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%.

scholarly journals Partial Least Squares (PLS) Integrated Fourier Transform Infrared (FTIR) Approach for Prediction of Moisture in Transformer Oil and Lubricating Oil

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Siong Fong Sim ◽  
Amelia Laccy Jeffrey Kimura
Keyword(s):  
Fourier Transform ◽  
Least Squares ◽  
Partial Least Squares ◽  
Fourier Transform Infrared ◽  
Ftir Spectra ◽  
Transformer Oil ◽  
Lubricating Oil ◽  
Least Squares Regression ◽  
Promising Alternative ◽  
Karl Fischer

Fourier transform infrared (FTIR) spectroscopy has been advocating a promising alternative for Karl Fischer titration method for quantification of moisture in oil. This study aims to integrate partial least squares regression (PLSR) approach on FTIR spectra for prediction of moisture in locally accessible transformer oil and lubricating oil. The oil samples spiked with known moisture concentrations were extracted with acetonitrile and subjected to analysis with an FTIR spectrophotometer. The PLSR model was built based on 100 training/test splits, and the prediction performance was measured with the percentage root mean squares error (% RMSE). The range of concentration studied was between 0 and 5000 ppm. The marker region of moisture was found at 3750–3400 and 1700–1600 cm−1 with the latter demonstrating a better predictive ability in both lubricating oil and transformer oil. The prediction of moisture in lubricating oil was characterized with lower % RMSE. At concentration less than 700 ppm, the prediction accuracy deteriorates suggesting poor sensitivity. The PLSR was implemented on IR spectra of a set of blind samples, verified with Karl Fischer (for transformer oil) method and Kittiwake (for lubricating oil) method. The prediction was encouraging at concentrations above 1000 ppm; at lower concentrations, the prediction was characterized with high percent error. The algorithm, validated with 100 training/test splits, was converted into an executable program for prediction of moisture based on FTIR spectra. This program can be used for prediction of other substances given that the marker region is identified. FTIR can be used for prediction of moisture in oil nevertheless the sensitivity and precision is low for samples with low moisture concentration.

scholarly journals Rapid Quantitative Analysis of Forest Biomass Using Fourier Transform Infrared Spectroscopy and Partial Least Squares Regression

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Gifty E. Acquah ◽  
Brian K. Via ◽  
Oladiran O. Fasina ◽  
Lori G. Eckhardt
Keyword(s):  
Fourier Transform ◽  
Chemical Composition ◽  
Ftir Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Fourier Transform Infrared ◽  
Least Squares Regression ◽  
Forest Logging ◽  
Logging Residue

Fourier transform infrared reflectance (FTIR) spectroscopy has been used to predict properties of forest logging residue, a very heterogeneous feedstock material. Properties studied included the chemical composition, thermal reactivity, and energy content. The ability to rapidly determine these properties is vital in the optimization of conversion technologies for the successful commercialization of biobased products. Partial least squares regression of first derivative treated FTIR spectra had good correlations with the conventionally measured properties. For the chemical composition, constructed models generally did a better job of predicting the extractives and lignin content than the carbohydrates. In predicting the thermochemical properties, models for volatile matter and fixed carbon performed very well (i.e.,R2> 0.80, RPD > 2.0). The effect of reducing the wavenumber range to the fingerprint region for PLS modeling and the relationship between the chemical composition and higher heating value of logging residue were also explored. This study is new and different in that it is the first to use FTIR spectroscopy to quantitatively analyze forest logging residue, an abundant resource that can be used as a feedstock in the emerging low carbon economy. Furthermore, it provides a complete and systematic characterization of this heterogeneous raw material.

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