Data fusion approaches for sensory and multimodal chemistry data applied to storage conditions
Jeanne BRAND, Mpho MAFATA, Astrid BUICA - South African Grape and Wine Research Institute, Department of Viticulture and Oenology, Stellenbosch University, South Africa
Martin KIDD - Centre for Statistical Consultation, Stellenbosch University, South Africa
Andrei MEDVEDOVICI - Faculty of Chemistry, University of Bucharest, Romania
Email contact: jeanne[@]sun.ac.za
AIM: The need to combine multimodal data for complex samples is due to the different information captured in each of the techniques (modes). The aim of the study was to provide a critical evaluation of two approaches to fusing multi-modal chemistry and sensory data, namely, multiblock multiple factor analysis (MFA) and concatenation using principal component analysis (PCA).
METHODS: Wines were submitted to sensory analysis using Pivot©Profile (Thuillier et al. 2015) and chemical analysis in four modes: antioxidant measurements (AM), volatile compounds composition (VCC), ultraviolet-visible light (UV-Vis) spectrophotometry (Mafata et al. 2019), and infra-red (IR) spectroscopy. Correspondence analysis (CA), principal component analysis (PCA), and multiple factor analysis (MFA) were used to model data under the data analysis steps involving data cleaning, visualizing, modelling and evaluation (Pagès 2004). Percentage explained variation (%EV) and regression vector (RV) coefficients were used as comparative evaluation parameters between data models (Abdi 2007).
RESULTS: IR spectral data were used as an example of the assessment of the need for data cleaning/pre-processing. Similarities in MFA and high RV coefficients indicated that the raw (unprocessed data) could be used for the data fusion. High RV coefficients and MFA proximity between the antioxidants and UV-Vis measurements indicated an overlap between the type of information contained in the two. The differences between the information captured in each of the five modes can be seen in the different measurements, from the knowledge of the theory/ ontext behind the technique, and statistically. Statistically, the differences are measured and visualised by a lack of overlap (redundancy) in the MFA and its accompanying cluster analysis.
CONCLUSIONS: The %EV when performing PCA are higher than with MFA, a consequence of fusing big data sets from various modes and not necessarily a direct result of the relationships among the data sets. Therefore, the %EV was ruled out as a reliable measure of the differences in informational value between MFA and PCA fusion strategies. RV coefficients, of which MFA were highest, were the best measurements of the performance of data fusion approaches. MFA demonstrated greater appropriateness as a statistical tool for fusing multi-modal data.
References:
Abdi, H., (2007) RV Coefficient and Congruence Coefficient. Encycl. Meas. andStatistics.
Mafata, M., Brand, J., Panzeri, V., Kidd, M. and Buica, A., (2019) A multivariate approach to evaluating the chemical and sensorial evolution of South African Sauvignon Blanc and Chenin Blanc wines under different bottle storage conditions. Food Res. Int. 125, 108515.
Pagès, J., (2004) Multiple factor analysis: Main features and application to sensory data. Rev. Colomb. Estad. 27, 1–26.
Thuillier, B., Valentin, D., Marchal, R. and Dacremont, C., (2015) Pivot© profile: A new descriptive method based on free description. Food Qual. Prefer. 42, 66–77.
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Volatile and phenolic profiles of wines closed with different stoppers and stored for 30 months
Prudence Fleur TCHOUAKEU BETNGA, Free University of Bozen-Bolzano, Italy
Edoardo LONGO, Free University of Bozen-Bolzano, Italy
Vakare MERKYTE, Free University of Bozen-Bolzano, Italy
Amanda DUPAS DE MATOS, Feast Lab, Massey University, New Zealand
Fabrizio ROSSETTI, Mérieux NutriSciences, Italy
Emanuele BOSELLI, Free University of Bozen-Bolzano, Italy
Email contact: prudencefleur.tchouakeubetnga[@]natec.unibz.it
AIM: The aim of this study was to evaluate the volatile and phenolic profiles of three red and one rosé wines stored in bottles for 30 months.
METHODS: Four wines were provided by a winery located in South Tyrol (Kellerei Bozen, Bolzano, Italy), which included Merlot, Lagrein red, Lagrein rosé and St. Magdalener and were closed with different types of stoppers: a blend of natural cork microgranules and polymers without glue addition (Supercap Nature, Mombaroccio, Italy), a one-piece natural cork, agglomerated natural cork and a technical cork 1+1. Volatile compounds were extracted by head-space solid phase microextraction (HS-SPME) and then analysed by GC-MS, while the phenolic compounds were determined by HPLC-DAD-FLD.
RESULTS: The type of stopper did not show significant differences on the chemical profiles of the wines. Instead, the interaction between the wines and the type of stoppers as well as the type of wines had a significant influence on the volatile and phenolic profiles. Regarding the volatile profile, significant differences were observed for ethyl butanoate and 2-hydroxyethylpropanoate which were present just in St. Magdalener and absent in Lagrein rosé wines, respectively. Also, 2-methylethyl butanoate and 3-methylethyl butanoate were not detected in both Lagrein red and rosé, whereas isopentyl acetate was found in Merlot wines at low concentration. On the other hand, 1-hexanol, ethyl hexanoate, ethyl octanoate and ethyl decanoate were found at high concentration in Lagrein rosé wine compared to the three red wines. Regarding the phenolic profile, results showed a low concentration of p-coumaric acid, protocatechuic acid, caftaric acid, (+)-catechin, (-)-epicatechin, S-glutathionyl caftaric acid (GRP) and syringic acid in Lagrein rosé wine with respect to the red wines. However, the concentration of gallic acid was higher in Merlot wine and differed significantly from the three others with the lowest value in the Lagrein rosé.
