Doctoral Degrees (Viticulture and Oenology)

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    Investigation into physical and metabolic interactions within the wine yeast ecosystem
    (Stellenbosch : Stellenbosch University, 2021-12) Luyt, Natasha Alethea; Bauer, Florian; Divol, Benoit; Setati, Mathabatha Evodia ; Stellenbosch University. Faculty of AgriSciences. Dept. of Viticulture and Oenology.
    ENGLISH ABSTRACT: The microbial community of the wine ecosystem consist of filamentous fungi, bacteria and yeast. These organisms interact and compete for space and nutrients throughout fermentation. Since yeast are the primary contributors to alcoholic fermentation, various studies have described and characterized the biotic and abiotic factors which may influence yeast-yeast interactions. Through this search for a fundamental understanding of interactions, physical and metabolic interaction have emerged as pivotal drivers of population dynamics during fermentation. Nevertheless, these interactions remain elusive and the molecular mechanisms behind them remain poorly described. This study aimed at characterizing cell-cell and metabolic interactions between Saccharomyces cerevisiae and Lachancea thermotolerans from a phenotypic and molecular viewpoint. To achieve these outcomes, synthetic grape must fermentations were performed in a compartmentalised bioreactor, followed by a transcriptomic analysis which evaluated the effect of cell-cell and metabolic contact on gene expression and finally, a qRT-PCR approach, further evaluating the expression of specific genes of interest. The data confirmed the existence of an antagonistic relationship between S. cerevisiae and L. thermotolerans, which has been previously reported. It was observed that the presence of S. cerevisiae caused cellular death in L. thermotolerans in a cell-cell and metabolic contact dependant manner and the former appears more important in S. cerevisiae’s strategy to outcompete L. thermotolerans. In turn, the data also suggest that the metabolic activity of L. thermotolerans has a negative effect on the culturability of S. cerevisiae. Analysing the transcriptomic responses as a result of cell-cell and metabolic contact revealed distinct responses in both yeasts. S. cerevisiae reacted in a targeted manner, reinforcing its cell wall through the up-regulation of genes associated with maintaining cell wall integrity and structural components of the cell wall. L. thermotolerans showed a different response, with in particular strongly up-regulated heat shock genes, some of which have previously been linked to interspecies interaction. Both yeasts avoided co-aggregation by expressing adhesion genes less when in physical contact. Genes of interest were identified and their expression was further monitored throughout different stages of fermentation and investigated as to whether these responses were generic or species-specific. In S. cerevisiae, PAU, TIR2, HSP12 and FLO gene regulation occurred in a species-specific manner when evaluated in co-fermentations. While the regulation of adhesion FLO genes occurred in a species-specific manner between two closely related non-Saccharomyces yeasts, the role of HSP genes appeared to be conserved between the two. The avoidance of co-adhesion appeared to be a generic response, both in S. cerevisiae and non-Saccharomyces yeasts. The data provide novel insights into the transcriptomic responses to cell-cell contact in S. cerevisiae and non-Saccharomyces yeasts. Furthermore, the data provides a basis for future annotation of the S. cerevisiae genome to include the role of genes in ecological interactions.
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    Evolutionary engineering of interspecies cooperation: Investigating Saccharomyces cerevisiae and Lactobacillus plantarum interactions in a synthetic ecological environment
    (Stellenbosch : Stellenbosch University, 2021-12) Du Toit, Sandra Christine; Bauer, Florian; Rossouw, Debra; Du Toit, Maret; Stellenbosch University. Faculty of AgriSciences. Faculty of Viticulture and Oenology.
