scholarly journals Partial least squares−near infrared spectrometric determination of ethanol in distilled alcoholic beverages

2017 ◽  
Vol 31 (2) ◽  
pp. 201 ◽  
Author(s):  
A. Debebe ◽  
S. Temesgen ◽  
M. R. Abshiro ◽  
B. S. Chandravanshi
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Alcoholic Beverages

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.

Reflectance Near-Infrared Spectroscopic Method with a Chemometric Technique Using Partial Least Squares Multivariate Calibration for Simultaneous Determination of Chondroitin, Glucosamine, and Ascorbic Acid

2012 ◽  
Vol 95 (3) ◽  
pp. 724-732 ◽  
Author(s):  
Alaa El-Gindy ◽  
Khalid Abdel-Salam Attia ◽  
Mohammad Wafaa Nassar ◽  
Nasr M A El-Abasawy ◽  
Maisra Al-Shabrawi Shoeib
Keyword(s):  
Ascorbic Acid ◽  
Least Squares ◽  
Simultaneous Determination ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Spectroscopic Method ◽  
Pharmaceutical Products ◽  
Calibration Model ◽  
Drug Concentrations

Abstract A reflectance near-infrared (RNIR) spectroscopy method was developed for simultaneous determination of chondroitin (CH), glucosamine (GO), and ascorbic acid (AS) in capsule powder. A simple preparation of the sample was done by grinding, sieving, and compression of the powder sample for improving RNIR spectra. Partial least squares (PLS-1 and PLS-2) was successfully applied to quantify the three components in the studied mixture using information included in RNIR spectra in the 4240–9780 cm–1 range. The calibration model was developed with the three drug concentrations ranging from 50 to 150% of the labeled amount. The calibration models using pure standards were evaluated by internal validation, cross-validation, and external validation using synthetic and pharmaceutical preparations. The proposed method was applied for analysis of two pharmaceutical products. Both pharmaceutical products had the same active principle and similar excipients, but with different nominal concentration values. The results of the proposed method were compared with the results of a pharmacopoeial method for the same pharmaceutical products. No significant differences between the results were found. The standard error of prediction was 0.004 for CH, 0.003 for GO, and 0.005 for AS. The correlation coefficient was 0.9998 for CH, 0.9999 for GO, and 0.9997 for AS. The highly accurate and precise RNIR method can be used for QC of pharmaceutical products.

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