CONCLUSION: The chemical profiles of the four wines were significantly influenced by the type of wine due to their grape variety and vinification processes. Conversely, the type of stopper did not show any significant differences in terms of volatile nor phenolic profile, due to the high technical quality of the closures under study.
References:
- Rossetti, F., Jouin, A., Jourdes, M., Teissedre, P. L., Foligni, R., Longo, E., & Boselli, E. (2020). Impact of Different Stoppers on the Composition of Red and Rosé Lagrein, Schiava (Vernatsch) and Merlot Wines Stored in Bottle. Molecules, 25(18), 4276. https://www.mdpi.com/1420-3049/25/18/4276
- Betnga, P. F. T., de Matos, A. D., Longo, E., & Boselli, E. (2020). Impact of closure material on the chemical and sensory profiles of grappa during storage in bottle. LWT, 133, 110014. https://doi.org/10.1016/j.lwt.2020.110014
- Skouroumounis, G. K., Kwiatkowski, M. J., Francis, I. L., Oakey, H., Capone, D. L., Duncan, B., ... & Waters, E. J. (2005). The impact of closure type and storage conditions on the composition, color and flavour properties of a Riesling and a wooded Chardonnay wine during five years' storage. Australian Journal of Grape and Wine Research, 11(3), 369-377. https://doi.org/10.1111/j.1755-0238.2005.tb00036
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Effect of simulated shipping conditions on colour and SO2 evolution in Soave wines
Diletta, INVINCIBILE, Università degli Sudi di Verona
Davide SLAGHENAUFI, Università degli Studi di Verona
Maurizio, UGLIANO, Università degli Studi di Verona
Email contact: diletta.invincibile[@]univr.it
AIM: The shelf life of food is defined as the period in which the product will remain safe, is certain to retain desired sensory, chemical, physical, and microbiological characteristics, and complies with any label declaration of nutritional data.1 Wine exhibits a “random” shelf life, as the chemical changes are as dependent on the initial condition of the product, including packaging, as on the storage and freight conditions of the product. However, storage and transport conditions of wine may lead to a reduction in wine quality because of unintended physical and chemical changes. These modifications are generally referred to as oxidative spoilage, and they result in browning and loss of fresh, fruity, and varietal aroma characters.2 Certain protective agents such as SO2 are also typically lost with the onset of oxidative spoilage. Recent studies have shown that the outcomes of oxidation in terms of degree of oxidative spoilage are strongly wine specific,3 so that certain wines appear to be more resistant against oxidative spoilage. In this study, chemical and electrochemical changes under the effects of shipping conditions were measured in thirteen Soave wines, to evaluate the potential of Cyclic Voltammetry in conjunction with other parameters to provide relevant information on the oxidative behaviour of individual white wines.
METHODS: The wines underwent an ageing protocol simulating a freight of 46 days, during which the wine was subjected to specific temperature cycles. In the storage of wines at the departure port, the temperature fluctuated between 16 and 25 °C, reflecting the diurnal cycle; while, during the journey of 28 days, the temperature reached 30 °C. Finally, storage at the arrival port produced an oscillation between 25 and 35 °C. Electrochemical methods, in particular the cyclic voltammetry using either glassy carbon or carbon paste electrodes, have been applied to the analysis of wine phenolics.4 Voltammograms of each wine were collected and their features were analysed in conjunction with concentration of free and total SO2, chromatic and spectrophotometric parameters.
RESULTS: The ageing protocol adopted led each wine to a different free and total SO2 consumption, also reflected in an electrochemical diversity and a general increase of chromatic parameters. Cyclic voltammograms have shown a diversity of electrochemical properties, concentration and type of oxidizable compounds present.
CONCLUSIONS: The objective of this study was to explore the electrochemical and chemical repercussions of adverse temperature conditions on Soave wines to better understand the changes due to freight and storage. Significant and seemingly correlated information derived from the electrochemical profile and SO2 consumption of each wine. This study could also constitute the beginning of research aimed at obtaining predictive parameters of the wine shelf life.
References:
1. F. Franks, Shelf-life of foods - guidelines for its determination and prediction, Cryo-Letters, 1993, 14, 131-131.
2. M. Ugliano, Oxygen Contribution to Wine Aroma Evolution during Bottle Aging, Journal of Agricultural and Food Chemistry, 2013, 61, 6125-6136.
3. J. C. Danilewicz, Reaction of Oxygen and Sulfite in Wine, American Journal of Enology and Viticulture, 2016, 67, 13-17.
4. P. A. Kilmartin, H. L. Zou and A. L. Waterhouse, A cyclic voltammetry method suitable for characterizing antioxidant properties of wine and wine phenolics, Journal of Agricultural and Food Chemistry, 2001, 49, 1957-1965.
Data fusion approaches for sensory and multimodal chemistry data applied to storage conditions
Volatile and phenolic profiles of wines closed with different stoppers and stored for 30 months
Effect of simulated shipping conditions on colour and SO2 evolution in Soave wines
