Baseline correction combined partial least squares algorithm and its application in on-line Fourier transform infrared quantitative analysis

2011 ◽  
Vol 690 (2) ◽  
pp. 162-168 ◽  
Author(s):  
Jiangtao Peng ◽  
Silong Peng ◽  
Qiong Xie ◽  
Jiping Wei
Keyword(s):  
Fourier Transform ◽  
Quantitative Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Fourier Transform Infrared ◽  
Baseline Correction ◽  
On Line ◽  
Least Squares Algorithm

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.

A Novel Calibration Method for the Quantification of Respirable Particles in Mining Scenarios Using Fourier Transform Infrared Spectroscopy

2020 ◽  
pp. 000370282096806
Author(s):  
Robert Stach ◽  
Teresa Barone ◽  
Emanuele Cauda ◽  
Boris Mizaikoff
Keyword(s):  
Particle Size ◽  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Sample Preparation ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Fourier Transform Infrared ◽  
Respirable Dust ◽  
Least Squares Regression

The exposure of mining workers to crystalline particles, e.g., alpha quartz in respirable dust, is a ubiquitous global problem in occupational safety and health at surface and underground operations. The challenge of rapid in-field monitoring for direct assessment and adoption of intervention has not been solved satisfactorily to date, as conventional analytical methods such as X-ray diffraction and infrared spectroscopy require laboratory environments, complex system handling, tedious sample preparation, and are limited by, e.g., addressable particle size. A novel monitoring approach was developed for potential in-field application enabling the quantification of crystalline particles in the respirable regime based on transmission infrared spectroscopy. This on-site approach analyzes samples of dust in ambient air collected onto PVC filters using respirable dust sampling devices. In the present study, we demonstrate that portable Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate data analysis provides a versatile tool for the identification and quantification of minerals in complex real-world matrices. Without further sample preparation, the loaded filters are immediately analyzed via transmission infrared spectroscopy, and the mineral amount is quantified in real-time using a partial least squares regression algorithm. Due to the inherent molecular selectivity for crystalline as well as organic matrix components, infrared spectroscopy uniquely allows to precisely determine the particle composition even in complex samples such as dust from coal mines or clay-rich environments. For establishing a robust partial least squares regression model, a method was developed for generating calibration samples representative in size and composition for respirable mine dust via aerodynamic size separation. Combined with experimental design strategies, this allows tailoring the calibration set to the demands of air quality management in underground mining scenarios, i.e., the respirable particle size regime and the matrix of the target analyte.

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