scholarly journals An Approach to Rapid Determination of Tween-80 for the Quality Control of Traditional Chinese Medicine Injection by Partial Least Squares Regression in Near-Infrared Spectral Modeling

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Jin-Fang Ma ◽  
Tian-Ling Chen ◽  
Xiang-Dong Zhang ◽  
Xue Xiao ◽  
Fa-Huan Ge
Keyword(s):  
Quantitative Analysis ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Rapid Determination ◽  
Tween 80 ◽  
Least Squares Regression

This study established an approach to rapidly determine the Tween-80 in traditional Chinese medicine (TCM) injection by using near-infrared (NIR) spectroscopy. Totally 133 standard solutions of Tween-80 were prepared and randomly divided into calibration set and validation set, containing 109 and 24 samples, respectively. Spectral data were preprocessed and then subjected to establish a predictive model using partial least-squares (PLS). The standard error of cross validation (SECV), standard deviation of calibration (SEC), and the determination coefficient (R) of the established model were 0.0561, 0.0526, and 0.9986, respectively. The model was successfully applied to determine Tween-80 contents in 25 XBJ Injections and 40 FFSX Injections, and it produced satisfactory quantitative analysis results with average relative deviations 0.60% and 0.16%, respectively, for 25 XBJ Injections and 40 FFSX Injections, and the maximum relative deviations 8.57% and 7.60%, respectively. This work shows that NIR model displayed quite good predictive ability for Tween-80 quantitative analysis, which could potentially be applied to rapid determination of Tween-80 in the production process of TCM injections and other TCM products.

Determination of carbon fraction and nitrogen concentration in tree foliage by near infrared reflectances: a comparison of statistical methods

1996 ◽  
Vol 26 (4) ◽  
pp. 590-600 ◽  
Author(s):  
Katherine L. Bolster ◽  
Mary E. Martin ◽  
John D. Aber
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Data Sets ◽  
Independent Data ◽  
Least Squares Regression ◽  
Near Infrared Reflectance ◽  
Regression Methods

Further evaluation of near infrared reflectance spectroscopy as a method for the determination of nitrogen, lignin, and cellulose concentrations in dry, ground, temperate forest woody foliage is presented. A comparison is made between two regression methods, stepwise multiple linear regression and partial least squares regression. The partial least squares method showed consistently lower standard error of calibration and higher R2 values with first and second difference equations. The first difference partial least squares regression equation resulted in standard errors of calibration of 0.106%, with an R2 of 0.97 for nitrogen, 1.613% with an R2 of 0.88 for lignin, and 2.103% with an R2 of 0.89 for cellulose. The four most highly correlated wavelengths in the near infrared region, and the chemical bonds represented, are shown for each constituent and both regression methods. Generalizability of both methods for prediction of protein, lignin, and cellulose concentrations on independent data sets is discussed. Prediction accuracy for independent data sets and species from other sites was increased using partial least squares regression, but was poor for sample sets containing tissue types or laboratory-measured concentration ranges beyond those of the calibration set.

Rapid Determination of Ethanol in Sugarcane Spirit Using Partial Least Squares Regression Embedded in Smartphone

2018 ◽  
Vol 11 (7) ◽  
pp. 1951-1957 ◽  
Author(s):  
Fernanda C. Böck ◽  
Gilson A. Helfer ◽  
Adilson B. da Costa ◽  
Morgana B. Dessuy ◽  
Marco F. Ferrão
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Rapid Determination ◽  
Least Squares Regression
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