Aznar Margarita, López Ricardo, Cacho Juan, Ferreira Vicente
Department of Analytical Chemistry, Faculty of Sciences, University of Zaragoza, 50009 Zaragoza, Spain.
J Agric Food Chem. 2003 Apr 23;51(9):2700-7. doi: 10.1021/jf026115z.
Partial least squares regression (PLSR) models able to predict some of the wine aroma nuances from its chemical composition have been developed. The aromatic sensory characteristics of 57 Spanish aged red wines were determined by 51 experts from the wine industry. The individual descriptions given by the experts were recorded, and the frequency with which a sensory term was used to define a given wine was taken as a measurement of its intensity. The aromatic chemical composition of the wines was determined by already published gas chromatography (GC)-flame ionization detector and GC-mass spectrometry methods. In the whole, 69 odorants were analyzed. Both matrixes, the sensory and chemical data, were simplified by grouping and rearranging correlated sensory terms or chemical compounds and by the exclusion of secondary aroma terms or of weak aroma chemicals. Finally, models were developed for 18 sensory terms and 27 chemicals or groups of chemicals. Satisfactory models, explaining more than 45% of the original variance, could be found for nine of the most important sensory terms (wood-vanillin-cinnamon, animal-leather-phenolic, toasted-coffee, old wood-reduction, vegetal-pepper, raisin-flowery, sweet-candy-cacao, fruity, and berry fruit). For this set of terms, the correlation coefficients between the measured and predicted Y (determined by cross-validation) ranged from 0.62 to 0.81. Models confirmed the existence of complex multivariate relationships between chemicals and odors. In general, pleasant descriptors were positively correlated to chemicals with pleasant aroma, such as vanillin, beta damascenone, or (E)-beta-methyl-gamma-octalactone, and negatively correlated to compounds showing less favorable odor properties, such as 4-ethyl and vinyl phenols, 3-(methylthio)-1-propanol, or phenylacetaldehyde.
已开发出能够根据葡萄酒化学成分预测其部分香气细微差别的偏最小二乘回归（PLSR）模型。51位葡萄酒行业专家测定了57种西班牙陈年红葡萄酒的芳香感官特征。记录专家给出的个体描述，并将用于定义某一特定葡萄酒的感官术语出现频率作为其强度的度量。葡萄酒的芳香化学成分通过已发表的气相色谱（GC）-火焰离子化检测器和GC-质谱法测定。总共分析了69种气味物质。通过对相关感官术语或化合物进行分组和重新排列，并排除次要香气术语或微弱香气化学成分，简化了感官和化学数据这两个矩阵。最后，针对18个感官术语以及27种化学物质或化学物质组建立了模型。对于九个最重要的感官术语（木材 - 香草醛 - 肉桂、动物 - 皮革 - 酚类、烤面包 - 咖啡、陈木 - 还原味、植物 - 胡椒、葡萄干 - 花香、甜 - 糖果 - 可可、果味和浆果味），可以找到解释超过45%原始方差的满意模型。对于这组术语，测量值与预测值Y（通过交叉验证确定）之间的相关系数范围为0.62至0.81。模型证实了化学物质与气味之间存在复杂的多变量关系。一般来说，令人愉悦的描述词与具有宜人香气的化学物质呈正相关，如香草醛、β - 大马酮或（E）-β - - 甲基 - γ - 辛内酯，与具有较不良气味特性的化合物呈负相关，如4 - 乙基苯酚和乙烯基苯酚、3 - （甲硫基）-1 - 丙醇或苯乙醛。
