Tufariello Maria, Pati Sandra, Palombi Lorenzo, Grieco Francesco, Losito Ilario
CNR-Institute of Sciences of Food Production (ISPA), Via Prov. le, Lecce-Monteroni, 73100 Lecce, Italy.
Department of Agriculture, Food, Natural Resources and Engineering (DAFNE), University of Foggia, Via Napoli 25, 71100 Foggia, Italy.
Foods. 2022 Mar 22;11(7):910. doi: 10.3390/foods11070910.
This review takes a snapshot of the main multivariate statistical techniques and methods used to process data on the concentrations of wine volatile molecules extracted by means of solid phase micro-extraction and analyzed using GC-MS. Hypothesis test, exploratory analysis, regression models, and unsupervised and supervised pattern recognition methods are illustrated and discussed. Several applications in the wine volatolomic sector are described to highlight different interactions among the various matrix components and volatiles. In addition, the use of Artificial Intelligence-based methods is discussed as an innovative class of methods for validating wine varietal authenticity and geographical traceability.
本综述简要介绍了用于处理通过固相微萃取提取并使用气相色谱-质谱联用仪分析的葡萄酒挥发性分子浓度数据的主要多元统计技术和方法。阐述并讨论了假设检验、探索性分析、回归模型以及无监督和有监督模式识别方法。描述了葡萄酒挥发物组学领域的几个应用，以突出各种基质成分和挥发物之间的不同相互作用。此外，还讨论了基于人工智能的方法作为验证葡萄酒品种真实性和地理可追溯性的创新方法的应用。