    ENGLISH ABSTRACT: Saccharomyces cerevisiae and lactic acid bacteria (LAB), like Lactiplantibacillus plantarum, form part of the wine microbiome, where they each play a part in the biochemical conversion of grape must to wine. Saccharomyces cerevisiae converts grape sugars to ethanol and carbon dioxide during alcoholic fermentation (AF) and Lb. plantarum converts malic acid to lactic acid during malolactic fermentation (MLF). Physical and metabolic interactions between S. cerevisiae and LAB are often inhibitory to the growth of the bacteria, which hinders the successful completion of MLF. Despite extensive research, the interactions between these species are still poorly understood and the natural complexity of grape must hinders the study of these interactions within the natural ecological environment. This dissertation evaluated the applicability of a combined synthetic ecology and evolutionary engineering approach to better understand and improve the interactions between these species. Nutrient co-dependency between S. cerevisiae BY4742Δthi4 (lysine auxotroph) and Lb. plantarum IWBT B038 (isoleucine and valine auxotroph) was used to ensure both species co-exist during the evolutionary period and to select for improved species cooperation. Overall, the data show that this system can be used to investigate the phenotypic and genetic changes involved in the coevolution of trans-kingdom ecosystems. However, the applicability of the system for the generation of yeast- bacteria pairings with improved oenological characteristics still needs to be further investigated. Under strong selective conditions, when lysine and isoleucine are omitted from the synthetic grape juice media, the bidirectional support in the mutualistic system required optimisation. This was achieved by inoculating BY4742Δthi4 and IWBT B038 at equal biomass concentrations. It was hypothesised that the release of small peptides by BY4742Δthi4 shortly after inoculation may be important for initiating IWBT B038 growth, while the release of nutrients by IWBT B038 due to membrane damage during the later stages of fermentation may be important for BY4742Δthi4 growth. The strains were evolved under coculture and monoculture conditions using different amino acid treatments. Overall, the data show that coevolution under selective conditions selected for isolates with improved cooperative interactions, relative to isolates coevolved under non-selective conditions (no amino acids omitted) and isolates evolved in monoculture. Three evolved yeast isolates showing improved growth and sugar consumption characteristics were subjected to whole-genome sequencing. Although various genetic mutations could be identified in these isolates, the mutations could not be linked to the observed phenotypes. Regardless, the FLO9 and ECM21 genes showed interesting mutations and should be investigated further to determine what role they play in the adaptation of BY4742Δthi4 to the imposed selective conditions.
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    Pinking of wine – influence of different winemaking processes, causative agents and pinking treatments
    (Stellenbosch : Stellenbosch University, 2021-03) Nel, Anton Pieter; Van Jaarsveld, Francois; Du Toit, Wessel J.; Stellenbosch University. Faculty of AgriSciences. Dept. of Viticulture and Oenology.
    ENGLISH ABSTRACT: Pinking is a slight salmon-red discolouration in certain white wines after some oxygen ingress. This discolouration can lead to some economical loss to the winemaker. This phenomenon was first described in the late 1960s to the early 1970s. In the 50 odd years that this phenomenon is known, only about 4 peer-reviewed scientific articles were ever written with a handful of popular articles. In the first article on pinking, the researcher looked at how to test for pinking susceptibility (PS) as well as the influence of SO2 and pH on a pinked wine. This article established the method that is now used all over wine-producing countries. The influence of SO2 was established and the outcome was that the wine needs at least 40 mg/L of SO2 to protect the wine against PS. It was also established that the change in pH does not influence the pink colour showing that pinking is not associated with anthocyanins. In another article, preventative agents were researched. It was found that PVPP can bind certain compounds, presumably phenolic compounds, and settle them out, decreasing the PS. This research led to a phenomenal increase in the sale of PVPP. Literature indicated the possibility of anthocyanins in white wine and found malvidin-3-O-glucoside to play a role in PS of Siria grapes. In this study, the influence of temperature, skin contact time and pressing methods were researched on PS. Whole bunch press was tested against crushed & destemmed grapes. Dissolved oxygen (DO) was also measured to deduce the influence of oxygen on PS. The DO of the whole bunch pressed grapes was significantly higher than the crushed & destemmed grapes. This was due to more air space between the berries. Micro oxygenation in the initial stages of the winemaking practices led to a higher PS in the whole bunch pressed wines. The temperature had the highest influence on PS. Cooler (4ºC) grapes also had a significantly higher DO than grapes at ambient (20ºC) temperature. This finding was confirmed by different winemakers which had problems with pinking. All of them picked their Sauvignon blanc grapes at the coolest temperature, keeping them as cool as possible before processing. This and the higher DO in the juice help with the oxidation of the pinking precursors leading to a wine with a high PS. Longer skin contact time leads to a higher potential to pink, but this PS was not significant. When long skin contact time was combined with a cool temperature, the PS increases significantly. In two articles by Portuguese researchers, the presence of malvidin-3-O-glucoside was established. The theory that all cultivars have the genes to produce anthocyanins, but that the genes are repressed in white wines grapes, could hold some truth. In UV-visible spectrophotometric analysis, the spectra of pinked wines and control wines were analysed. The ranges where the differences in spectra were visible was not associated with any phenolic or anthocyanin compounds. During LC-MS analysis of the peaks showed that the compounds causing PS does not fit any of the known wine phenols. During the LC-MS analysis, the mass and retention times were given. The masses were compared to the library of the LC-MS and no fits to the mass data were established, thus making the identification of the compounds near impossible. A sensory analysis was done on pinked wines which are novel work and was never done before. The anecdotal evidence shows that pinked wine does not influence the aroma and taste. This was tested against a trained panel. It was found that the trained panel could not pick up the difference between a pinked wine and a control wine, although some of the tasters could pick up oxidation characters on the aroma. The sensory study showed that the panelists could pick up differences by smell but not on the taste of the wines. This study proved the anecdotal evidence and could lead to further sensory studies in the sensory science of pinked white wines. This study paved the way for further sensory research on pinked wines. The data also showed that panels can now be trained in identifying pink wine as a wine fault or to contribute a new style of wine to the public.
