FTIR spectroscopy coupled with machine learning approaches as a rapid tool for identification and quantification of artificial sweeteners.

Clicks: 231
ID: 24511
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #225 of 341 articles by views in Food chemistry

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 341 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Fourier transform infrared (FTIR) spectroscopy calibrations were developed to simultaneously determine the multianalytes of five artificial sweeteners, including sodium cyclamate, sucralose, sodium saccharin, acesulfame-K and aspartame. By combining the pretreatment of the spectrum and principal component analysis, 131 feature wavenumbers were extracted from the full spectral range for modelling to qualitative and quantitative analysis. Compared to random forest, k nearest neighbour and linear discriminant analysis, support vector machine model had better predictivity, indicating the most effective identification performance. Furthermore, multivariate calibration models based on partial least squares regression were constructed for quantifying any combinations of the five artificial sweeteners, and validated by prediction data sets. As shown by the good agreement between the proposed method and the reference HPLC for the determination of the sweeteners in beverage samples, a promising and rapid tool based on FTIR spectroscopy, coupled with chemometrics, has been performed to identify and objectively quantify artificial sweeteners.
Reference Key
wang2019ftirfood Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wang, Yu-Tang;Li, Bin;Xu, Xiao-Juan;Ren, Hai-Bin;Yin, Jia-Yi;Zhu, Hao;Zhang, Ying-Hua;
Journal Food chemistry
Year 2019
DOI
S0308-8146(19)31518-3
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.