Predicting odor perceptual similarity from odor structure.
Predicting odor perceptual similarity from odor structure.
复制标题
DOI：
10.1371/journal.pcbi.1003184
复制
发表时间：
2013
影响因子：
4.3
通讯作者：
Sobel N
中科院分区：
文献类型：
--
作者：
Snitz K;Yablonka A;Weiss T;Frumin I;Khan RM;Sobel N
为了理解嗅觉的大脑机制，我们必须理解控制气味结构和气味感知之间联系的规则。天然气味实际上是由许多分子组成的混合物，目前还没有方法来观察这种气味混合物的分子结构并预测它们的气味。在三个独立的实验中，我们要求139名受试者对64种气味混合物的成对感知相似性进行评级，这些气味混合物的大小从4到43个单分子组分不等。然后，我们测试了替代模型，将气味混合物结构与气味混合物感知相似性联系起来。然而，单独考虑混合物的每个单分子组分的模型提供了对混合物相似性的较差预测，将混合物表示为单个结构向量的模型提供了预测和实际感知相似性之间的一致相关性（r≥0.49，p<0.001）。该模型的优化版本在预测和实际混合物相似性之间产生r=0.85（p<0.001）的相关性。 换句话说，我们开发了一种算法，可以查看两种新型气味混合物的分子结构，并预测它们随后的感知相似性。这一目标是通过使用将混合物视为单个向量的模型来实现的，这与嗅觉中的合成而不是分析性大脑处理机制是一致的。一百年前，亚历山大格雷厄姆贝尔问：“你能测量一种气味和另一种气味之间的区别吗？很明显，我们有很多不同种类的气味，从紫罗兰和玫瑰的气味一直到阿魏的气味。但是，除非你能测量它们的相似性和差异性，否则你就不可能有气味科学。”在这里，我们开发了一个计算框架和算法，着眼于两种气味的分子结构，并预测其随后的感知相似性。重要的是，该算法适用于由数十种不同分子组成的混合物组成的气味，就像自然气味一样。最有效的算法是将气味混合物视为单个值，而不是反映其每个成分的一堆值。这与哺乳动物大脑如何处理气味的观点一致：合成单一的气味感受器，而不是从气味混合物中分析提取单个气味特征。总之，我们的算法的性能可能有助于气味科学的实践，我们的算法的性质可能有助于理解大脑的嗅觉机制。
To understand the brain mechanisms of olfaction we must understand the rules that govern the link between odorant structure and odorant perception. Natural odors are in fact mixtures made of many molecules, and there is currently no method to look at the molecular structure of such odorant-mixtures and predict their smell. In three separate experiments, we asked 139 subjects to rate the pairwise perceptual similarity of 64 odorant-mixtures ranging in size from 4 to 43 mono-molecular components. We then tested alternative models to link odorant-mixture structure to odorant-mixture perceptual similarity. Whereas a model that considered each mono-molecular component of a mixture separately provided a poor prediction of mixture similarity, a model that represented the mixture as a single structural vector provided consistent correlations between predicted and actual perceptual similarity (r≥0.49, p<0.001). An optimized version of this model yielded a correlation of r = 0.85 (p<0.001) between predicted and actual mixture similarity. In other words, we developed an algorithm that can look at the molecular structure of two novel odorant-mixtures, and predict their ensuing perceptual similarity. That this goal was attained using a model that considers the mixtures as a single vector is consistent with a synthetic rather than analytical brain processing mechanism in olfaction. One hundred years ago, Alexander Graham Bell asked: “Can you measure the difference between one kind of smell and another? It is very obvious that we have very many different kinds of smells, all the way from the odor of violets and roses up to asafetida. But until you can measure their likenesses and differences you can have no science of odor.” Here we developed a computational framework and algorithm that looks at the molecular structure of two odors and predicts their ensuing perceptual similarity. Importantly, the algorithm works for odors that are each composed of a mixture containing tens of different molecules, much like natural smells. The algorithm that worked best was one that treats the odor-mixture as a single value, rather than a bunch of values reflecting each of its components. This is consistent with the growing view of how the mammalian brain treats odors: synthesizing a singular odor percept rather than analytically extracting individual odorant features from the odor-mixture. In conclusion, our algorithm's performance may contribute to the practice of the science of odor, and our algorithm's nature may contribute to the understanding of brain mechanisms of smell.
登录
查看更多内容
影响因子：
25
作者：
Barnes, Dylan C.;Hofacer, Rylon D.;Zaman, Ashiq R.;Rennaker, Robert L.;Wilson, Donald A.
通讯作者：
Wilson, Donald A.
影响因子：
64.8
作者：
Keller, Andreas;Zhuang, Hanyi;Matsunami, Hiroaki
通讯作者：
Matsunami, Hiroaki
影响因子：
48
作者：
Haddad, Rafi;Khan, Rehan;Sobel, Noam
通讯作者：
Sobel, Noam
影响因子：
34.7
作者：
Gottfried, Jay A.
通讯作者：
Gottfried, Jay A.
影响因子：
6.1
作者：
Aznar, M;López, R;Ferreira, V
通讯作者：
Ferreira, V