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    Natural white wine alcoholic fermentation: a focus on progression trajectories and sensory outcomes
    (Stellenbosch : Stellenbosch University, 2021-03) Kruger, Marinda; Nieuwoudt, Hélène H; Stellenbosch University. Faculty of AgriSciences. Dept. of Viticulture and Oenology.
    ENGLISH ABSTRACT: Wine is the result of the impact of collective production decisions made from the vineyard and throughout the steps taken in the winery. Nowadays, consumers have access to wines from across the world. This has resulted in an elevated consumer-demand for wines that demonstrate, in sensorial terms, individuality, exceptional quality, and provenance. Wine producers interpret this hugely competitive market-pull through alternative winemaking strategies, such as natural or spontaneous fermentations, in the belief that wines produced in this way reflect uniqueness and authenticity. The sensory profile of a wine is hugely influenced by the fermentation regime chosen by the oenologist or winemaker. Alcoholic fermentation is the bioprocess whereby grape sugars, which mainly consist of glucose and fructose, are converted by yeasts to ethanol, CO2, and secondary metabolites. The sensory profiles of some white wine styles are largely determined by the grape flavour compounds and those derived during alcoholic fermentation. Alcoholic fermentation (AF) is, arguably, the most important step in winemaking and, therefore, the control and monitoring of this bioprocess is of the utmost importance for a predictable duration and outcome, as well as reproducibility from one vintage to the next. Fourier Transform mid-infrared (FT-MIR) spectroscopy is well implemented, in wine laboratories, for routine chemical analysis of alcoholic fermentation parameters. Extensive research exists for quantification calibration development using FT-MIR spectroscopy but only few studies where the FT-MIR spectra are used for qualitative calibrations. No studies for explorative data mining of the information-rich FT-MIR spectra of AF could be found. The visualisation of big data is receiving much attention. Visualisation of data using multivariate data analysis techniques, gives a clear idea of what the information means, it highlights trends, patterns, and outliers. In this study, the visualisation of AF process data is novel. Using Chardonnay grape must (Data set 1), fermented at a constant temperature and a three- by-three experimental design, it was possible to visualise the variation of the progression trajectories between the fermentations. The data consisted of FT-MIR spectra and chemical parameters which aids the interpretation of the progression trajectories. Statistical data analysis of the chemical parameters correlated with the visualisation of the FT-MIR spectral data and chemical parameters using multiway partial least square regression (MPLS) or batch evolution modeling (the term used in this study). PCA of the PLS scores of the BEM, the fermentations could be visually compared on the PCA score plot. Further to determine class separation by orthogonal PLS discriminant analysis (OPLS-DA) confirmed correlation and variation between the fermentations. The second data set (Data set 2) was historical, Chenin blanc, Colombard and Chardonnay fermentation data, from a commercial winery. The fermentations all fermented to dry (residual sugars < 5g/L). No prior knowledge existed of which yeasts were used, only that best fermentation practises were applied. The time trajectory of alcoholic fermentations varies greatly. To compare and monitor fermentations effectively the biological state at a certain point needs to be the same in relation to the process. A relative time scale was introduced in this study to realign the data and put it on a generic time basis. The PLS model with X- FT-MIR spectra and Y-relative time demonstrated significant statistical indicators. Concluding that relative time implemented in the multivariate model, ensures correct interpretation of the progression trajectories of a given point in time and that the prediction of fermentation time is possible. Natural or uninoculated fermentation introduces more variation within the FT-MIR spectral data. The reason being that these fermentations are not inoculated with a Saccharomyces cerevisiae yeast, as in the first two data sets. In natural fermentation, the grape must is fermented by the indigenous S. cerevisiae and non-Saccharomyces (NS) yeasts. Yeast-to-yeast interaction occurs which introduces more variation than an inoculated commercial yeast fermentation. It was important to compare the between variation of the different fermentations. The third data set (Data set 3), comprised of Sauvignon blanc, this global popular cultivar was chosen as a solid foundation exists due to previous research. Inoculated and natural fermentations on a micro- and commercial scale was performed. S. cerevisiae and Torulaspora delbrueckii was each inoculated in one small-scale fermentation, respectively. Two co-inoculation fermentations, the sequential inoculation strategy of T. delbrueckii and S. cerevisiae, as well as two natural fermentations were performed on small- and commercial-scale. FT-MIR spectroscopy spectral data was acquired during each of the fermentations, chemical parameters were determined through the calibrations from the FT120 