Rapid monitoring five components of ethanol precipitation process of Shenzhiling oral solution using near infrared spectroscopy

2016 ◽  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Oral Solution ◽  
Precipitation Process ◽  
Ethanol Precipitation ◽  
Rapid Monitoring

scholarly journals The relevance study of effective information between near infrared spectroscopy and chondroitin sulfate in ethanol precipitation process

2014 ◽  
Vol 07 (06) ◽  
pp. 1450022 ◽  
Author(s):  
Lian Li ◽  
Baoyang Ding ◽  
Qi Yang ◽  
Shang Chen ◽  
Huaying Ren ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Chondroitin Sulfate ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Process Analytical Technology ◽  
Precipitation Process ◽  
Calibration Model ◽  
Optical Cell ◽  
Ethanol Precipitation

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.

Rapid monitoring of grape withering using visible near-infrared spectroscopy

2015 ◽  
Vol 95 (15) ◽  
pp. 3144-3149 ◽  
Author(s):  
Roberto Beghi ◽  
Valentina Giovenzana ◽  
Simone Marai ◽  
Riccardo Guidetti
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Rapid Monitoring

scholarly journals pH value monitoring during human albumin purification with near infrared spectroscopy and chemometrics

2018 ◽  
Vol 11 (03) ◽  
pp. 1850009 ◽  
Author(s):  
Qiaofeng Sun ◽  
Zhongyu Sun ◽  
Fei Wang ◽  
Lian Li ◽  
Ronghua Liu ◽  
...  
Keyword(s):  
Quality Control ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Ph Value ◽  
Acid Precipitation ◽  
Human Albumin ◽  
Precipitation Process ◽  
Least Squares Regression

Human albumin (HA) is a very important blood product which requires strict quality control strategy. Acid precipitation is a key step which has a great effect on the quality of final product. Therefore, a new method based on quality by design (QbD) was proposed to investigate the feasibility of realizing online quality control with the help of near infrared spectroscopy (NIRS) and chemometrics. The pH value is the critical process parameter (CPP) in acid precipitation process, which is used as the end-point indicator. Six batches, a total of 74 samples of acid precipitation process, were simulated in our lab. Four batches were selected randomly as calibration set and remaining two batches as validation set. Then, the analysis based on material information and three different variable selection methods, including interval partial least squares regression (iPLS), competitive adaptive reweighted sampling (CARS) and correlation coefficient (CC) were compared for eliminating irrelevant variables. Finally, iPLS was used for variables selection. The quantitative model was built up by partial least squares regression (PLSR). The values of determination coefficients ([Formula: see text] and [Formula: see text]), root mean squares error of prediction (RMSEP), root mean squares error of calibration (RMSEC) and root mean squared error of cross validation (RMSECV) were 0.969, 0.953, 0.0496, 0.0695 and 0.0826, respectively. The paired [Formula: see text] test and repeatability test showed that the model had good prediction ability and stability. The results indicated that PLSR model could give accurate measurement of the pH value.

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