Characterizing Semi-Volatile Compounds in Italian Ciders Using GC×GC–TOF-MS and Multivariate Analysis
A recent study of 56 Italian ciders utilizing comprehensive two-dimensional gas chromatography coupled to time-of-flight-mass spectrometry (GC×GC–TOF-MS) detected the presence of approximately 130 semi-volatile organic compounds.
In a recent study published in Heliyon (1), 56 samples of differently produced commercial Italian apple and pear ciders were analyzed for semi-volatile organic compounds (SVOCs) profiling, using comprehensive two-dimensional gas chromatography coupled to time-of-flight-mass spectrometry (GC×GC–TOF-MS). The authors state that this is the first survey regarding the profiling of SVOCs performed on Italian commercial craft cider, and the results are aimed to be a milestone for its characterization and to start and promote cider culture in Italy.
Cider, one of the oldest known beverages, is a traditional low-alcoholic drink realized by partial or complete fermentation of juice, with or without the addition of sugar, water, or flavoring (2,3). The rising knowledge on biochemical phenomena happening along the cider production process pushed the scientific community to deepen the influence of identified organic molecules on the cider sensory profile (4). As all the steps in the cider production process, from orchard varieties to bottles stored in shelves, are involved in volatile compounds production and transformation, the researchers believe it important to perform SVOCs profiling, due to their massive ability to affect the quality and the aroma of the finished product.
Out of a pool of 155 SVOCs that could be categorized into multiple chemical families, researchers found approximately 130 SVOCs in the ciders analyzed. Their primary findings originate from the cross-examination of the measurements of cider SVOCs followed by their association with perceived odorants. Chemometric methods like principal component analysis (PCA) were then used to emphasize the presence of odd samples found to be anomalous during the tasting session. PCA objects distribution is consistent with the findings of the sensory panel’s findings and showed what the researchers deemed “an impressive correlation between sensed odorants and detected variables.” Furthermore, t-distributed stochastic neighbor embedding (t-SNE), a statistical method for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map (5), proved to be more effective in highlighting similarities across different classes of cider (in this case, pear versus apple).
The researchers state that the pairing of two powerful approaches such as bidimensional GC and chemometrics showed to be the best option for exploring and deepening new topics in the food science area. Also, they said that this research delivers a promising tool for the promotion of scientific culture on the production of cider. The team plans on conducting further research, expanding their scope to consider new vintages and a wider variety of ciders, as they believe that their results only present the fundamentals to understand the typicality of Italian ciders, with the evolution of their work being essential in the verification of the correlation between cider molecular odorants and perceived aromatic notes.
Hard apple cider - © Brent Hofacker - stock.adobe.com
References
1. Orecchio, C.; Bedini, A.; Romagnoli, M.; Pantò, S.; Alladio, E.; Pazzi, M. Characterization of Semi-Volatile Compounds in 56 Italian Ciders using GC×GC-TOF-MS and Multivariate Analysis. Heliyon 2024, 10 (15), E35687. DOI: 10.1016/j.heliyon.2024.e35687
2. McKay, M.; Buglass, A. J.; Gook Lee, C.; Cider and Perry, in: Handbook of Alcoholic Beverages: Technical, Analytical and Nutritional Aspects. John Wiley and Sons, 2010,231-265. DOI:10.1002/9780470976524.ch11
3. Joshi, V. K.; Attri, B. L.; Panesar, P. S.; Abrol, G. S.; Sharma, S.; Thakur, A. D.; Selli, S.; Kelebek, H.; Reddy, L.V.; Specific Features of Table Wine Production Technology, in: Science and Technology of Fruit Wine Production. Elsevier Inc., 2017, 295-461. DOI: 10.1016/B978-0-12-800850-8.00007-7
4. Buglass, A.J.; Handbook of Alcoholic Beverages,Wiley, 2016.
5. t-distributed stochastic neighbor embedding. Wikipedia (accessed 2024-08-22).
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