A rapid analytical method for the quantitative determination of the sugar in acarbose fermentation by infrared spectroscopy and chemometrics.

Spectrochim Acta A Mol Biomol Spectrosc

Guangdong Provincial Engineering Research Center of Online Monitoring of Water Pollution, Guangdong Provincial Key Laboratory of Emergency Test for Dangerous Chemicals, Guangdong Institute of Analysis (China National Analytical Center Guangzhou), Guangdong Academy of Sciences, Guangzhou 510070, China. Electronic address:

Published: October 2020

In this article, a rapid analytical method for the quantitative determination of the glucose and maltose in the industrial acarbose fermentation was established by the combination application of infrared spectroscopy and chemometrics. The spectra of the 398 acarbose samples were collected by a portable infrared fast analyzer and the concentration of the glucose and maltose in the acarbose fermentation solution were determinate by high performance liquid chromatography (HPLC) as the referent database. Four spectral pretreatment methods, first derivative (FD), second derivative (SD), Savitzky-Golay (SG) convolution smoothing and mean center (MC) were employed to eliminate the optical interference from background and other noise information. The best result was obtained with FD+SG(21, 3)+MC method. The effects of different principal component numbers (PCs) on the parameters were also optimized. Two models of PLS and MLR, were used to predict the concentration of the glucose and maltose. The FD+SG(21, 3)+MC method was chosen as best method, with 12 PCs for glucose and 11 for maltose as optimized parameters. The PLS model was significantly better than the MLR model. Furthermore, both the predicted values and the reference values of glucose and maltose models showed superior linear relationship within the calibration range. The absolute errors of the predicted values and their corresponding reference values of glucose and maltose in the PLS model were within ±0.14 and ±0.35 confidence intervals, respectively. The prediction correct rate was 98.3%, which indicated that the prediction results of model were excellent.

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http://dx.doi.org/10.1016/j.saa.2020.118571DOI Listing

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