scholarly journals Near infrared spectroscopic assessment of loosely and tightly bound cortical bone water

The Analyst ◽  
2020 ◽  
Vol 145 (10) ◽  
pp. 3713-3724
Author(s):  
Ramyasri Ailavajhala ◽  
William Querido ◽  
Chamith S. Rajapakse ◽  
Nancy Pleshko
Keyword(s):  
Cortical Bone ◽  
Bone Tissue ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Bone Water ◽  
Infrared Spectroscopic

NIR spectroscopy can differentiate water loosely bound to bone tissue, and tightly bound to either collagen or mineral.

scholarly journals Environmentally-Controlled Near Infrared Spectroscopic Imaging of Bone Water

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ramyasri Ailavajhala ◽  
Jack Oswald ◽  
Chamith S. Rajapakse ◽  
Nancy Pleshko
Keyword(s):  
Near Infrared ◽  
Spectroscopic Imaging ◽  
Bone Water ◽  
Infrared Spectroscopic ◽  
Infrared Spectroscopic Imaging

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.

Near-infrared spectroscopic identification and quantification of active pharmaceutical ingredients in closed capsules: a feasibility study for pediatric doses

2019 ◽  
Vol 11 (40) ◽  
pp. 5185-5194
Author(s):  
David L. Mainka ◽  
Andreas Link
Keyword(s):  
Feasibility Study ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Active Pharmaceutical Ingredients ◽  
Pharmaceutical Ingredients ◽  
Infrared Spectroscopic ◽  
Quality Checks ◽  
3D Printed ◽  
Spectroscopic Identification ◽  
Identification And Quantification

3D printed NIR spectroscopy sample holders were evaluated as means for improvement of quality checks of pharmacy compounded capsules.

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.

Feasibility study for rapid near-infrared spectroscopic identification of different gallbladder diseases by direct analysis of bile juice

The Analyst ◽  
2019 ◽  
Vol 144 (24) ◽  
pp. 7236-7241
Author(s):  
Eunjin Jang ◽  
Tung Duy Vu ◽  
Dongho Choi ◽  
Yun Kyung Jung ◽  
Kyeong Geun Lee ◽  
...  
Keyword(s):  
Feasibility Study ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Direct Analysis ◽  
Gallbladder Diseases ◽  
Infrared Spectroscopic ◽  
Spectroscopic Identification

A whole-sample-covering near-infrared (NIR) spectroscopy scheme has been adopted for the simple drop-and-dry measurement of raw bile juice for the identification of gallbladder (GB) diseases of stone, polyp, and cancer.

A Near-Infrared Spectroscopic Study of 60 Micron Peakers

1999 ◽  
Vol 118 (5) ◽  
pp. 2038-2054 ◽  
Author(s):  
Charlene A. Heisler ◽  
Michael M. De Robertis
Keyword(s):  
Spectroscopic Study ◽  
Near Infrared ◽  
Infrared Spectroscopic Study ◽  
Infrared Spectroscopic
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