Javascript must be enabled to continue!
Sensory Evaluation of Odor Approximation Using NMF with Kullback-Leibler Divergence and Itakura-Saito Divergence in Mass Spectrum Space
View through CrossRef
The odor reproduction can be achieved by approximating mass spectra of different odors by blending a set of odor components. The method enables us to create various odors by adjusting the blending recipe. The reproduced odor should be as close as possible to the target odor. Since there are no primary odors that have been found so far, finding an appropriate set of odor components to perform odor reproduction is essential. The number of odor components should be kept as small as possible whereas it should cover the widest range of odors. In the present study, we performed a sensory evaluation of odor reproduction. Odor reproduction and approximation by utilizing Nonnegative Matrix Factorization (NMF) particularly with Kullback-Leibler (KL) and Itakura-Saito (IS) divergences on mass spectrum space were evaluated. The sensory test reveals that the accuracy of odor approximation by IS divergence were higher than that of KL divergence. Moreover, the combination of NMF with IS divergence with that of KL divergence improved the accuracy.
The Electrochemical Society
Title: Sensory Evaluation of Odor Approximation Using NMF with Kullback-Leibler Divergence and Itakura-Saito Divergence in Mass Spectrum Space
Description:
The odor reproduction can be achieved by approximating mass spectra of different odors by blending a set of odor components.
The method enables us to create various odors by adjusting the blending recipe.
The reproduced odor should be as close as possible to the target odor.
Since there are no primary odors that have been found so far, finding an appropriate set of odor components to perform odor reproduction is essential.
The number of odor components should be kept as small as possible whereas it should cover the widest range of odors.
In the present study, we performed a sensory evaluation of odor reproduction.
Odor reproduction and approximation by utilizing Nonnegative Matrix Factorization (NMF) particularly with Kullback-Leibler (KL) and Itakura-Saito (IS) divergences on mass spectrum space were evaluated.
The sensory test reveals that the accuracy of odor approximation by IS divergence were higher than that of KL divergence.
Moreover, the combination of NMF with IS divergence with that of KL divergence improved the accuracy.
Related Results
Robust denoising FCM clustering via L2,1 NMF and local constraint
Robust denoising FCM clustering via L2,1 NMF and local constraint
The Fuzzy C-Means (FCM) algorithm is widely used in data mining and
machine learning. However, the sensitivity of FCM to the initial value
and noise inevitably leads to the decline...
The Black Mass as Play: Dennis Wheatley's The Devil Rides Out
The Black Mass as Play: Dennis Wheatley's The Devil Rides Out
Literature—at least serious literature—is something that we work at. This is especially true within the academy. Literature departments are places where workers labour over texts c...
Swarm Intelligence for Non-Negative Matrix Factorization
Swarm Intelligence for Non-Negative Matrix Factorization
The Non-negative Matrix Factorization (NMF) is a special low-rank approximation which allows for an additive parts-based and interpretable representation of the data. This article ...
Swarm Intelligence for Non-Negative Matrix Factorization
Swarm Intelligence for Non-Negative Matrix Factorization
The Non-negative Matrix Factorization (NMF) is a special low-rank approximation which allows for an additive parts-based and interpretable representation of the data. This article ...
Odor Classification of Human Body by Neural Networks
Odor Classification of Human Body by Neural Networks
This paper considers classification of human body odor based on the neural network of learning vector quantization (LVQ). Odors of human body are sweaty odor, middle-aged odor, and...
Statistical Divergences between Densities of Truncated Exponential Families with Nested Supports: Duo Bregman and Duo Jensen Divergences
Statistical Divergences between Densities of Truncated Exponential Families with Nested Supports: Duo Bregman and Duo Jensen Divergences
By calculating the Kullback–Leibler divergence between two probability measures belonging to different exponential families dominated by the same measure, we obtain a formula that ...
Breast Carcinoma within Fibroadenoma: A Systematic Review
Breast Carcinoma within Fibroadenoma: A Systematic Review
Abstract
Introduction
Fibroadenoma is the most common benign breast lesion; however, it carries a potential risk of malignant transformation. This systematic review provides an ove...
Utilizing Amari-Alpha Divergence to Stabilize the Training of Generative Adversarial Networks
Utilizing Amari-Alpha Divergence to Stabilize the Training of Generative Adversarial Networks
Generative Adversarial Nets (GANs) are one of the most popular architectures for image generation, which has achieved significant progress in generating high-resolution, diverse im...