Winescan, the chemical aroma compounds were measured as well as sensory profiling using sensory descriptive analysis method of the finished wines. Natural fermentations had lower fermentation kinetics and thus has a longer duration time than inoculated fermentations. BLM, with relative time (Y-variable) and the FT-MIR spectra (X-variables), visualised the variances between the fermentations. The S. cerevisiae inoculated fermentation was more different in comparison to the other fermentations. This was seen on the multivariate statistical control charts (MSPC) or batch control charts, as the fermentation sat well above the other fermentations which indicated higher fermentation kinetics. The PCA score plot of BEM also confirmed the variation visually. A point-to-point function on the MSPC charts demonstrated the loading weights for glucose, fructose, and ethanol, responsible for this variation. It was established that natural fermentation can also be monitored and compared in the same model as inoculated fermentations. PLS-Trees® , hierarchical classification method, was used to explore the within variation of the fermentation spectral data. Three data clusters were identified within the progression of inoculated fermentations. However, within the natural and co-inoculated fermentations four data clusters were identified. It could be speculated that more variation exists within the fermentation due to the longer fermentation time. The changing relationship of the FT-MIR spectra and chemical parameters between the clusters could be interpreted for insight, through the respective loading weights. A possible application will be that a local predictive model based on this insight will be more accurate, a lower standard error of calibration, than a global model. It was of interest to investigate if correlation exists between the BEM of the FT-MIR spectral fermentation data and the sensory descriptive and aroma compounds data of the corresponding finished wines. This was a three-data block comparison. Multiblock orthogonal component analysis (MOCA) finds the joint and unique variation between the data blocks. Joint variation and correlation were found between the three data blocks. No unique variation exists between the three data blocks. However, from the PCA score plot of the MOCA model observation C (small- scale co-inoculation) had larger variations between the three block models. Observation F (commercial-scale co-inoculation) had the least variation between the three block models. The other observations demonstrated similar variations. The reason for this could not be determined in this study and needs further research. The correlation between the three data blocks makes qualitative calibrations a possibility. The positive contribution is that qualitative outcomes could be possibly predicted during certain stages of alcoholic fermentation and encourages further research in this field.
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    Non-destructive evaluation of external and internal table grape quality
    (Stellenbosch : Stellenbosch University, 2021-03) Daniels, Andries Jerrick; Opara, Umezuruike Linus; Nieuwoudt, Hélène H; Stellenbosch University. Faculty of AgriSciences. Dept. of Viticulture and Oenology.
    ENGLISH ABSTRACT: Determining the correct harvest maturity parameters of table grapes is an essential step before harvesting. The chemical analysis of table grapes to determine harvest and quality parameters such as total soluble solids (TSS), titratable acidity (TA) and pH, is very time-consuming, expensive, and destructive. Developing faster and more cost-effective methods to obtain the information can benefit the table grape industry by reducing losses suffered at the postharvest stage. There are multitudes of factors that can influence table grape postharvest quality leading to huge losses. These losses are exacerbated even further by the long list of postharvest external and internal defects that can occur, including browning in all its various manifestations. The application of cutting-edge technologies such as Fourier Transform Near-Infrared (FT-NIR) spectroscopy that can accurately assess the external and internal quality of fruit is, therefore, essential. This particularly concerns the identification of defects or assessment of the risks of defects that are likely to develop during post storage. The aim of this application would thus be to evaluate these new technologies to monitor table grape quality non-destructively, before, during, and/or after harvest. This study, therefore, focussed on the development and optimisation of faster, cost- effective, and fit-for-purpose methods to monitor harvest maturity and quality of table grapes in the vineyard before harvesting and during packaging and cold storage. Harvest of three different cultivars, namely, Thompson Seedless, Regal Seedless and Prime, happened over two seasons (2016 and 2017) from six different commercial vineyards. Five of these vineyards were in the Western Cape (two in the Hex River Valley, three in Wellington) and one in the Northern Cape (Kakamas), South Africa. Harvest occurred twice at each vineyard, at optimum ripeness and two weeks later (after the optimum harvest date). The incidence and intensity of browning on each berry on a bunch were evaluated for different defects and browning phenotypes. Quantitative harvest