2020年5月1日
Sensory Evaluation of Odor Approximation Using Nmf with KullbackLeibler Divergence and ItakuraSaito Divergence in Mass Spectrum Space
ECS Meeting Abstracts
 ,
 巻
 MA202001
 号
 26
 開始ページ
 1843
 終了ページ
 1843
 記述言語
 掲載種別
 研究論文（学術雑誌）
 DOI
 10.1149/ma202001261843mtgabs
 出版者・発行元
 The Electrochemical Society
Introduction
The odor reproduction is achieved by approximating mass spectra of various odors by blending a set of odor components. These reproduced odors should be as close to the target odor as possible. The fidelity of odor reproduction should be as in the same manner as reproducing colors. Since no primary odors have been found, thus finding an appropriate set of odor components to perform odor reproduction is essential. The odor components should be kept as small as possible whereas it should cover a wide range of odors. In the present study, we performed a sensory evaluation of odors approximated with KullbackLeibler and ItakuraSaito divergences on mass spectrum space.
Method
In our previous study, the nonnegative matrix factorization (NMF) method was used to extract basis vector corresponding to odor components [1], [2]. NMF was performed to analyze the mass spectrum data space of essential oils as shown in Figure 1. Nonnegativity property of NMF is useful in exploring odor components. We created a mass spectrum database of 185 essential oils originating from plants. Mass spectrum data were gathered at mass to charge (m/z) region from 50 to 250. Region below 50 was discarded because we diluted the samples with ethanol (molecular weight of 46). An example of essential oils' mass spectra that we gathered is shown in Figure 2.
NMF with two different cost functions were performed in odor component exploration. KullbackLeibler divergence (NMFKL) and ItakuraSaito divergence (NMFIS) were performed for the odor component exploration. IS divergence can extract small peaks in the high m/z region, with much contribution to human perception [3]. Those small peaks are ignored or degraded when using KL divergences [4]. Thus NMFIS can enhance the approximation accuracy of the small peaks in high m/z area which might have a contribution to human olfaction.
Nonnegative least squares methods were performed to approximate the recipe for odor approximation. Nonnegative least squares methods were applied twice. First one was used to obtain the recipe to approximate odor components explored by NMF and the second one to obtain the recipe to approximate the target odor based on odor components. Nonnegative least squares with IS divergence based on optimization algorithm was applied to the result of NMFIS, whereas normal nonnegative least squares were applied to the result of NMFKL.
Results and Discussions
The previous study revealed that 30 odor components worked well in approximating odor. We evaluated NMFIS and NMFKL only using 10 odor components. Although 10 odor components were insufficient for obtaining good approximation accuracy, the difference between the two methods became large. We extracted odor components from essential oils based on the recipe approximated by first nonnegative least squares method.
We approximated 6 target odors (clove bud, ylangylang, orange, origanum, mint) by using both NMFIS and NMFKL methods. Duo trio sensory test was performed to evaluate the approximated odors. A comparison of the original target odor and approximated odor mass spectrum are shown in Figure 3. The peaks locations were actually reproduced in spite of certain errors in magnitude even if we used only 10 odor components. A subject was asked to pick up either of the two approximated odor (one based on NMFIS and another one based on NMFKL) closer to the target one. We evaluated the Z score to the results of sensory tests (Table 1, P<0.05). The Z score of “+2” means the approximated odor by NMFIS is significantly closer to the target odor whereas the Z score of “2” means the approximated odor by NMFKL is significantly closer to the target odor. The Z score of between +2 and 2 means none of approximated odor from both NMFs is closer to the target odor [5]. Three of the approximated odors (orange, mint, and ylangylang) by NMFIS were closer to the target odor than that by NMFKL. One approximated odor (cypress) by NMFKL was closer to the target odor than that by NMFIS. There was no significant closeness for the rest of two odors (clove bud and origanum). Overall, the sensory test reveals that approximated odors by NMFIS were closer to the target odor than the odor approximated by NMFKL. The Approximation quality based upon IS divergence was better than that based upon KL divergence.
References:
[1] T. Nakamoto, M. Ohno, and Y. Nihei, Odor Approximation Using Mass Spectrometry, IEEE Sens. J. 12 (2012), 3225–3231; doi: 10.1109/JSEN.2012.2190506
[2] D. D. Lee and H. S. Seung, Learning the parts of objects by nonnegative matrix factorization, Nature. 401 (1999), 788–791 ; doi: 10.1038/44565
[3] D. Prasetyawan and T. Nakamoto, Comparison of NMF with KullbackLeibler Divergence and ItakuraSaito Divergence for Odor Approximation, Proc. of the ISOEN. 18 (2019); doi: 10.1109/ISOEN.2019.8823186
[4] C. Févotte, N. Bertin, and J.L. Durrieu, Nonnegative Matrix Factorization with the ItakuraSaito Divergence: With Application to Music Analysis, Neural Comput. 21 (2009), 793–830; doi: 10.1162/neco.2008.0408771
[5] Gail Vance Civille, B. Thomas Carr, Sensory Evaluation Techniques, CRC Press. (2015); ISBN: 9781482216905
Figure 1
<p></p>
The odor reproduction is achieved by approximating mass spectra of various odors by blending a set of odor components. These reproduced odors should be as close to the target odor as possible. The fidelity of odor reproduction should be as in the same manner as reproducing colors. Since no primary odors have been found, thus finding an appropriate set of odor components to perform odor reproduction is essential. The odor components should be kept as small as possible whereas it should cover a wide range of odors. In the present study, we performed a sensory evaluation of odors approximated with KullbackLeibler and ItakuraSaito divergences on mass spectrum space.
