. These findings provide insights to promote cultivation and fermentation management strategies, advocate natural terroir attributes for grapes and wines, and promote sustainable development of the wine industry.

Could Collected Chemical Parameters Be Utilized to Build Soft Sensors Capable of Predicting the Provenance, Vintages, and Price Points of New Zealand Pinot Noir Wines Simultaneously?

Soft sensors work as predictive frameworks encapsulating a set of easy-to-collect input data and a machine learning method (ML) to predict highly related variables that are difficult to measure. The machine learning method could provide a prediction of complex unknown relations between the input data and desired output parameters. Recently, soft sensors have been applicable in predicting the prices and vintages of New Zealand Pinot noir wines based on chemical parameters. However, the previous sample size did not adequately represent the diversity of provenances, vintages, and price points across commercially available New Zealand Pinot noir wines. Consequently, a representative sample of 39 commercially available New Zealand Pinot noir wines from diverse provenances, vintages, and price points were selected. Literature has shown that wine phenolic compounds strongly correlated with wine provenances, vintages and price points, which could be used as input data for developing soft sensors. Due to the significance of these phenolic compounds, chemical parameters, including phenolic compounds and pH, were collected using UV-Vis visible spectrophotometry and a pH meter. The soft sensor utilising Naive Bayes (belongs to ML) was designed to predict Pinot noir wines' provenances (regions of origin) based on six chemical parameters with the prediction accuracy of over 75%. Soft sensors based on decision trees (within ML) could predict Pinot noir wines' vintages and price points with prediction accuracies of over 75% based on six chemical parameters. These predictions were based on the same collected six chemical parameters as aforementioned.

Impacto de levaduras autóctonas no Saccharomyces en la reducción de etanol y perfil químico del vino chileno Sauvignon blanc

El estudio de levaduras No-Saccharomyces (NSY) en fermentaciones enológicas permite explorar nuevas alternativas para la reducción de etanol en vinos. El objetivo de este trabajo fue evaluar la capacidad fermentativa de dos levaduras autóctonas y poco exploradas del tipo NSY (NSYa, NSYb) en fermentaciones monocultivo y secuenciales (escala laboratorio y microvinificación) para producir vino Sauvignon Blanc chileno. Las fermentaciones se monitorearon mediante la determinación de etanol, glicerol, ácidos orgánicos y azúcares residuales. Los resultados indicaron que a escala de laboratorio tanto para las fermentaciones monocultivo y secuenciales fue posible reducir la concentración de etanol, a un 0,77% v/v (monocultivo) y 1,5% v/v (secuencial) para NSYa y 0,50% v/v (monocultivo) y 0,04% v/v (secuencial) para NSYb comparado con S. cerevisiae (12,87% v/v). Adicionalmente, mayores concentraciones de glicerol fueron obtenidas en fermentaciones monocultivo en comparación a las secuenciales (NSYa: 9,47 g/L y NSYb 10,97 g/L). A escala de microvinificación, las fermentaciones monocultivo y secuenciales con NSYb lograron reducir el contenido de etanol en 0,17% v/v y 0,54% v/v, respectivamente, comparado al control de S. cerevisiae (13,74% v/v). En el caso de NSYa, la reducción solo se observó en fermentaciones secuenciales con 0,62% v/v

Artificial intelligence-based approaches for traditional fermented alcoholic beverages' development: review and prospect

Traditional fermented alcoholic beverages (TFABs) have gained widespread acceptance and enjoyed great popularity for centuries. COVID-19 pandemics lead to the surge in health demand for diet, thus TFABs once again attract increased focus for the health benefits. Though the production technology is quite mature, food companies and research institutions are looking for transformative innovation in TFABs to make healthy, nutritious offerings that give a competitive advantage in current beverage market. The implementation of intelligent platforms enables companies and researchers to gather, store and analyze data in a more convenient way. The development of data collection methods contributed to the big data environment of TFABs, providing a fresh perspective that helps brewers to observe and improve the production steps. Among data analytical tools, Artificial Intelligence (AI) is considered to be one of the most promising methodological approaches for big data analytics and decision-making of automated production, and machine learning (ML) is an important method to fulfill the goal. This review describes the development trends and challenges of TFABs in big data era and summarize the application of AI-based methods in TFABs. Finally, we provide perspectives on the potential research directions of new frontiers in application of AI approaches in the supply chain of TFABs.

