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Updated: Jan 20, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
FTIR spectroscopy coupled with machine learning approaches as a rapid tool for identification and quantification of
Yu-Tang Wang1, Bin Li2, Xiao-Juan Xu2
1Key Laboratory of Dairy Science, Ministry of Education, Northeast Agricultural University, Harbin 150030, PR China; Department of Food Science, Northeast Agricultural University, Harbin 150030, PR China.
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.
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