Use of partial least squares regression for the multivariate calibration of hazardous air pollutants in open-path FT-IR spectrometry

1998 ◽  
Author(s):  
Brian K. Hart ◽  
Peter R. Griffiths
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Air Pollutants ◽  
Partial Least Squares Regression ◽  
Multivariate Calibration ◽  
Least Squares Regression ◽  
Hazardous Air Pollutants ◽  
Ir Spectrometry ◽  
Open Path ◽  
Ft Ir

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.

Calibration approaches for the measurement of aerosol multielemental concentration using spark emission spectroscopy

2018 ◽  
Vol 33 (3) ◽  
pp. 404-412 ◽  
Author(s):  
Lina Zheng ◽  
Pramod Kulkarni ◽  
Dionysios D. Dionysiou
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Emission Spectroscopy ◽  
Partial Least Squares Regression ◽  
Elemental Concentration ◽  
Multivariate Calibration ◽  
Least Squares Regression ◽  
Calibration Approach

A multivariate calibration approach, using partial least squares regression, has been developed for the measurement of aerosol elemental concentration.

Estimation of olive oil acidity using FT-IR and partial least squares regression

2009 ◽  
Vol 3 (3) ◽  
pp. 187-191 ◽  
Author(s):  
Alexandra Nunes ◽  
Joana Martins ◽  
António S. Barros ◽  
Andrea C. Galvis-Sánchez ◽  
Ivonne Delgadillo
Keyword(s):  
Olive Oil ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Least Squares Regression ◽  
Ft Ir

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.

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