Near infrared spectroscopy combined with multivariate analysis for monitoring the ethanol precipitation process of fraction I+II+III supernatant in human albumin separation

2017 ◽  
Vol 175 ◽  
pp. 17-23 ◽  
Author(s):  
Can Li ◽  
Fei Wang ◽  
Lixuan Zang ◽  
Hengchang Zang ◽  
Manel Alcalà ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Human Albumin ◽  
Precipitation Process ◽  
Ethanol Precipitation ◽  
Fraction I

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.

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.

Occurrence of wooden breast and white striping in Brazilian slaughtering plants and use of near‐infrared spectroscopy and multivariate analysis to identify affected chicken breasts

2020 ◽  
Vol 85 (10) ◽  
pp. 3102-3112
Author(s):  
Leila Moreira Carvalho ◽  
Marta Suely Madruga ◽  
Mario Estévez ◽  
Amanda Teixeira Badaró ◽  
Douglas Fernandes Barbin
Keyword(s):  
Multivariate Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
White Striping ◽  
Wooden Breast

scholarly journals Rancidity Estimation of Perilla Seed Oil by Using Near-Infrared Spectroscopy and Multivariate Analysis Techniques

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Suk-Ju Hong ◽  
Shin-Joung Rho ◽  
Ah-Yeong Lee ◽  
Heesoo Park ◽  
Jinshi Cui ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Seed Oil ◽  
Prediction Models ◽  
Oxidation Process ◽  
Analysis Techniques ◽  
Peroxide Values ◽  
Perilla Seed

Near-infrared spectroscopy and multivariate analysis techniques were employed to nondestructively evaluate the rancidity of perilla seed oil by developing prediction models for the acid and peroxide values. The acid, peroxide value, and transmittance spectra of perilla seed oil stored in two different environments for 96 and 144 h were obtained and used to develop prediction models for different storage conditions and time periods. Preprocessing methods were applied to the transmittance spectra of perilla seed oil, and multivariate analysis techniques, such as principal component regression (PCR), partial least squares regression (PLSR), and artificial neural network (ANN) modeling, were employed to develop the models. Titration analysis shows that the free fatty acids in an oil oxidation process were more affected by relative humidity than temperature, whereas peroxides in an oil oxidation process were more significantly affected by temperature than relative humidity for the two different environments in this study. Also, the prediction results of ANN models for both acid and peroxide values were the highest among the developed models. These results suggest that the proposed near-infrared spectroscopy technique with multivariate analysis can be used for the nondestructive evaluation of the rancidity of perilla seed oil, especially the acid and peroxide values.

Sign in / Sign up

Export Citation Format

Share Document