Zarzo Manuel, Stanton David T
Corporate Research, Modeling and Simulations Department, Procter & Gamble Co., Miami Valley Innovation Center, 11810 East Miami River Road, Cincinnati, OH 45252, USA.
Chem Senses. 2006 Oct;31(8):713-24. doi: 10.1093/chemse/bjl013. Epub 2006 Jul 19.
Many classifications of odors have been proposed, but none of them have yet gained wide acceptance. Odor sensation is usually described by means of odor character descriptors. If these semantic profiles are obtained for a large diversity of compounds, the resulting database can be considered representative of odor perception space. Few of these comprehensive databases are publicly available, being a valuable source of information for fragrance research. Their statistical analysis has revealed that the underlying structure of odor space is high dimensional and not governed by a few primary odors. In a new effort to study the underlying sensory dimensions of the multivariate olfactory perception space, we have applied principal component analysis to a database of 881 perfume materials with semantic profiles comprising 82 odor descriptors. The relationships identified between the descriptors are consistent with those reported in similar studies and have allowed their classification into 17 odor classes.
人们已经提出了许多气味分类方法，但尚未有任何一种获得广泛认可。气味感觉通常通过气味特征描述符来描述。如果针对大量不同的化合物获得这些语义概况，那么由此产生的数据库可被视为代表气味感知空间。这些综合数据库中很少有公开可用的，它们是香料研究的宝贵信息来源。对它们的统计分析表明，气味空间的潜在结构是高维的，并非由少数几种基本气味所主导。为了研究多元嗅觉感知空间的潜在感官维度，我们进行了一项新的尝试，将主成分分析应用于一个包含881种香料材料的数据库，这些材料的语义概况包含82个气味描述符。所确定的描述符之间的关系与类似研究中报告的关系一致，并使它们能够被分类为17个气味类别。
