Mid-infrared Attenuated Total Reflectance (MIR-ATR) Predictive Models for Asphaltene Contents in Vacuum Residua: Asphaltene Structure–Functionality Correlations Based on Partial Least-Squares Regression (PLS-R)

2011 ◽  
Vol 25 (8) ◽  
pp. 3678-3686 ◽  
Author(s):  
Jorge A. Orrego-Ruiz ◽  
Alexander Guzmán ◽  
Daniel Molina ◽  
Enrique Mejía-Ospino
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Predictive Models ◽  
Partial Least Squares Regression ◽  
Attenuated Total Reflectance ◽  
Least Squares Regression ◽  
Mid Infrared ◽  
Total Reflectance ◽  
Asphaltene Structure

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.

scholarly journals Rapid determination of fecal fat by Fourier transform infrared analysis (FTIR) with partial least-squares regression and an attenuated total reflectance accessory

1996 ◽  
Vol 42 (12) ◽  
pp. 2015-2020 ◽  
Author(s):  
P Franck ◽  
J L Sallerin ◽  
H Schroeder ◽  
M A Gelot ◽  
P Nabet
Keyword(s):  
Fourier Transform ◽  
Least Squares ◽  
Partial Least Squares ◽  
Rapid Determination ◽  
Fourier Transform Infrared ◽  
Attenuated Total Reflectance ◽  
Infrared Spectrometry ◽  
Infrared Analysis ◽  
Least Squares Regression ◽  
Total Reflectance

Abstract Fecal lipid content is usually determined by titrimetric or gravimetric methods, but these methods are time consuming and involve dangerous solvents. We have developed a new method of measuring fecal lipids by Fourier transform infrared spectrometry (FTIR) with an attenuated total reflectance accessory that is fast and requires no solvents. The spectra of stools from 4000 to 750 cm-1 were analyzed, and the lipid concentrations were measured by using a calibration curve prepared by partial least-squares analysis of data from 34 stools. The linearity of the method was tested by mixing low-lipid stools with lipid-overloaded stools to give a range of 0.5-15% lipid. The prediction residual values were -0.49-0.78% for calibrators, and -2.55-2.34% for unknown samples. There was good agreement between the fecal lipids measured by gravimetric (x) and FTIR(y) methods: y = 0.87x + 5.5. The standard error of prediction was 1.07%.

Partial Least-Squares Quantitative Analysis of Infrared Spectroscopic Data. Part II: Application to Detergent Analysis

1988 ◽  
Vol 42 (2) ◽  
pp. 228-236 ◽  
Author(s):  
M. P. Fuller ◽  
G. L. Ritter ◽  
C. S. Draper
Keyword(s):  
Quantitative Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Spectroscopic Data ◽  
Statistical Approach ◽  
Attenuated Total Reflectance ◽  
Polypropylene Glycol ◽  
Mid Infrared ◽  
Total Reflectance ◽  
Commercial Detergent

An implementation of the PLS (partial least-squares) statistical approach to quantitative analysis was applied to a set of mid-infrared spectra obtained from a series of commercial detergent samples. The components analyzed included the base detergent, sodium benzoate, 2-propanol, 1,2-propanediol, polypropylene glycol, and glycerol. The samples were analyzed with the use of a horizontal attenuated total reflectance (ATR) accessory equipped with a zinc selenide crystal. The PLS model created in the calibration was found to provide excellent results for a set of validation samples.

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