Literature DB >> 30517192

Correction: Predictive modeling for odor character of a chemical using machine learning combined with natural language processing.

Yuji Nozaki, Takamichi Nakamoto.   

Abstract

[This corrects the article DOI: 10.1371/journal.pone.0198475.].

Entities:  

Year:  2018        PMID: 30517192      PMCID: PMC6281253          DOI: 10.1371/journal.pone.0208962

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


There are errors in S2 Table. The values in the “Applicable descriptor Number (from S1 Table)” column are incorrect and should be 1 value lower. Additionally, the sensory dataset which was used in the computer simulation in S2 Table, is not perfectly equivalent to the original data source, Sigma-Aldrich’s “Flavors and Fragrances” [2]. Therefore, S2 Table data are different from the original source. Some of the descriptors may be ignored for samples described by more than 6 descriptors. As descriptors are listed ascending in alphabet, ignored descriptors are mainly: “sweet”, “vanilla” and “wine-like”. Those descriptors are used when the number of descriptors is not more than six. Approximately 11% of samples in the dataset affected. Please see the corrected S2 Table caption and file below.

Odor character profile of chemicals.

S2 data set is different from original source. (CSV) Click here for additional data file.
  1 in total

1.  Predictive modeling for odor character of a chemical using machine learning combined with natural language processing.

Authors:  Yuji Nozaki; Takamichi Nakamoto
Journal:  PLoS One       Date:  2018-06-14       Impact factor: 3.240

  1 in total
  1 in total

1.  Exploration of sensing data to realize intended odor impression using mass spectrum of odor mixture.

Authors:  Daisuke Hasebe; Manuel Alexandre; Takamichi Nakamoto
Journal:  PLoS One       Date:  2022-08-17       Impact factor: 3.752

  1 in total

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