Comparison of chemometric approaches for near-infrared spectroscopic data

2016 ◽  
Vol 8 (8) ◽  
pp. 1914-1923 ◽  
Author(s):  
Liming Yang ◽  
Qun Sun
Keyword(s):  
Spectroscopic Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Complex Samples ◽  
Infrared Spectroscopic

Near-infrared (NIR) spectroscopy technology has demonstrated great potential in the analysis of complex samples owing to its simplicity, rapidity and being nondestructive.

Characterization of fast pyrolysis bio-oil properties by near-infrared spectroscopic data

2018 ◽  
Vol 133 ◽  
pp. 9-15 ◽  
Author(s):  
Torbjörn A. Lestander ◽  
Linda Sandström ◽  
Henrik Wiinikka ◽  
Olov G.W. Öhrman ◽  
Mikael Thyrel
Keyword(s):  
Spectroscopic Data ◽  
Near Infrared ◽  
Fast Pyrolysis ◽  
Bio Oil ◽  
Infrared Spectroscopic

Open-source python module for automated preprocessing of near infrared spectroscopic data

2020 ◽  
Vol 1108 ◽  
pp. 1-9 ◽  
Author(s):  
Jari Torniainen ◽  
Isaac O. Afara ◽  
Mithilesh Prakash ◽  
Jaakko K. Sarin ◽  
Lauri Stenroth ◽  
...  
Keyword(s):  
Open Source ◽  
Spectroscopic Data ◽  
Near Infrared ◽  
Infrared Spectroscopic

scholarly journals Near infrared spectroscopic assessment of developing engineered tissues: correlations with compositional and mechanical properties

The Analyst ◽  
2017 ◽  
Vol 142 (8) ◽  
pp. 1320-1332 ◽  
Author(s):  
Arash Hanifi ◽  
Uday Palukuru ◽  
Cushla McGoverin ◽  
Michael Shockley ◽  
Eliot Frank ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Spectroscopic Data ◽  
Near Infrared ◽  
Engineered Tissues ◽  
Infrared Spectroscopic ◽  
Engineered Cartilage ◽  
Non Destructive

Non-destructive near infrared spectroscopic data can be utilized for assessment of compositional and mechanical properties of engineered cartilage.

Regression models based on new local strategies for near infrared spectroscopic data

2016 ◽  
Vol 933 ◽  
pp. 50-58 ◽  
Author(s):  
F. Allegrini ◽  
J.A. Fernández Pierna ◽  
W.D. Fragoso ◽  
A.C. Olivieri ◽  
V. Baeten ◽  
...  
Keyword(s):  
Spectroscopic Data ◽  
Regression Models ◽  
Near Infrared ◽  
Infrared Spectroscopic

scholarly journals Optimal Regression Method for Near-Infrared Spectroscopic Evaluation of Articular Cartilage

2017 ◽  
Vol 71 (10) ◽  
pp. 2253-2262 ◽  
Author(s):  
Mithilesh Prakash ◽  
Jaakko K. Sarin ◽  
Lassi Rieppo ◽  
Isaac O. Afara ◽  
Juha Töyräs
Keyword(s):  
Articular Cartilage ◽  
Variable Selection ◽  
Least Squares ◽  
Multivariate Regression ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Regression Technique ◽  
Selection Methods ◽  
Tissue Properties ◽  
Infrared Spectroscopic

Near-infrared (NIR) spectroscopy has been successful in nondestructive assessment of biological tissue properties, such as stiffness of articular cartilage, and is proposed to be used in clinical arthroscopies. Near-infrared spectroscopic data include absorbance values from a broad wavelength region resulting in a large number of contributing factors. This broad spectrum includes information from potentially noisy variables, which may contribute to errors during regression analysis. We hypothesized that partial least squares regression (PLSR) is an optimal multivariate regression technique and requires application of variable selection methods to further improve the performance of NIR spectroscopy-based prediction of cartilage tissue properties, including instantaneous, equilibrium, and dynamic moduli and cartilage thickness. To test this hypothesis, we conducted for the first time a comparative analysis of multivariate regression techniques, which included principal component regression (PCR), PLSR, ridge regression, least absolute shrinkage and selection operator (Lasso), and least squares version of support vector machines (LS-SVM) on NIR spectral data of equine articular cartilage. Additionally, we evaluated the effect of variable selection methods, including Monte Carlo uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA), backward interval PLS (BiPLS), genetic algorithm (GA), and jackknife, on the performance of the optimal regression technique. The PLSR technique was found as an optimal regression tool (R2Tissue thickness = 75.6%, R2Dynamic modulus = 64.9%) for cartilage NIR data; variable selection methods simplified the prediction models enabling the use of lesser number of regression components. However, the improvements in model performance with variable selection methods were found to be statistically insignificant. Thus, the PLSR technique is recommended as the regression tool for multivariate analysis for prediction of articular cartilage properties from its NIR spectra.

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