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Recent Advances in Chemometrics, QSAR/QSPR, and Analytical Chemistry Applied to Flavor Compounds and Odorants Study

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Analytical Chemistry".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 1369

Special Issue Editor


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Guest Editor
CSGA (Centre des Sciences du Goût et de l’Alimentation), CNRS, INRAE, Institut Agro, Université de Bourgogne-Franche Comté, F-21000 Dijon, France
Interests: odorants; stucture-activity relationships; QSAR; pharmacophore

Special Issue Information

Dear Colleagues,

The understanding of the perception of odors, fragrances, and flavors is a challenging issue that requires both reliable experimental data acquisition from multiple sources and the gathering, analyzing, and harnessing of these.

In the odorants and flavor compounds area, the experimental data can be obtained through the use of several tools, such as chromatographic and spectroscopic methods, and sensory evaluations. These data relate to a number of physicochemical properties, such as the retention–release equilibrium and the kinetics release of compounds from liquid or solid media, and also involves olfactory perception aspects, including olfactory thresholds, odor quality, and intensity measurements. In so far as such experimental properties are quantifiable or encodable, their study can be performed using chemometric methods.

Briefly, chemometrics can be defined as an explorative data analysis discipline that includes experimental design, statistical and multivariate analyses, artificial intelligence (neural networks, learning, and genetic algorithms), and can be additionally applied to the quantitative structure–activity/property relationships studies (QSAR/QSPR) using computational chemistry methods. Because the gathering of a vast amount of data involves the buildup of large databases, data curation, and data mining are complementary to chemometric approaches.

The current Special Issue aims to gather articles related to the application, development, and improvement of chemometric and analytical methods, embracing (Q)SAR/(Q)SPR and computational chemistry approaches. Original research papers as well review articles involving the application of above methods to the study of odors, odorants, fragrances and flavors are welcome.

Dr. Anne Tromelin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • flavor compounds
  • odor
  • odorants
  • olfactory threshold
  • databases
  • data mining
  • QSAR
  • QSPR
  • statistical analysis
  • multivariate analysis
  • classification
  • clustering
  • learning algorithms

Published Papers (1 paper)

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Research

15 pages, 3070 KiB  
Article
Deep-Learning-Based Mixture Identification for Nuclear Magnetic Resonance Spectroscopy Applied to Plant Flavors
by Yufei Wang, Weiwei Wei, Wen Du, Jiaxiao Cai, Yuxuan Liao, Hongmei Lu, Bo Kong and Zhimin Zhang
Molecules 2023, 28(21), 7380; https://doi.org/10.3390/molecules28217380 - 1 Nov 2023
Viewed by 1158
Abstract
Nuclear magnetic resonance (NMR) is a crucial technique for analyzing mixtures consisting of small molecules, providing non-destructive, fast, reproducible, and unbiased benefits. However, it is challenging to perform mixture identification because of the offset of chemical shifts and peak overlaps that often exist [...] Read more.
Nuclear magnetic resonance (NMR) is a crucial technique for analyzing mixtures consisting of small molecules, providing non-destructive, fast, reproducible, and unbiased benefits. However, it is challenging to perform mixture identification because of the offset of chemical shifts and peak overlaps that often exist in mixtures such as plant flavors. Here, we propose a deep-learning-based mixture identification method (DeepMID) that can be used to identify plant flavors (mixtures) in a formulated flavor (mixture consisting of several plant flavors) without the need to know the specific components in the plant flavors. A pseudo-Siamese convolutional neural network (pSCNN) and a spatial pyramid pooling (SPP) layer were used to solve the problems due to their high accuracy and robustness. The DeepMID model is trained, validated, and tested on an augmented data set containing 50,000 pairs of formulated and plant flavors. We demonstrate that DeepMID can achieve excellent prediction results in the augmented test set: ACC = 99.58%, TPR = 99.48%, FPR = 0.32%; and two experimentally obtained data sets: one shows ACC = 97.60%, TPR = 92.81%, FPR = 0.78% and the other shows ACC = 92.31%, TPR = 80.00%, FPR = 0.00%. In conclusion, DeepMID is a reliable method for identifying plant flavors in formulated flavors based on NMR spectroscopy, which can assist researchers in accelerating the design of flavor formulations. Full article
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