In silico prediction of fragrance retention grades for monomer flavors using QSPR models-Reference-Cited by-同舟云学术

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In silico prediction of fragrance retention grades for monomer flavors using QSPR models

Published:2021-11 Issue: Volume:218 Page:104424
ISSN:0169-7439
Container-title:Chemometrics and Intelligent Laboratory Systems
language:en
Short-container-title:Chemometrics and Intelligent Laboratory Systems

Author:

Liu Qi,Luo Dehan,Wen Tengteng^ORCID,GholamHosseini Hamid,Li Jingshan

Funder

National Natural Science Foundation of China

Guangdong Department of Science and Technology

Science and Technology Planning Project of Guangdong Province

Publisher

Elsevier BV

Subject

Spectroscopy,Process Chemistry and Technology,Computer Science Applications,Software,Analytical Chemistry

Reference40 articles.

1. Effect of fabric parameters on fragrance retention;Du;Industria Textila,2020

2. Zwitterionic comb-like lipid polymers encapsulating linalool for increasing the fragrance retention time;Zhang;Chin. Chem. Lett.,2021

3. Perfumes, Cosmetics and Soaps: Modern Cosmetics;Poucher,2013

4. Qspr analysis for the retention index of flavors and fragrances on a ov-101 column;Rojas;Chemometr. Intell. Lab. Syst.,2015

5. Deeprei: deep learning-based gas chromatographic retention index predictor;Vrzal;Anal. Chim. Acta,2021

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1. Intelligent Consensus Predictions of the Retention Index of Flavor and Fragrance Compounds Using 2D Descriptors;Chromatographia;2024-07-18

2. Computational Methods as Part of Scientific Research in Cosmetic Sciences—Are We Using the Opportunity?;Cosmetics;2024-05-14

3. MDs-NP: a property prediction model construction procedure for naphtha based on molecular dynamics simulation;Journal of Physics: Condensed Matter;2024-05-10

4. A novel hybrid binary whale optimization algorithm with chameleon hunting mechanism for wrapper feature selection in QSAR classification model:A drug-induced liver injury case study;Expert Systems with Applications;2023-12

5. Recent advances and application of machine learning in food flavor prediction and regulation;Trends in Food Science & Technology;2023-08

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