Method
In our previous study, the nonnegative matrix factorization (NMF) method was used to extract basis vector corresponding to odor components [1], [2]. NMF was performed to analyze the mass spectrum data space of essential oils as shown in Figure 1. Nonnegativity property of NMF is useful in exploring odor components. We created a mass spectrum database of 185 essential oils originating from plants. Mass spectrum data were gathered at mass to charge (m/z) region from 50 to 250. Region below 50 was discarded because we diluted the samples with ethanol (molecular weight of 46). An example of essential oils' mass spectra that we gathered is shown in Figure 2.
NMF with two different cost functions were performed in odor component exploration. KullbackLeibler divergence (NMFKL) and ItakuraSaito divergence (NMFIS) were performed for the odor component exploration. IS divergence can extract small peaks in the high m/z region, with much contribution to human perception [3]. Those small peaks are ignored or degraded when using KL divergences [4]. Thus NMFIS can enhance the approximation accuracy of the small peaks in high m/z area which might have a contribution to human olfaction.
Nonnegative least squares methods were performed to approximate the recipe for odor approximation. Nonnegative least squares methods were applied twice. First one was used to obtain the recipe to approximate odor components explored by NMF and the second one to obtain the recipe to approximate the target odor based on odor components. Nonnegative least squares with IS divergence based on optimization algorithm was applied to the result of NMFIS, whereas normal nonnegative least squares were applied to the result of NMFKL.
Results and Discussions
The previous study revealed that 30 odor components worked well in approximating odor. We evaluated NMFIS and NMFKL only using 10 odor components. Although 10 odor components were insufficient for obtaining good approximation accuracy, the difference between the two methods became large. We extracted odor components from essential oils based on the recipe approximated by first nonnegative least squares method.
We approximated 6 target odors (clove bud, ylangylang, orange, origanum, mint) by using both NMFIS and NMFKL methods. Duo trio sensory test was performed to evaluate the approximated odors. A comparison of the original target odor and approximated odor mass spectrum are shown in Figure 3. The peaks locations were actually reproduced in spite of certain errors in magnitude even if we used only 10 odor components. A subject was asked to pick up either of the two approximated odor (one based on NMFIS and another one based on NMFKL) closer to the target one. We evaluated the Z score to the results of sensory tests (Table 1, P<0.05). The Z score of “+2” means the approximated odor by NMFIS is significantly closer to the target odor whereas the Z score of “2” means the approximated odor by NMFKL is significantly closer to the target odor. The Z score of between +2 and 2 means none of approximated odor from both NMFs is closer to the target odor [5]. Three of the approximated odors (orange, mint, and ylangylang) by NMFIS were closer to the target odor than that by NMFKL. One approximated odor (cypress) by NMFKL was closer to the target odor than that by NMFIS. There was no significant closeness for the rest of two odors (clove bud and origanum). Overall, the sensory test reveals that approximated odors by NMFIS were closer to the target odor than the odor approximated by NMFKL. The Approximation quality based upon IS divergence was better than that based upon KL divergence.
References:
[1] T. Nakamoto, M. Ohno, and Y. Nihei, Odor Approximation Using Mass Spectrometry, IEEE Sens. J. 12 (2012), 3225–3231; doi: 10.1109/JSEN.2012.2190506
[2] D. D. Lee and H. S. Seung, Learning the parts of objects by nonnegative matrix factorization, Nature. 401 (1999), 788–791 ; doi: 10.1038/44565
[3] D. Prasetyawan and T. Nakamoto, Comparison of NMF with KullbackLeibler Divergence and ItakuraSaito Divergence for Odor Approximation, Proc. of the ISOEN. 18 (2019); doi: 10.1109/ISOEN.2019.8823186
[4] C. Févotte, N. Bertin, and J.L. Durrieu, Nonnegative Matrix Factorization with the ItakuraSaito Divergence: With Application to Music Analysis, Neural Comput. 21 (2009), 793–830; doi: 10.1162/neco.2008.0408771
[5] Gail Vance Civille, B. Thomas Carr, Sensory Evaluation Techniques, CRC Press. (2015); ISBN: 9781482216905
Figure 1
<p></p>
 リンク情報
 ID情報

 DOI : 10.1149/ma202001261843mtgabs
 eISSN : 21512043