Muñoz-Castells Raquel, Modesti Margherita, Moreno-García Jaime, Catini Alexandro, Capuano Rosamaria, Di Natale Corrado, Bellincontro Andrea, Moreno Juan
Department of Agricultural Chemistry, Edaphology and Microbiology, Agrifood Campus of International Excellence CeiA3, University of Córdoba, Marie Curie (C3) and Severo Ochoa (C6) Buildings, Ctra. N-IV-A, km 396, 14014 Córdoba, Spain.
Department for Innovation of Biological, Agrofood and Forest Systems (DIBAF), University of Tuscia, 01100 Viterbo, Italy.
Molecules. 2025 Apr 2;30(7):1584. doi: 10.3390/molecules30071584.
Electronic noses (E-noses) have become powerful tools for the rapid and cost-effective differentiation of wines, providing valuable information for the comprehensive evaluation of aroma patterns. However, they need to be trained and validated using classical analytical techniques, such as gas chromatography coupled with mass spectrometry, which accurately identify the volatile compounds in wine. In this study, five low-ethanol wines with distinctive sensory profiles-produced using and non- yeasts and tailored to modern consumer preferences-were analyzed to validate the E-nose. A total of 57 volatile compounds were quantified, 27 of which had an Odor Activity Value (OAV) over 0.2. The content in volatiles, grouped into 11 odorant series according to their odor descriptors, along with the data provided by 12 E-nose sensors, underwent advanced statistical treatments to identify relationships between both data matrices. Partial least squares discriminant analysis (PLS-DA) applied to the data from the 12 E-nose sensors revealed well-defined clustering patterns and produced a model that explained approximately 92% of the observed variability. In addition, a principal component regression (PCR) model was developed to assess the ability of the E-nose to non-destructively predict odorant series in wine. The synergy between the volatile compound profiles and the pattern recognition capability of the E-nose, as captured by PLS-DA, enables a detailed characterization of wine aromas. In addition, predictive models that integrate data from gas chromatography, flame ionization detection, and mass spectrometry (GC-FID/GC-MSD) with the electronic nose demonstrating a promising approach for a rapid and accurate odor series prediction, thereby increasing the efficiency of wine aroma analysis.
电子鼻已成为快速且经济高效地区分葡萄酒的有力工具，为香气模式的全面评估提供有价值的信息。然而，它们需要使用经典分析技术进行训练和验证，如气相色谱-质谱联用技术，该技术能准确识别葡萄酒中的挥发性化合物。在本研究中，对五种具有独特感官特征的低乙醇葡萄酒进行了分析，这些葡萄酒采用不同酵母生产，以满足现代消费者的喜好，旨在验证电子鼻。共定量了57种挥发性化合物，其中27种的气味活性值（OAV）超过0.2。根据气味描述符将挥发性成分分为11个气味系列，连同12个电子鼻传感器提供的数据，进行了先进的统计处理，以确定两个数据矩阵之间的关系。对12个电子鼻传感器的数据应用偏最小二乘判别分析（PLS-DA），揭示了明确的聚类模式，并生成了一个能解释约92%观察到的变异性的模型。此外，还开发了主成分回归（PCR）模型，以评估电子鼻无损预测葡萄酒中气味系列的能力。PLS-DA所捕捉到的挥发性化合物谱与电子鼻模式识别能力之间的协同作用，能够对葡萄酒香气进行详细表征。此外，将气相色谱、火焰离子化检测和质谱（GC-FID/GC-MSD）数据与电子鼻相结合的预测模型，为快速准确的气味系列预测提供了一种有前景的方法，从而提高了葡萄酒香气分析的效率。