maturity and indicative quality parameters such as TSS, TA and pH, as well as the sensory-related parameters – sugar:acid ratio (TSS:TA ratio) and BrimA, were investigated by scanning whole table grape bunches contactless with Bruker’s MATRIX-F spectrometer in the laboratory. Partial Least Squares (PLS) regression was used to build prediction models for each parameter. Two different infrared spectrometers, namely the Bruker Multipurpose Analyser Fourier Transform Near-Infrared (MPA FT-NIR) and MicroNIR Pro 1700 were also used to determine TSS on whole table grape berries. The MicroNIR Pro 1700 was utilised in the vineyard and the laboratory and the MPA only in the laboratory. The same spectral dataset used to build the quantitative models was used to build classification models for two browning phenotypes, namely chocolate browning and friction browning. Partial Least Squares Discriminant Analysis (PLS-DA) and Artificial Neural Networks (ANN) were used for the classification tasks. Key results showed that the incidence and intensity of different defects and browning phenotypes such as sulphur dioxide (SO2) damage were prevalent on all three white seedless table grape cultivars. The incidences of fungal infection, sunburn and abrasion damage were high on Regal Seedless and Thompson Seedless in 2016. Contact browning, mottled browning and friction browning and bruising damage had higher incidences in 2017 than in 2016. Overall, the intensity of defects was very high in 2016 except on Regal Seedless from Hex River Valley. Prime from Kakamas and Wellington had the highest intensity of defects in 2017, which appeared on the grapes after 7 weeks of cold storage. Prediction models were successfully developed for TSS, TA, TSS:TA, pH, and BrimA minus acids on intact table grape bunches using FT-NIR spectroscopy in a contactless measurement mode, and applying spectral pre-processing techniques for regression analysis with PLS. The combination of Savitzky-Golay first derivative coupled with multiplicative scatter correction on the original spectra delivered the best models. Statistical indicators used to evaluate the models were the number of latent variables (LV) used to build the model, the prediction correlation coefficient (R2p) and root mean square error of prediction (RMSE). For the respective parameters TSS, TA, TSS:TA ratio, pH, and BrimA, the number of LV used when the models were build according to a random split of the calibration and validation set were 6, 4, 5, 5 and 10, the R2p = 0.81, 0.43, 0.66, 0.27, and 0.71, and the RMSEP = 1.30 °Brix, 1.09 g/L, 7.08, 0.14, and 1.80. When 2016 was used as the calibration set and 2017 as the validation set in model building the number of LV used were 9, 5, 5, 4 and the R2p = 0.44, 0.06, 0.17, 0.05, and 0.05 and the RMSEP = 3.22 °Brix, 2.41 g/L, 14.53, 0.21, and 8.03 for for the respective parameters. Determining TSS of whole table grape berries in the vineyard before and after harvesting using handheld and benchtop spectrometers on intact table grape berries showed that spectra taken in the laboratory with the MicroNIR were more homogenous than those taken in the vineyard with the same spectrometer, over the two years investigated. The results obtained with the MPA were not as good as those obtained with the MicroNIR in the laboratory were. The model constructed with the combined data of 2016 and 2017 taken in the laboratory with the MicroNIR had the best statistics in terms of R2p (0.74) and RPDp (1.97). The model constructed with the 2017 data obtained in the laboratory with the MicroNIR had the lowest prediction error (RMSEP = 1.13°Brix). Good models were obtained using PLS-DA and ANN to classify bunches as either clear or as having chocolate browning and friction browning based on the spectra obtained from intact table grape bunches with the MATRIX-F spectrometer. The classification error rate (CER), specificity and sensitivity were used to evaluate the models constructed using PLS-DA and the kappa score was used for ANN. The CER for chocolate browning (25%) was better than that of friction browning (46%) after Weeks 3 and 4 in cold storage for both class 0 (absence of browning) and class 1 (presence of browning). Both the specificity and sensitivity of class 0 and class 1 of friction browning were not as good as for chocolate browning. With ANN, the testing kappa score to classify table grape bunches as clear or having chocolate browning or friction browning showed that chocolate browning could be classified with the strong agreement after Weeks 3 and 4 and Weeks 5 and 6 and that friction browning could be classified with moderate agreement after three and four weeks in cold storage. Classification of chocolate browning and friction browning phenotypes was done using PLS-DA and ANN and the result showed that both types of browning can be classified with moderate agreement. The implications of the results of this study for the table grape industry are that the industry can move beyond just assessing methods and techniques in the laboratory towards implementation in the vineyard and the packhouse. Much quicker decisions regarding grape quality and destination of export can now be made using a combination of the MicroNIR handheld and MATRIX-F instruments for onsite quality measurement and the models to predict internal (e.g. TSS) and external (browning) quality attributes.