Bacterial community diversity and its potential contributions to the flavor components of traditional smoked horsemeat sausage in Xinjiang, China

Smoked horsemeat sausage is a famous fermented traditional food in Xinjiang, China. However, the microbial diversity and its potential contributions to the flavor components of smoked horsemeat sausage are unclear. In this study, the microbial community and flavor components of smoked horsemeat sausage from six regions of Xinjiang were measured by using amplicon sequencing and headspace solid-phase microextraction combined with gas chromatography–mass spectrometry (HS-SPME-GC–MS) technology, respectively. Relations among microbial communities, flavor components and environmental factors were subsequently predicted based on redundancy analysis (RDA) and Monte Carlo permutation tests. Although smoked horsemeat sausage samples from different regions possessed distinct microbial communities, lactic acid bacteria (LAB) were identified as the dominant consortium in smoked horsemeat sausage. Lactobacillus, Vagococcus, Lactococcus, and Carnobacterium were detected at high abundance in different sausages. The moisture content, nitrite content, and pH of the sausage might be important factors influencing the dominant bacterial community, according to the RDA. Among the dominant consortia, the eight core bacterial genera showed considerable correlations with the formation of sixteen volatile compounds in smoked horsemeat sausage based on multivariate statistical analysis. For example, the levels of Leuconostoc and Lactobacillus were positively correlated with those of 1-hexadecanol, hexyl acetate, 2-methyl-phenol, 1-pentanol, d-limonene, and 2-heptanone, and the levels of Leuconostoc, Lactobacillus, and Weissella were negatively correlated with those of 1-octanol, acetic acid, octanal, heptanal, and 1-hexanol. This study will provide a theoretical basis for understanding the microbial metabolic modes of Xinjiang smoked horsemeat sausages.

Multi-Element Analysis and Origin Discrimination of Panax notoginseng Based on Inductively Coupled Plasma Tandem Mass Spectrometry (ICP-MS/MS)

Panax notoginseng is an important functional health product, and has been used worldwide because of a wide range of pharmacological activities, of which the taproot is the main edible or medicinal part. However, the technologies for origin discrimination still need to be further studied. In this study, an ICP-MS/MS method for the accurate determination of 49 elements was established, whereby the instrumental detection limits (LODs) were between 0.0003 and 7.716 mg/kg, whereas the quantification limits (LOQs) were between 0.0011 and 25.7202 mg/kg, recovery of the method was in the range of 85.82% to 104.98%, and the relative standard deviations (RSDs) were lower than 10%. Based on the content of multi-element in P. notoginseng (total of 89 mixed samples), the discriminant models of origins and cultivation models were accurately determined by the neural networks (prediction accuracy was 0.9259 and area under ROC curve was 0.9750) and the support vector machine algorithm (both 1.0000), respectively. The discriminant models established in this study could be used to support transparency and traceability of supply chains of P. notoginseng and thus avoid the fraud of geographic identification.

Evaluation of Indigenous Candida oleophila and Candida boidinii in Monoculture and Sequential Fermentations: Impact on Ethanol Reduction and Chemical Profile in Chilean Sauvignon Blanc Wines

The study of non-Saccharomyces yeasts in wine fermentations allows the exploration of new alternatives for the reduction of ethanol in wines. The objective of this work was to evaluate the fermentation capacity of two indigenous Candida yeasts (C. oleophila and C. boidinii) in monoculture and sequential fermentations (laboratory and microvinification scale) to produce Chilean Sauvignon Blanc wine. Fermentations were monitored by the determination of ethanol, glycerol, organic acids, and residual sugars. The results indicated that at the laboratory scale for both the monoculture and sequential fermentations it was possible to reduce the ethanol concentration on 0.77% v/v (monoculture) and 1.5% v/v (sequential) for C. oleophila and 0.50% v/v (monoculture) and 0.04% v/v (sequential) for C. boidinii compared to S. cerevisiae (12.87% v/v). Higher glycerol concentrations were produced in monoculture than sequential fermentations (C. oleophila: 9.47 g/L and C. boidinii 10.97 g/L). For microvinifications, the monoculture and sequential fermentations with C. boidinii managed to reduce ethanol content by 0.17% v/v and 0.54% v/v, respectively, over the S. cerevisiae control (13.74% v/v). In the case of C. oleophila, the reduction was only observed in sequential fermentations with 0.62% v/v. Interestingly, grapes with higher sugar concentration resulted in wines with lees ethanol concentrations. This might be associated to the use of C. oleophila (13.12% v/v) and C. boidinii (13.20% v/v) in sequential fermentations microvinification scale.

A fundamental landscape of fungal biogeographical patterns across the main Chinese wine-producing regions and the dominating shaping factors

The microbial terroir plays an indispensable role in the formation of regional wine characteristics. A fundamental landscape of the fungal biogeographical patterns across Chinese wine-producing regions was obtained by characterizing the fungal communities in spontaneous fermentation. After confirming the established national microbial terroir, the fungal heterogeneity was evaluated at different geographical levels. The result showed that the variation between the wineries was more evident than at a regional level. Moreover, the microbial comparability from various regions with similar climates or wineries within the same regions was revealed. Further discriminant analysis determined the specific fungal biomarkers in different regions, while the associated reverse identification model displayed reliable accuracy (>70%). Correlation analysis illustrated the primary role of the geoclimatic factors (>41%) in shaping the fungal geographical patterns, and the relationship between the microbiome and spontaneous fermentation performance. In addition to expanding the knowledge regarding wine microbes, these findings provided a new benchmark for harnessing the microbial terroir to enhance regional wine expression.