Multivariate calibration of an x-ray diffractometer by partial least squares regression

1987 ◽  
Vol 2 (1-3) ◽  
pp. 209-219 ◽  
Author(s):  
Terje V. Karstang ◽  
Richard J. Eastgate
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Multivariate Calibration ◽  
Least Squares Regression ◽  
X Ray

scholarly journals Rapid tannin profiling of tree fodders using untargeted mid-infrared spectroscopy and partial least squares regression

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Jordi Ortuño ◽  
Sokratis Stergiadis ◽  
Anastasios Koidis ◽  
Jo Smith ◽  
Chris Humphrey ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Multivariate Calibration ◽  
Plant Ecology ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy

Abstract Background The presence of condensed tannins (CT) in tree fodders entails a series of productive, health and ecological benefits for ruminant nutrition. Current wet analytical methods employed for full CT characterisation are time and resource-consuming, thus limiting its applicability for silvopastoral systems. The development of quick, safe and robust analytical techniques to monitor CT’s full profile is crucial to suitably understand CT variability and biological activity, which would help to develop efficient evidence-based decision-making to maximise CT-derived benefits. The present study investigates the suitability of Fourier-transformed mid-infrared spectroscopy (MIR: 4000–550 cm−1) combined with multivariate analysis to determine CT concentration and structure (mean degree of polymerization—mDP, procyanidins:prodelphidins ratio—PC:PD and cis:trans ratio) in oak, field maple and goat willow foliage, using HCl:Butanol:Acetone:Iron (HBAI) and thiolysis-HPLC as reference methods. Results The MIR spectra obtained were explored firstly using Principal Component Analysis, whereas multivariate calibration models were developed based on partial least-squares regression. MIR showed an excellent prediction capacity for the determination of PC:PD [coefficient of determination for prediction (R2P) = 0.96; ratio of prediction to deviation (RPD) = 5.26, range error ratio (RER) = 14.1] and cis:trans ratio (R2P = 0.95; RPD = 4.24; RER = 13.3); modest for CT quantification (HBAI: R2P = 0.92; RPD = 3.71; RER = 13.1; Thiolysis: R2P = 0.88; RPD = 2.80; RER = 11.5); and weak for mDP (R2P = 0.66; RPD = 1.86; RER = 7.16). Conclusions MIR combined with chemometrics allowed to characterize the full CT profile of tree foliage rapidly, which would help to assess better plant ecology variability and to improve the nutritional management of ruminant livestock.

Calibration approaches for the measurement of aerosol multielemental concentration using spark emission spectroscopy

2018 ◽  
Vol 33 (3) ◽  
pp. 404-412 ◽  
Author(s):  
Lina Zheng ◽  
Pramod Kulkarni ◽  
Dionysios D. Dionysiou
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Emission Spectroscopy ◽  
Partial Least Squares Regression ◽  
Elemental Concentration ◽  
Multivariate Calibration ◽  
Least Squares Regression ◽  
Calibration Approach

A multivariate calibration approach, using partial least squares regression, has been developed for the measurement of aerosol elemental concentration.

Improving precision of X-ray fluorescence analysis of lanthanide mixtures using partial least squares regression

2015 ◽  
Vol 113 ◽  
pp. 126-131 ◽  
Author(s):  
Dmitry Kirsanov ◽  
Vitaly Panchuk ◽  
Alexander Goydenko ◽  
Maria Khaydukova ◽  
Valentin Semenov ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Fluorescence Analysis ◽  
Least Squares Regression ◽  
X Ray
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