Browsing Doctoral Degrees (Viticulture and Oenology) by Subject "Alcoholic fermentation"
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- ItemNatural 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.