Quantitative analysis of bisphenol analogue mixtures by terahertz spectroscopy using machine learning method. (1st August 2021)

• Record Type: Journal Article
• Authors: Sun, Yiwen; Huang, Jialiang; Shan, Lianxin; Fan, Shuting; Zhu, Zexuan; Liu, Xudong
• Is Part Of: Food chemistry. Volume 352(2021)
• Publication Date: 2021-08-01
• Subjects: Terahertz spectroscopy -- Bisphenol -- Quantitative analysis -- Analogue mixtures -- SVR; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2021.129313
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Amount of BpA in its analogues can be determined by THz spectral processing method. SVR is used to extract obscure properties from THz spectra of complex mixtures. SVR in combination with THz-TDS can predict sample concentrations with R 2 = 0.98. Machine learning method is a forecast strategy for THz spectroscopic analysis. Abstract: Quantitative analysis of complex mixtures is a great challenge for spectral analysis. Bisphenol A (BpA) is a chemical predominantly used in manufacturing and is being replaced by other analogs due to its potential toxicity. Reliability methods is hence crucial for identification and quantification of bisphenol mixtures. In this study we present an attractive strategy for composition determination of BpA incorporated in its analogue mixtures. Terahertz spectra of four bisphenol components are analyzed using machine learning method (SVR) to learn the underlying model of the frequency against the target concentration of BpA in mixtures. The learned mode predicts the concentrations of the unknown samples with decision coefficient R 2 = 0.98. Absorption spectra for bisphenols mixtures were successfully reconstructed by a hold-out validation scheme. The results indicate the terahertz spectroscopy in combination with SVR is robust and accurate in mixture quantitative analysis and should play a significant role for industrial applications in the future.