scholarly journals Feasibility of attenuated total reflection-fourier transform infrared (ATR-FTIR) chemical imaging and partial least squares regression (PLSR) to predict protein adhesion on polymeric surfaces

The Analyst ◽  
2019 ◽  
Vol 144 (5) ◽  
pp. 1535-1545 ◽  
Author(s):  
S. Mukherjee ◽  
J. A. Martinez-Gonzalez ◽  
A. A. Gowen
Keyword(s):  
Fourier Transform ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Chemical Imaging ◽  
Total Reflection ◽  
Attenuated Total Reflection ◽  
Least Squares Regression ◽  
Polymeric Surfaces ◽  
Protein Adhesion

PLSR with ATR-FTIR chemical imaging predicts protein adhesion on polymeric surfaces well (R2 = 0.99, RMSECV = 0.16).

scholarly journals Correction: Feasibility of attenuated total reflection-fourier transform infrared (ATR-FTIR) chemical imaging and partial least squares regression (PLSR) to predict protein adhesion on polymeric surfaces

The Analyst ◽  
2020 ◽  
Vol 145 (11) ◽  
pp. 4051-4051
Author(s):  
S. Mukherjee ◽  
J. A. Martinez-Gonzalez ◽  
A. A. Gowen
Keyword(s):  
Fourier Transform ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Chemical Imaging ◽  
Total Reflection ◽  
Attenuated Total Reflection ◽  
Least Squares Regression ◽  
Polymeric Surfaces ◽  
Protein Adhesion

Correction for ‘Feasibility of attenuated total reflection-fourier transform infrared (ATR-FTIR) chemical imaging and partial least squares regression (PLSR) to predict protein adhesion on polymeric surfaces’ by S. Mukherjee et al., Analyst, 2019, 144, 1535–1545. DOI: 10.1039/C8AN01768A

scholarly journals Predictive modelling of the water contact angle of surfaces using attenuated total reflection – Fourier transform infrared (ATR-FTIR) chemical imaging and partial least squares regression (PLSR)

The Analyst ◽  
2018 ◽  
Vol 143 (15) ◽  
pp. 3729-3740 ◽  
Author(s):  
S. Mukherjee ◽  
J. Á. Martínez-González ◽  
D. P. Dowling ◽  
A. A. Gowen
Keyword(s):  
Contact Angle ◽  
Fourier Transform ◽  
Partial Least Squares Regression ◽  
Predictive Modelling ◽  
Chemical Imaging ◽  
Total Reflection ◽  
Attenuated Total Reflection ◽  
Least Squares Regression ◽  
Water Contact ◽  
Simple Method

A simple method to model and predict the surface wettability using a combination of ATR-FTIR imaging and PLSR modelling.

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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