Abstract
Near infrared spectroscopy (NIRS) is based on molecular overtone and combination vibrations. It is difficult to assign specific features under complicated system. So it is necessary to find the relevance between NIRS and target compound. For this purpose, the chondroitin sulfate (CS) ethanol precipitation process was selected as the research model, and 90 samples of 5 different batches were collected and the content of CS was determined by modified carbazole method. The relevance between NIRS and CS was studied throughout optical pathlength, pretreatment methods and variables selection methods. In conclusion, the first derivative with Savitzky–Golay (SG) smoothing was selected as the best pretreatment, and the best spectral region was selected using interval partial least squares (iPLS) method under 1 mm optical cell. A multivariate calibration model was established using PLS algorithm for determining the content of CS, and the root mean square error of prediction (RMSEP) is 3.934 g⋅L-1. This method will have great potential in process analytical technology in the future.
Highlights
Near infrared spectroscopy (NIRS) is a rapid and nondestructive analytical technology, which is treated as one of the most e±cient process analytical tools to analyze complicated components.[1]
This research studied the relevance of e®ective information between NIRS and chondroitin sulfate (CS) during the process of ethanol precipitation
By comprehensive comparison of the R2c, root mean square error of calibration (RMSEC) and RMSEP, it can be concluded that the model built in 1 mm optical pathlength is better than the one built in 4 mm pathlength
Summary
Near infrared spectroscopy (NIRS) is a rapid and nondestructive analytical technology, which is treated as one of the most e±cient process analytical tools to analyze complicated components.[1]. It is very important to investigate the relevance of e®ective information between NIRS and objective compound. The factors which a®ect the relevance include optical pathlength, pretreatment, variables selection, etc. In order to get a good spectrum, selection of appropriate pathlength is very important especially for analysis of trace component.[4] Pretreatment methods are essentially important. Standard normal variate (SNV) transformation[6] seems to be suitable to remove the multiplicative interferences of scatter and particle size.[5] On the other hand, selection of variables will solve the collinearity between spectral variables and eliminate the information that are useless, as well as decrease the cost of instrument, and improve the interpretability of the results .[7]
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