Introduction

Smells can be described by assigning the words that come to mind when sniffing an odorous material. A great number of terms can be applied, but not all of them are independent, and it is possible to establish groups of words often applied together when describing a smell. Such classification of olfactory descriptors is of scientific interest in order to better understand the dimensionality and structure of human olfactory perception space. For this purpose, compilations of olfactory profiles contain valuable information that may lead to certain consensus in odor classification.

Methods

One of the most comprehensive odor databases is the Dravnieks’ Atlas, which contains quantitative olfactory profiles for 160 samples. For each one, a large panel rated the applicability of 146 odor character descriptors on a numeric scale.

Results

By applying principal component analysis to this Atlas, 105 descriptors were reorganized in 24 classes, and 33 attributes were considered as odors intermediate of two or three categories. The similarities between classes were studied by means of a further multivariate analysis based on latent variables, which provides valuable information about the most salient dimensions of odor space.

Conclusions

Consistent with other reported statistical analyses of olfactory databases, the perceptual space of odor character is multidimensional with about 20–30 dimensions, and it is better described as a continuum spectrum rather than as a segmented space.

Implications

Attempts to classify all possible odor descriptors in a restricted number of classes appear to be inappropriate. Instead, 24 categories of related terms are proposed here, regarding the rest as intermediate smells, assuming that olfactory classes are not independent and follow certain hierarchy according to particular underlying dimensions.

中文翻译：

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146个气味特征描述符的多元分析与分类

介绍

可以通过分配嗅闻气味材料时想到的词来描述气味。可以应用大量术语，但并非所有术语都是独立的，并且可以建立描述气味时经常一起应用的词组。为了更好地理解人类嗅觉感知空间的维度和结构，嗅觉描述符的这种分类具有科学意义。为此，嗅觉概况的汇编包含可能导致气味分类中的某些共识的有价值的信息。

方法

最全面的气味数据库之一是 Dravnieks 的 Atlas，其中包含 160 个样本的定量嗅觉特征。对于每一个，一个大型小组在数字尺度上对 146 个气味特征描述符的适用性进行评级。

结果

通过对该图谱应用主成分分析，将105个描述符重组为24个类别，将33个属性视为二三类气味的中间体。通过基于潜在变量的进一步多变量分析研究了类别之间的相似性，这提供了有关气味空间最显着维度的宝贵信息。

结论

与其他报告的嗅觉数据库统计分析一致，气味特征的感知空间是多维的，大约有 20-30 维，最好将其描述为连续谱而不是分段空间。

影响

试图将所有可能的气味描述符归入有限数量的类别似乎是不合适的。相反，这里提出了 24 类相关术语，将其余的视为中间气味，假设嗅觉类不是独立的，并且根据特定的底层维度遵循一定的层次结构。