Abstract

In-situ Fourier transform infrared (FT-IR) spectroscopy has been recognized as an important technology for online monitoring of chemical reactions. However, analysis of the real-time IR data for identification and quantification of uncertain reactants or intermediates is often ambiguous and difficult. Here, we propose an analysis algorithm based on reaction kinetic modeling and the chemometric method of partial least squares (PLS) to comprehensively and quantitatively study reaction processes. Concentration profiles and apparent kinetic parameters can be simultaneously calculated from the spectral data, without the demand of complicated analysis on characteristic absorbance peaks or tedious sampling efforts for multivariate modeling. Paal–Knorr reactions and glyoxylic acid synthesis reactions were selected as typical reactions to validate the algorithm. A lack of fit of the Paal–Knorr reaction spectra was less than 2.5% at various conditions, and the absolute errors between the predicted values and HPLC measurement of glyoxylic acid synthesis were less than 6% during the reaction process. Moreover, the reaction kinetic models extracted from FT-IR data were used to simulate reaction processes and optimize the conditions in order to maximize product yields, which proved that this analysis method could be used for process optimization.

© 2022 The Author(s)

Wavelength selection of terahertz time-domain spectroscopy based on a partial least squares model for quantitative analysis

Qingxiao Ma, Chun Li, Biao Wang, Xin Ma, and Ling Jiang
Appl. Opt. 60(19) 5638-5642 (2021)

Error analysis of the spectral shift for partial least squares models in Raman spectroscopy

Haiyi Bian and Jing Gao
Opt. Express 26(7) 8016-8027 (2018)

Partial least squares regression calculation for quantitative analysis of metals submerged in water measured using laser-induced breakdown spectroscopy

Tomoko Takahashi, Blair Thornton, Takumi Sato, Toshihiko Ohki, Koichi Ohki, and Tetsuo Sakka
Appl. Opt. 57(20) 5872-5883 (2018)

Quantitative modeling of the reaction/diffusion kinetics of two-chemistry diffusive photopolymers

Benjamin A. Kowalski, Adam C. Urness, Martha-Elizabeth Baylor, Michael C. Cole, William L. Wilson, and Robert R. McLeod
Opt. Mater. Express 4(8) 1668-1682 (2014)

Pattern recognition of visible and near-infrared spectroscopy from bayberry juice by use of partial least squares and a backpropagation neural network

Haiyan Cen, Yidan Bao, and Yong He
Appl. Opt. 45(29) 7679-7683 (2006)