scholarly journals The Impact of Preprocessing Methods for a Successful Prostate Cell Lines Discrimination Using Partial Least Squares Regression and Discriminant Analysis Based on Fourier Transform Infrared Imaging

Cells ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 953
Author(s):  
Danuta Liberda ◽  
Ewa Pięta ◽  
Katarzyna Pogoda ◽  
Natalia Piergies ◽  
Maciej Roman ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Data Structure ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Cell Lines ◽  
Partial Least Squares Regression ◽  
Baseline Correction ◽  
Least Squares Regression ◽  
Ft Ir

Fourier transform infrared spectroscopy (FT-IR) is widely used in the analysis of the chemical composition of biological materials and has the potential to reveal new aspects of the molecular basis of diseases, including different types of cancer. The potential of FT-IR in cancer research lies in its capability of monitoring the biochemical status of cells, which undergo malignant transformation and further examination of spectral features that differentiate normal and cancerous ones using proper mathematical approaches. Such examination can be performed with the use of chemometric tools, such as partial least squares discriminant analysis (PLS-DA) classification and partial least squares regression (PLSR), and proper application of preprocessing methods and their correct sequence is crucial for success. Here, we performed a comparison of several state-of-the-art methods commonly used in infrared biospectroscopy (denoising, baseline correction, and normalization) with the addition of methods not previously used in infrared biospectroscopy classification problems: Mie extinction extended multiplicative signal correction, Eiler’s smoothing, and probabilistic quotient normalization. We compared all of these approaches and their effect on the data structure, classification, and regression capability on experimental FT-IR spectra collected from five different prostate normal and cancerous cell lines. Additionally, we tested the influence of added spectral noise. Overall, we concluded that in the case of the data analyzed here, the biggest impact on data structure and performance of PLS-DA and PLSR was caused by the baseline correction; therefore, much attention should be given, especially to this step of data preprocessing.

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.

scholarly journals Can We Trust Score Plots?

Metabolites ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 278 ◽  
Author(s):  
Marta Bevilacqua ◽  
Rasmus Bro
Keyword(s):  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Classification Models ◽  
Least Squares Regression ◽  
Calibration Models ◽  
Accepted Practice ◽  
Component Models

In this paper, we discuss the validity of using score plots of component models such as partial least squares regression, especially when these models are used for building classification models, and models derived from partial least squares regression for discriminant analysis (PLS-DA). Using examples and simulations, it is shown that the currently accepted practice of showing score plots from calibration models may give misleading interpretations. It is suggested and shown that the problem can be solved by replacing the currently used calibrated score plots with cross-validated score plots.

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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